code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : List[Any] =... | 115 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : int ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception("""Principal borrowed must be... | 115 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_... | 350 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake... | 36 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase : Union[str, Any] = "\\n\n"
_lowerCamelCase : List[str] ... | 28 | 0 |
'''simple docstring'''
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_... | 358 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.co... | 227 | 0 |
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_available
from ...test_configu... | 103 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
""... | 97 | 0 |
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 20 | import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_... | 20 | 1 |
import random
class UpperCAmelCase :
'''simple docstring'''
@staticmethod
def __magic_name__ ( lowerCAmelCase_ : str ):
"""simple docstring"""
_A: Union[str, Any] = [ord(__lowercase ) for i in text]
_A: str = []
_A: Optio... | 121 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : Tuple = [1]
for i in range(2 , __lowerCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of b... | 114 | 0 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_avail... | 241 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
lowerCamelCase = TypeVar("""_T""")
class lowercase__ ( Generic[_T] ):
'''simple docstring'''
def __init__( self : int , _Up... | 241 | 1 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = [1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0, 0, 0
__UpperCamelCase = ugly_nums[ia] * 2
__UpperCamelCase = ugly_nums[ia] * 3
__UpperCamelCase ... | 316 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( __SCREAMING_SNAKE_... | 316 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : ... | 298 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTest... | 298 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
UpperCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code... | 12 |
import os
from distutils.util import strtobool
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) )... | 296 | 0 |
import unittest
from knapsack import knapsack as k
class UpperCamelCase ( unittest.TestCase ):
def __A ( self ):
A__ = 0
A__ = [0]
A__ = [0]
A__ = len(UpperCAmelCase__ )
self.assertEqual(k... | 356 |
from __future__ import annotations
from random import random
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ = None ):
A__ = value
A__ = random()
A__ = None
A__ = None
def __repr__( self )... | 198 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : Dict, __snake_case : List[Any], __snake_case : Union[str, Any], __snake_case : Tuple, __snake_case : List[str], __snake_case : List[str] ) -> List[Any]:
"""simple docstring"""... | 134 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def __lowerCamelCase ( ) -> int:
"""simple docstring"""
A__ : int =9
A__ : int =[
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7,... | 134 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 1 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = "EncodecFeatureExtractor... | 116 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:str = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_tr... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class A :
def __init__( self , SCREAMING_SNAKE_CASE = None ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = ... | 311 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase : Any = 'src/transform... | 311 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCas... | 47 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_UpperCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_f... | 254 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_util... | 249 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Tuple = {
"configuration_convnext": ["CONVNEXT_PRE... | 249 | 1 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for test... | 98 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_t... | 350 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 0 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of ini... | 335 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( Uppe... | 335 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 271 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, At... | 271 | 1 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_... | 160 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
fr... | 160 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREAMING_SNAKE_CASE_:List[Any] ... | 360 |
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
... | 115 | 0 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerat... | 109 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string shou... | 96 | 0 |
'''simple docstring'''
def _lowercase ( __A = "The quick brown fox jumps over the lazy dog" ,):
'''simple docstring'''
__UpperCamelCase = set()
# Replace all the whitespace in our sentence
__UpperCamelCase = input_str.replace(""" """ ,"""""" )
f... | 243 |
'''simple docstring'''
def _lowercase ( __A ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 243 | 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_bert import BertTokenizer
a__: Optional[int] = logging.get_logger(__name__)
a_... | 193 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__( UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamel... | 193 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 360 |
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple=None):
_A : Any = data
_A : Optional[Any] =... | 227 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 257 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any =logging.get_logger(__name__)
lowerCAmelCase__ : str ={
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingfac... | 257 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__lowerCamelCase : List[str] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__lowerCamelCase : Dict = 1
... | 113 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a ="""src/transformers"""
a ="""docs/source/en"""
... | 113 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __magic_name__ ( __a : bytes , __a : int ):
'''simple docstring'''
UpperCamelCase__ = f"{sampling_rate}"
UpperCamelCase__ = ""... | 244 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTeste... | 244 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'''nielsr/canine-s''': 20_48,
}
# Unicode d... | 62 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-pa... | 62 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=a_ ):
"""simple docstring"""
lowercase__ = ["speech"]
def __init__( self : Tuple ,*lowercase_ : Tuple ,**lowercase_ ... | 106 |
"""simple docstring"""
__A : Any = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''':... | 33 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 182 |
"""simple docstring"""
lowerCamelCase__ = """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 imp... | 182 | 1 |
def a ( A__ : int ) -> bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 205 |
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 accelerat... | 205 | 1 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not i... | 328 | 0 |
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 ModelTeste... | 128 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ... | 128 | 1 |
def a__ ( UpperCAmelCase : int ):
UpperCAmelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( UpperCAmelCase : int = 5_000 ):
UpperCAmelCase : int = [(i * (3 * i - 1)) // 2 for i in range(1 , UpperCAm... | 350 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 99 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor... | 164 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfi... | 164 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase (__UpperCam... | 85 | """simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =FileLock(str(tmpdir / '''foo.lock''' ) )
__UpperCamelCase =F... | 85 | 1 |
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_available, loggi... | 24 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Tens... | 53 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
UpperCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase__ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase__ ... | 30 | """simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blo... | 30 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the r... | 209 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
... | 209 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_A = "\nimport os\n"
_A = "\ndef foo():\n import os\n return False\n"
_A = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
_A = ... | 371 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-c... | 137 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class A__ :
"""simple docstring"""
def __init__( self , lowercase = None) -> Dict:
'''simple docstring'''
if components... | 99 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase : Union[str, Any] = TypeVar("T")
UpperCAmelCase : Dict = Union[List[T], Tuple[T, ...]]
UpperCAmelCase : int = Union[T, List[T], Dict[str, T]]
UpperCAmelCase : ... | 136 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration... | 55 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : list[float] , lowerCamelCase : list[float] ):
lowerCAmelCase = sorted(numsa + numsa )
lowerCAmelCase , lowerCAmelCase = divmod(len(lowerCamelCas... | 55 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenize... | 58 |
'''simple docstring'''
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,
)
lowercase_ = {"""configuration_mbart""": ["... | 58 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
... | 100 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mo... | 100 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCamelCase ( UpperCamelCase__ ):
def __init__( self :Optional[Any] , lowerCamelCase :Optional[Any] , lowerCamelCase :Optional[Any] ) -> Dict:
Upp... | 169 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 95 | 0 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowerCAmelCase_( *lowercase_ : Tuple , lowercase_ : Tuple = None , lowercase_ : Tuple=True , lowercase_ : List[... | 366 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_f... | 73 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] ... | 21 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 21 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
class lowerCAmelCase ( __UpperCamelCase ):
def __init__( self : Optional[int] , UpperCAmelCase : str=None , **Up... | 45 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : int = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
""... | 45 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class _snake_case :
lowerCAmelCase_ : torch.Tensor # [batch_size x 3]
lowerCAmelCase_ : torch.Tensor # [batch_size x 3]
lowerCAmelCase_ : to... | 85 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE : int = {
... | 85 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ... | 352 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
... | 336 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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, ... | 334 |
'''simple docstring'''
def UpperCAmelCase ( a_ = 1_0_0 ) -> int:
"""simple docstring"""
A_ : Dict = n * (n + 1) * (2 * n + 1) / 6
A_ : Optional[int] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __n... | 344 | 0 |
"""simple docstring"""
import numpy as np
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Dict )... | 289 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase__ : Optional[int] = {
"""configuration_... | 289 | 1 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specifi... | 223 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slo... | 223 | 1 |
"""simple docstring"""
def _A ( _a : List[Any] , _a : List[str] ):
"""simple docstring"""
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError(... | 363 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCamelCase__ ( SC... | 77 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAP... | 85 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, R... | 59 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
... | 359 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils i... | 303 | 0 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |'''... | 101 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_n... | 219 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 364 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __magic_name__ :
lowerCAmelCase : str = field(
... | 107 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 327 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = {
... | 69 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def _snake_case ( UpperCAmelCase_ : int="ro" , UpperCAmelCase_ : Optional[int]="en" , UpperCAmelCase_ : List[Any]="wmt16" , UpperCAmelCase_ : str=None ):
... | 69 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that nex... | 50 |
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
UpperCAmelCase : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class __low... | 252 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowercase_ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowercase_ = typing.Union[np.floataa, int, float] # noqa: ... | 354 |
"""simple docstring"""
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowercase_ ... | 11 | 0 |
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,
AutoModelForQue... | 184 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization... | 34 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 370 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProc... | 26 | 0 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLogg... | 165 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCamelCase (nn.Module ):
lowerCamelCase__ : int
lowerCame... | 165 | 1 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def __SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
p... | 204 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils imp... | 204 | 1 |
from __future__ import annotations
def UpperCAmelCase__ ( lowerCamelCase ):
if not nums:
return 0
lowercase :Union[str, Any] = nums[0]
lowercase :Union[str, Any] = 0
for num in nums[1:]:
lowercase , lowercase :Any = (
max_excluding + num,
... | 236 |
import numpy
# List of input, output pairs
_UpperCAmelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_UpperCAmelCase : Optional[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase : Tuple = [2, 4, 1, 5... | 236 | 1 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
a : int = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass clas... | 150 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import Auto... | 150 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ , lowercase_ = " " ):
UpperCAmelCase = []
UpperCAmelCase = 0
for index, char in enumerate(lowercase_ ):
if char == separator:
split_words.append(string[l... | 78 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = list(range(len(lowercase_ ) ) )
UpperCAmelCase = [v / w for v, w in zip(lowercase_ , lowercase_ ... | 78 | 1 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase = logging.getLogger(__n... | 357 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCon... | 38 | 0 |
"""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_available... | 261 | """simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase( a ... | 261 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def lowercase__ ( __UpperCamelCase )-> Tuple:
# getting number of pixels in the image
UpperCamelCase ,UpperCamelCase = img.shape[0], img.shape[1]
# converting each p... | 368 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
... | 183 | 0 |
'''simple docstring'''
import unittest
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 ImageProcessingSavingTestMixin, prepare_image_inputs
... | 4 | """simple docstring"""
import requests
UpperCAmelCase__ = """""" # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase__ = """https://api.openweathermap.org/data/2.5/"""
def __UpperCAmelCase ( lowercase = "Chicago" ,lowercase = APPID ):
"""simple docstring"""
... | 289 | 0 |
from __future__ import annotations
from collections.abc import Generator
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = {}
lowerCamelCase = 2
while True:
lowerCamelCase = factor_map.pop(lowerCamelCase_ , low... | 367 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 66 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(_lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":... | 105 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 0 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillatio... | 369 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : str = 0
__lowerCamelCase : Tuple = len(SCREAMING_SNAKE_CASE__ )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE__ ):
if arr[i] > arr[j]:
num_inversions +=... | 194 | 0 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstr... | 36 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.rob... | 36 | 1 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklea... | 184 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 1000 ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = -1
lowerCAmelCase__ : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equa... | 184 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://h... | 140 | import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_UpperCAmelCase = models.Sequential()
# Step 1 - Convolution
#... | 140 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase: List[str] = Lock()
def a( A , A , A , A , A , A , A ) -> List[str]:
"""simple docstring"""
global pr... | 351 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 71 | 0 |
A__ : int = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
A__ : List[Any] = frozenset(['prompt', 'negative_prompt'])
A_... | 207 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = ('''dense.weight''', '''attention.self.query''', ''... | 207 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 3 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,... | 3 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class SC... | 24 |
'''simple docstring'''
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 UpperCAmelCase ( datasets.BuilderConfig ):
_l... | 70 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = len(lowerCAmelCase__ ) + 1
UpperCAmelCase_ = len(lowerCAmelCase__ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefi... | 353 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = 1 # (0 is vertical, 1 is horizontal)
de... | 241 | 0 |
'''simple docstring'''
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_imag... | 53 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 90 | 0 |
"""simple docstring"""
from collections import deque
def A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
_a = len(_lowerCAmelCase )
_a = deque()
_a = [False for _ in range(_lowerCAmelCase )]
_a = [-1 for _ in range... | 361 |
"""simple docstring"""
# 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
... | 153 | 0 |
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
... | 140 | import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_UpperCAmelCase = """scheduler_config.json"""
class UpperCAmelCase ( __A ):
'''simple docstring'... | 140 | 1 |
def _a ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> Optional[int]:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
SCREAMING_SNAKE_CASE__ ... | 364 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowerCamelCase : Tuple = collections.namedtuple('''_D... | 191 | 0 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 26 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
... | 126 | 0 |
def __magic_name__ ( __lowerCAmelCase : list[int] ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__lowerCamelCase = sum(__lowerCAmelCase ) / len(__lowerCAmelCase ) # Calculate the a... | 339 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def snake_case_ (_a : str = "https://www.worldometers.info/coronavirus" ):
UpperCAmelCase = BeautifulSoup(requests.get(_a ).text , '''html.parser''' )
UpperCAmelCase = sou... | 34 | """simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
... | 221 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ ) -> float:
if not nums:
raise ValueError('''List is empty''' )
return sum(lowerCAmelCase__ ) / len(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
... | 299 |
'''simple docstring'''
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 c... | 299 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-large''': ''... | 320 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
... | 259 | 0 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weig... | 221 |
import random
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : List[str] ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = [], [], []
for element in data:
if element < pivot:
... | 221 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger(__name__)
lowercase : str = {... | 3 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : str ... | 3 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available... | 33 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __versi... | 33 | 1 |
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