code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
A__ : Union[str, Any] = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
c... | 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , ... | 660 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_form... | 717 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase... | 660 | 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 tensorflow as tf
fro... | 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__... | 660 | 0 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase... | 719 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_co... | 660 | 0 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j... | 720 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.... | 660 | 0 |
"""simple docstring"""
import os
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 logging
A__ : List[str] = logging.get_logger(__name__)
A__ :... | 721 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTok... | 660 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy... | 700 |
"""simple docstring"""
from timeit import timeit
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase__ : Tuple = 0
while number:
numbe... | 660 | 0 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = (DDIMParallelScheduler,)
_A = (('eta', 0.0), ('... | 701 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase , lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Dict:
Uppe... | 660 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tran... | 702 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both ... | 660 | 0 |
def a__ ( lowerCAmelCase : int = 200 ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
UpperCAmelCase__ : List[str] = [0] * (pence + 1)
UpperCAmelCase__ : Optional[Any] = 1 # base ca... | 703 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mul... | 660 | 0 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision... | 704 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availab... | 660 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def a__ ( lowerCAmelCase : str , lowerCAmelCase : float | Decimal , lowerCAmelCase : float = 10**-10 ):
'''sim... | 705 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .schedulin... | 660 | 0 |
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class _lowercase ( lowerCAmelCase_ ):
'''simple d... | 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ... | 660 | 0 |
from __future__ import annotations
from typing import Any
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> None:
UpperCAmelCase__ : Any = num_of_nodes
UpperCAmelCase__ : list[list[int]] = ... | 707 |
"""simple docstring"""
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 (
... | 660 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase : str ):
'''simple docstring'''
if n_term == "":
return []
UpperCAmelCase__ : list = []
for temp in range(int(lowerCAmelCase ) ):
series.append(F"1/{temp + 1}" if series else "1" )
... | 708 |
"""simple docstring"""
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
raise TypeError("Input value must be a 'int' ... | 660 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A__ : List[str] = datasets.utils.logging.get_logger(__name__)
@dataclass
cl... | 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availab... | 660 | 0 |
"""simple docstring"""
from maths.prime_check import is_prime
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Union[str, Any] = F"Input value... | 710 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A__ : List[str] = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A__ : Any = _LazyMod... | 660 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 711 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstrin... | 660 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : An... | 660 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
de... | 713 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A__ : Optional[int] = ["""small""", """medium""", """large"""]
A__ : Optional[int] = """lm_head.decoder.weight"""
A__ : Dict = """lm_head.weight"""
de... | 660 | 0 |
"""simple docstring"""
from string import ascii_uppercase
A__ : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
A__ : Optional[Any] = dict(enumerate(ascii_uppercase))
def a__ ( lowerCAmelCase : str , lowerCAmelCase : str ):
... | 714 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j... | 660 | 0 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
@pytest.... | 715 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerB... | 660 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( lowerCAmelCase : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = {}
UpperCAmelCase__ : Optional[An... | 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , ... | 660 | 0 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__... | 717 |
"""simple docstring"""
from manim import *
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
def lowerCAmelCase__ ( self )-> Tuple:
UpperCAmelCase__ : str = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase... | 660 | 0 |
"""simple docstring"""
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> None:
UpperCAmelCase__ : Any = len(__UpperCamelCase )
UpperCAmelCase__ : str = [0] * len_array
if len_ar... | 718 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__... | 660 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
A__ : Optional[Any] = datasets.utils.logging.get_logger(__name__)
class _lowercase ( folder_based_builder.Fo... | 719 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_co... | 660 | 0 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTe... | 720 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.... | 660 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class _lowercase :
'''simple docstring'''
def __init__( self , __UpperCamelCase )-> None:
UpperCAmelCase__ : Union[str, Any] = size
# approximate the overall size ... | 721 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTok... | 660 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def A__ ( lowercase: Namespace ) -> Optional[Any]:
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args... | 661 | _lowercase : Dict ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import ... | 661 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pip... | 661 | from typing import List
from .keymap import KEYMAP, get_character
def A__ ( lowercase: str ) -> List[str]:
def decorator(lowercase: int ):
A : Tuple =getattr(lowercase, 'handle_key', [] )
handle += [key]
setat... | 661 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def A__ ( lowercase: List[Any] ) -> str:
if "model" in orig_key:
A : int =orig_key.replace('model.', '' )
if "norm1" in orig_key:
A : ... | 661 | import math
def A__ ( lowercase: int ) -> list:
A : Optional[Any] =[True] * n
A : Tuple =False
A : List[Any] =False
A : Dict =True
for i in range(3, int(n**0.5 + 1 ), 2 ):
... | 661 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : Any =argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''-... | 661 | import heapq
def A__ ( lowercase: dict ) -> set[int]:
A : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min... | 661 | 1 |
def A__ ( lowercase: str, lowercase: str ) -> int:
if len(lowercase ) != len(lowercase ):
raise ValueError('String lengths must match!' )
A : List[Any] =0
for chara, chara in zip(lowercase, lowercase ):
if ch... | 661 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase : List[Any] =logging.get_logger(__na... | 661 | 1 |
def A__ ( lowercase: list[int], lowercase: list[int] ) -> tuple[float, float]:
# Check if the input is valid
if not len(lowercase ) == len(lowercase ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equ... | 661 | from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorTyp... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase : List[str] ={
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_M... | 661 | from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCRE... | 661 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blender... | 661 | import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test... | 661 | 1 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowercase : List[Any] =TypeVar('''T''')
class SCREAMING_SNAKE_CASE_ ( Generic[T] ):
'''simple docstring'''
lowercase : deque[T] # Cache store of k... | 661 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int =logging.get_logger(__name__)
_lowercase : Dict ={
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See ... | 661 | 1 |
from __future__ import annotations
def A__ ( lowercase: int | str ) -> bool:
A : List[str] =str(lowercase )
return n == n[::-1]
def A__ ( lowercase: int = 1_000_000 ) -> Dict:
A : Dict =0
for i in range(1... | 661 | # Copyright 2021 The HuggingFace 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 a... | 661 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Tuple ={
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Informe... | 661 | import collections
import importlib.util
import os
import re
from pathlib import Path
_lowercase : List[str] ='''src/transformers'''
# Matches is_xxx_available()
_lowercase : Dict =re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_str... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase : int ={
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 661 | import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase : Any =logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCa... | 661 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
... | 661 | import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils imp... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Optional[int] ={
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerCon... | 661 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available()... | 661 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available()... | 661 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 661 | 1 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host>... | 661 | 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 im... | 661 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A__ ( lowercase: int = 2_000_000 ) -> int:
A : list[int] =[0]
A : int
for idx in range(1, ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.... | 661 | def A__ ( lowercase: int ) -> int:
if not isinstance(lowercase, lowercase ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
A : Any =0
while number:
# This way we arrive at next set bit (next 1) ins... | 661 | 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_distilbert import DistilBertTokenizer
_lowercase : Tuple =logging.get_logger(__name_... | 661 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A__ ( *lowercase: Tuple, lowercase: Optional[Union[Dict, Any]] = None, lowercase: Dict=True, lowercase: Any=2 ) -> List[Any]:
from .. import __version__
... | 661 | 1 |
from statistics import mean
import numpy as np
def A__ ( lowercase: list, lowercase: list, lowercase: list, lowercase: int ) -> list:
A : int =0
# Number of processes finished
A : Optional[int] =0
# Displays the fi... | 661 | import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A__ ( lowercase: ... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : str ={
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokeniz... | 661 | import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAG... | 661 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_... | 661 | import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
... | 661 | 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
_lowercase : Dict =logging.get_logger(__name__)
_lowercase : ... | 661 | import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_lowercase : Optional[Any] =WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def A__ ( lowe... | 661 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, 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
... | 661 | _lowercase : Dict ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import ... | 661 | 1 |
def A__ ( lowercase: list, lowercase: list, lowercase: int, lowercase: int, lowercase: int ) -> int:
if index == number_of_items:
return 0
A : Any =0
A : Optional[Any] =0
A : Dict =knapsa... | 661 | from typing import List
from .keymap import KEYMAP, get_character
def A__ ( lowercase: str ) -> List[str]:
def decorator(lowercase: int ):
A : Tuple =getattr(lowercase, 'handle_key', [] )
handle += [key]
setat... | 661 | 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... | 661 | import math
def A__ ( lowercase: int ) -> list:
A : Optional[Any] =[True] * n
A : Tuple =False
A : List[Any] =False
A : Dict =True
for i in range(3, int(n**0.5 + 1 ), 2 ):
... | 661 | 1 |
def A__ ( lowercase: int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661 | import heapq
def A__ ( lowercase: dict ) -> set[int]:
A : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min... | 661 | 1 |
from collections.abc import Sequence
def A__ ( lowercase: Sequence[float], lowercase: float ) -> float:
return sum(c * (x**i) for i, c in enumerate(lowercase ) )
def A__ ( lowercase: Sequence[float], lowercase: float ) -> float:
A : ... | 661 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase : List[Any] =logging.get_logger(__na... | 661 | 1 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_lowercase : List[Any] =(
'''This metric will be removed from the... | 661 | from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorTyp... | 661 | 1 |
from __future__ import annotations
from collections import namedtuple
def A__ ( lowercase: float, lowercase: float, lowercase: float ) -> tuple:
A : List[Any] =namedtuple('result', 'name value' )
if (voltage, current, power).count(0 ) != 1:
... | 661 | from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCRE... | 661 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def A__ ( ) -> Optional[int]:
raise RuntimeError('CUDA out of memory.' )
class SCREAMING_SNAKE_CASE_ (... | 661 | import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test... | 661 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_ ):
'''simple docstring''... | 661 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int =logging.get_logger(__name__)
_lowercase : Dict ={
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See ... | 661 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int =logging.get_logger(__name__)
_lowercase : str ={
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/... | 661 | # Copyright 2021 The HuggingFace 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 a... | 661 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A__ ( lowercase: int ) ->... | 661 | import collections
import importlib.util
import os
import re
from pathlib import Path
_lowercase : List[str] ='''src/transformers'''
# Matches is_xxx_available()
_lowercase : Dict =re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_str... | 661 | 1 |
from __future__ import annotations
import numpy as np
def A__ ( lowercase: list[float] ) -> Tuple:
return np.maximum(0, lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 661 | import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase : Any =logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCa... | 661 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
_lowercase : str =tuple[int, int]
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : set[int] , SCREAMING_SN... | 661 | import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils imp... | 661 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_lowercase : Dict... | 661 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available()... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Any ={
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
... | 661 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 661 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
_lowercase : List[str] ='''src/transformers'''
# Matches is_xxx_available()
_lowercase : Dict =re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_str... | 661 | 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 im... | 661 | 1 |
from __future__ import annotations
import math
def A__ ( lowercase: int, lowercase: int, lowercase: bool, lowercase: list[int], lowercase: float ) -> int:
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(lowercase ... | 661 | def A__ ( lowercase: int ) -> int:
if not isinstance(lowercase, lowercase ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
A : Any =0
while number:
# This way we arrive at next set bit (next 1) ins... | 661 | 1 |
import torch
from torch import nn
class SCREAMING_SNAKE_CASE_ ( nn.Module ):
'''simple docstring'''
def __init__( self : str , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCRE... | 661 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A__ ( *lowercase: Tuple, lowercase: Optional[Union[Dict, Any]] = None, lowercase: Dict=True, lowercase: Any=2 ) -> List[Any]:
from .. import __version__
... | 661 | 1 |
import math
def A__ ( lowercase: int ) -> str:
A : Optional[Any] =0
A : str =0
while num > 0:
A : Dict =num % 8
A : Union[str, Any] =octal + (remainder * math.floor(math.pow(10... | 661 | import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A__ ( lowercase: ... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_lowercase : Optional[int] ={
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConf... | 661 | import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAG... | 661 | 1 |
import math
def A__ ( lowercase: int ) -> bool:
assert isinstance(lowercase, lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < ... | 661 | import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Any ={
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
rais... | 661 | import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_lowercase : Optional[Any] =WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def A__ ( lowe... | 661 | 1 |
def A__ ( lowercase: float, lowercase: float ) -> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if ... | 661 | _lowercase : Dict ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import ... | 661 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 661 | from typing import List
from .keymap import KEYMAP, get_character
def A__ ( lowercase: str ) -> List[str]:
def decorator(lowercase: int ):
A : Tuple =getattr(lowercase, 'handle_key', [] )
handle += [key]
setat... | 661 | 1 |
_lowercase : List[Any] ={
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
... | 661 | import math
def A__ ( lowercase: int ) -> list:
A : Optional[Any] =[True] * n
A : Tuple =False
A : List[Any] =False
A : Dict =True
for i in range(3, int(n**0.5 + 1 ), 2 ):
... | 661 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int =logging.get_logger(__name__)
_lowercase : Dict ={
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See ... | 661 | import heapq
def A__ ( lowercase: dict ) -> set[int]:
A : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min... | 661 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A__ ( lowercase: dict ) -> tuple:
return (data["... | 661 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase : List[Any] =logging.get_logger(__na... | 661 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.h... | 661 | from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorTyp... | 661 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A__ ( lowercase: Tuple ) -> Any: # picklable for multiprocessing
... | 661 | from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCRE... | 661 | 1 |
import logging
import os
from .state import PartialState
class SCREAMING_SNAKE_CASE_ ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE_ ( SCREAMING_SNAKE_CASE__ : List[str] ) -> Optional[int]:
A : Optional[in... | 661 | import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test... | 661 | 1 |
from typing import Any
def A__ ( lowercase: list ) -> list[Any]:
if not input_list:
return []
A : Optional[int] =[input_list.count(lowercase ) for value in input_list]
A : Tuple =max(lowercase ) # Gets the maximum co... | 661 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int =logging.get_logger(__name__)
_lowercase : Dict ={
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See ... | 661 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, E... | 661 | # Copyright 2021 The HuggingFace 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 a... | 661 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokeniz... | 661 | import collections
import importlib.util
import os
import re
from pathlib import Path
_lowercase : List[str] ='''src/transformers'''
# Matches is_xxx_available()
_lowercase : Dict =re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_str... | 661 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 661 | import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase : Any =logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCa... | 661 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, ... | 661 | import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils imp... | 661 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 661 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available()... | 661 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenizati... | 661 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 661 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_... | 661 | 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 im... | 661 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowercase : Dict =logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any... | 661 | def A__ ( lowercase: int ) -> int:
if not isinstance(lowercase, lowercase ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
A : Any =0
while number:
# This way we arrive at next set bit (next 1) ins... | 661 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[str] =logging.get_logger(__name__)
_lowercase : int ={
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',... | 661 | import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A__ ( *lowercase: Tuple, lowercase: Optional[Union[Dict, Any]] = None, lowercase: Dict=True, lowercase: Any=2 ) -> List[Any]:
from .. import __version__
... | 661 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowercase: Any, lowercase: Dict, lowercase: List[Any], lowercase: Li... | 661 | import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A__ ( lowercase: ... | 661 | 1 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_lowercase : List[str] =logging.get_logger(__name__)... | 661 | import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAG... | 661 | 1 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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_GUID... | 661 | import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
... | 661 | 1 |
import functools
from typing import Any
def A__ ( lowercase: str, lowercase: list[str] ) -> bool:
# Validation
if not isinstance(lowercase, lowercase ) or len(lowercase ) == 0:
raise ValueError('the string should be not empty string' )
... | 661 | import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_lowercase : Optional[Any] =WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def A__ ( lowe... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowercase : str ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 661 | _lowercase : Dict ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import ... | 661 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_lowercase : Any ='''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
Dorr, Bon... | 661 | from typing import List
from .keymap import KEYMAP, get_character
def A__ ( lowercase: str ) -> List[str]:
def decorator(lowercase: int ):
A : Tuple =getattr(lowercase, 'handle_key', [] )
handle += [key]
setat... | 661 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY... | 661 | import math
def A__ ( lowercase: int ) -> list:
A : Optional[Any] =[True] * n
A : Tuple =False
A : List[Any] =False
A : Dict =True
for i in range(3, int(n**0.5 + 1 ), 2 ):
... | 661 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Dict ={
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
... | 661 | import heapq
def A__ ( lowercase: dict ) -> set[int]:
A : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min... | 661 | 1 |
def A__ ( ) -> list[list[int]]:
return [list(range(1_000 - i, -1_000 - i, -1 ) ) for i in range(1_000 )]
_lowercase : str =generate_large_matrix()
_lowercase : Optional[int] =(
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -... | 661 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase : List[Any] =logging.get_logger(__na... | 661 | 1 |
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 im... | 661 | from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorTyp... | 661 | 1 |
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