code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def a_ ( lowerCAmelCase_ : int = 10 ):
if not isinstance(A__, A__ ) or n < 0:
raise ValueError('Invalid input' )
__lowerCAmelCase = 10**n
__lowerCAmelCase = 2_8433 * (pow(2, 783_0457, A__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doc... | 284 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
UpperCAmelC... | 12 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main... | 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfi... | 12 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__A = 4
__A = 3
class UpperCAmelCase (__lowerCamelCase ):
"""si... | 177 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.... | 12 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class A ( __lowerCamelCase ):
'''simple docstring'''
A = 'bert-generation'
def __init__(self , _UpperCAmelCase=5_0_3_5_8 , _UpperCAmelCase=1_0_2_4 , _UpperCAmelCas... | 298 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impo... | 12 | 0 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensor... | 309 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accel... | 12 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__lowerCAmelCase : int = logging.get_logger(__name__)
class UpperCAmelCase_ :
''... | 88 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bir... | 12 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if density <= 0:
raise ValueError('''Impossible fluid density''')
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''')
return (bulk_modulus / density) ** 0.5
if __na... | 211 |
def lowerCamelCase__ ( A__ : list ):
'''simple docstring'''
__lowerCamelCase = len(A__ )
for _ in range(A__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__lowe... | 12 | 0 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__A ='\\n@misc{chen2021evaluating,\n title={Evaluat... | 163 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase__:
def __init__( self: Any , ... | 12 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTo... | 98 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCAmelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
datarow=DataRow... | 12 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ :... | 143 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...util... | 12 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
fr... | 161 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Dict =... | 12 | 0 |
_snake_case : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def a_ ( ):
__lowerCAmelCase = input('Enter message: ' )
__lowerCAmelCase = input('Enter key [alphanumeric]: ' )
__lowerCAmelCase = input('Encrypt/Decrypt [e/d]: ' )
if mode.lower().startswith('e' ):
__lo... | 284 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 12 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : str = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCH... | 13 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUM... | 12 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
lowercase__, lowercase__: Optional[int] = len(A__ ), len(grid[0] )
if (
min(A__ , A__ ) < 0
or ... | 177 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase_ ... | 12 | 0 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
... | 298 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : list[int] , A__ : list[int] , A__ : list[int] , A__ : list[list[str]] , A__ : int , ):
'''simple docstring'''
__lowerCamelCase = len(A__ ... | 12 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a_ (__lowerCamelCase ):
def __init__( self , *snake_case_ , **snake_case_ ... | 309 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCamelCase__:
UpperCAmelCase__ : int
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | None = None
UpperCAme... | 12 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class ... | 88 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, vali... | 12 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/umt5-small": "https://huggingface.co/google... | 211 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : list[int | float] , A__ : int , A__ : int ):
'''simple docstring'''
if len(A__ ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (... | 12 | 0 |
'''simple docstring'''
import math
import os
import sys
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Optional[Any] = """"""
try:
with open(A__ , """rb""" ) as binary_file:
UpperCAmelCase__ : Option... | 163 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torc... | 12 | 0 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a_ ( lowerCamelCase , lowerCamelCase=False ):
UpperCAmelCase__ = OmegaConf.load(A__ )
if display:
print(ya... | 98 |
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase_ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0... | 12 | 0 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...tes... | 13 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvaila... | 13 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availa... | 13 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 13 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
fro... | 13 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCase : List[Any] = loggi... | 13 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase : Union[str, Any] = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attenti... | 13 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : Union[str, Any] = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_M... | 13 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ... | 13 | 1 |
from __future__ import annotations
lowerCAmelCase : List[Any] = """Muhammad Umer Farooq"""
lowerCAmelCase : Tuple = """MIT"""
lowerCAmelCase : List[str] = """1.0.0"""
lowerCAmelCase : Any = """Muhammad Umer Farooq"""
lowerCAmelCase : Optional[Any] ... | 13 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Tuple = ... | 13 | 1 |
import math
import sys
def A_ ( _UpperCAmelCase ):
if number != int(_UpperCAmelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number... | 13 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvaila... | 13 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 13 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteSch... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("Al... | 13 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTeste... | 13 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTra... | 13 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
fro... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isi... | 13 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 13 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 * 2**20] )
def A_ ( _UpperCA... | 13 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : str ... | 13 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import ca... | 13 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 |
import math
import sys
def A_ ( _UpperCAmelCase ):
if number != int(_UpperCAmelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number... | 13 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, s... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
rais... | 13 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {"""vocab_file""... | 13 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
... | 13 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 13 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [0] * len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = []
SCREAMING_SNAKE_CASE_: str = []
SCREAMING_SNAKE_CASE_: List[str] = 0
for values in graph.values():
... | 13 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.tes... | 13 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedTy... | 13 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 13 |
from collections.abc import Callable
class __lowercase :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Callable | None = None):
# Stores actual heap items.
SCREAMING_SNAKE_CASE_: list = []
# Stores indexes of each i... | 13 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("Al... | 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 13 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : int = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_av... | 13 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow... | 13 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_res... | 13 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvaila... | 13 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_conf... | 13 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Dict , *lowerCAmelCase__ : ... | 13 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
fro... | 13 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCAmelCase : Union[str, Any] = HUGGINGFACE_HUB_CACHE
lowerCAmelCase : int = """config.json"""
lowerCAmelCase : str = """diffusion_pytorch_model.bin"""
lowerCAmelCase : Dict... | 13 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase : Union[str, Any] = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attenti... | 13 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase : Any = False
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
... | 13 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ... | 13 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAI... | 13 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Tuple = ... | 13 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 13 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 1 |
from __future__ import annotations
from math import pi
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
... | 13 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 13 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("Al... | 13 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Any = '''timm_backbone'''
def __init... | 13 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTra... | 13 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Tuple = ... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isi... | 13 | 1 |
from __future__ import annotations
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ): # noqa: E741
while r - l > 1:
SCREAMING_SNAKE_CASE_: Tuple = (l + r) // 2
if v[m] >= key:
SCREAMING_SNAKE_CASE_: ... | 13 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
... | 13 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : str ... | 13 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
fr... | 13 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def A_ ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def A_ ( _UpperCAmelCase ):
return word_by_signature[signature(_UpperCAmelCase )]
lowerCA... | 13 |
import math
import sys
def A_ ( _UpperCAmelCase ):
if number != int(_UpperCAmelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number... | 13 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : int = ['''speech''']
def __init__( self : Optional[int] , *lowerCAmelCase__ : int , **lowerCAmelCase_... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
rais... | 13 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 13 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
... | 13 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 13 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [0] * len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = []
SCREAMING_SNAKE_CASE_: str = []
SCREAMING_SNAKE_CASE_: List[str] = 0
for values in graph.values():
... | 13 | 1 |
lowerCAmelCase : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowerCAmelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, ... | 13 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedTy... | 13 | 1 |
def A_ ( _UpperCAmelCase ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 |
from collections.abc import Callable
class __lowercase :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Callable | None = None):
# Stores actual heap items.
SCREAMING_SNAKE_CASE_: list = []
# Stores indexes of each i... | 13 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_se... | 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 13 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __lowercase ( nn.Module ):
"""simple docstring"""
_UpperCAmelCase : int
_UpperCAmelCase : i... | 13 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow... | 13 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase ... | 13 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvaila... | 13 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = ""
for i in table:
res += inp[i - 1]
return res
def A_ ( _UpperCAmelCase ):
return data[1:] + data[0]
def A_ ( _UpperCAmelCase , _UpperCAme... | 13 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 | 1 |
from __future__ import annotations
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!" )
... | 13 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
fro... | 13 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def A_ ( ):
print("Making key files..." )
make_key_files("rsa" , 10_24 )
print("Key files generation successful." )
... | 13 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase : Union[str, Any] = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attenti... | 13 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew... | 13 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ... | 13 | 1 |
import math
lowerCAmelCase : Dict = 10
lowerCAmelCase : Dict = 7
lowerCAmelCase : Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def A_ ( _UpperCAmelCase = 20 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = math.comb(_UpperCAmelCase , ... | 13 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Tuple = ... | 13 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from di... | 13 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 13 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 13 | 1 |
import math
import unittest
def A_ ( _UpperCAmelCase ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif num... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("Al... | 13 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTra... | 13 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase__ : flo... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isi... | 13 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase : Optional[Any] = """src/diffusers"""
# Matches is_xxx_available()
lowerCAmelCase : str = re.com... | 13 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 1 |
from math import ceil
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Dict = list(range(0 , _UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: str = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate c... | 13 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : str ... | 13 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase : Union[str, Any] = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attenti... | 13 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 13 |
import math
import sys
def A_ ( _UpperCAmelCase ):
if number != int(_UpperCAmelCase ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number... | 13 | 1 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet impor... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
rais... | 13 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCAmelCase : str = logging.get_logger(__name_... | 13 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
... | 13 | 1 |
import math
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = f"Input value of [number={number}] must be an integer"
raise TypeError(_UpperCAmelCase )
if number < 1:
S... | 13 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [0] * len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = []
SCREAMING_SNAKE_CASE_: str = []
SCREAMING_SNAKE_CASE_: List[str] = 0
for values in graph.values():
... | 13 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_... | 13 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedTy... | 13 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : str ... | 13 |
from collections.abc import Callable
class __lowercase :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Callable | None = None):
# Stores actual heap items.
SCREAMING_SNAKE_CASE_: list = []
# Stores indexes of each i... | 13 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : List[str]
... | 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 13 | 1 |
from math import sqrt
def A_ ( _UpperCAmelCase ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
SCREAMING_SNAKE_CASE_: Optional[int] = True
# 0 and 1 are none primes.
if ... | 13 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow... | 13 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowercase ( unittest.TestCase ):
"""si... | 13 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvaila... | 13 | 1 |
import numpy as np
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
return np.where(vector > 0 , _UpperCAmelCase , (alpha * (np.exp(_UpperCAmelCase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 |
class __lowercase :
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[Any]):
SCREAMING_SNAKE_CASE_: List[str] = name
SCREAMING_SNAKE_CASE_: Union[str, Any] = val
... | 13 | 1 |
lowerCAmelCase : Any = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def A_ ( ):
SCREAMING_SNAKE_CASE_: int = input("Enter message: " )
SCREAMING_SNAKE_CASE_: Optional[int] = input("Enter key [alphanumeric]: " )
SCREAMING_SNAKE_CASE_: List[Any] = ... | 13 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
fro... | 13 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase : Op... | 13 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase : Union[str, Any] = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attenti... | 13 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
rais... | 13 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ... | 13 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
... | 13 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Tuple = ... | 13 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCAmelCase : str = l... | 13 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 13 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("Al... | 13 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __lowercase ( datasets.BuilderConfig ):
"""simple docstring"""
_UpperCAmelCase : Optional[da... | 13 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTra... | 13 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple d... | 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isi... | 13 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase : Any = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
i... | 13 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 1 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : int=None , lowerCAmelCase__ : Optional[int]=None , lowerCAm... | 13 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : str ... | 13 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
# Load checkpoint
... | 13 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 | 1 |
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