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 |
|---|---|---|---|---|
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 352 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case_ = _modexpt(__UpperCAmelCase, exponent // 2, ... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> list[list[int]]:
'''simple docstring'''
snake_case_ = []
create_all_state(1, __lowerCAmelCase, __lowerCAmelCase, [], __lowerCAmelCase ... | 353 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Union[str, Any] = 'src/transformers'
# This is to make sure the transf... | 72 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a : Tuple = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def __magic_name__ ... | 354 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = len(__a )
snake_case_ = len(__a )
snake_case_ = [[False for _ in range(m + 1 )] for _ in range... | 355 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unitte... | 72 | 0 |
'''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,
)
a : List[str] = {
"""configurat... | 356 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
snake_case_ = [
'''safety_checker/pytorch_model.bin''',
... | 72 | 0 |
'''simple docstring'''
from math import factorial, pi
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 30 ) -> float:
'''simple docstring'''
if not isinstance(__A, (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or f... | 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 0 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterToke... | 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/x... | 72 | 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 : Any = logging.get_logger(__name__)
a : List[Any] ... | 359 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 72 | 0 |
'''simple docstring'''
from typing import Any
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not input_list:
return []
snake_case_ = [input_list.count(__lowerCamelCase ) for value in input_list]
snake_case_ = ... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple ... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
snake_case_ = 42
snake_case_ = None
snake_case_ = None
def ... | 361 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, ... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
snake_case_ = str(bin(lowercase__ ) )[2:] # re... | 362 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n... | 72 | 0 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class a ( unittest.TestCase ):
def A_ ( self : Optional[int] ):
snake_cas... | 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
snake_case_ = [0] * no_of_processes
snake_case_ = [0] * ... | 364 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.model... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(__a, x % y )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstr... | 365 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( __UpperCAmelCase ) -> bytes:
'''simple docstring'''
snake_case_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
snake_case_ = requests... | 72 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Dict = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : Optional[int] ... | 366 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class ... | 72 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __magic_name__ ( ) -> str:
'''simple docstring'''
snake_case_ = HfArgumentParser(a_ )
snake_case_ = parser.parse_args_into_dataclasses... | 367 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
a : Tuple = 6_378_137.0
a : int = 6_356_752.314_245
a : Dict = 637_8137
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> ... | 72 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached... | 368 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list[list]:
'''simple docstring'''
snake_case_ = current_set.copy()
for row_index, row in enumerate(__UpperCAmelCase ):
snake_case_ = row[0]
for column_index, column ... | 72 | 0 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __magic_name__ ( *__UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase=True, __UpperCAmelCase=2 ) -> Dict:
'''simple docstring'''
from .... | 369 |
'''simple docstring'''
a : Dict = 6_5521
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = 0
for plain_chr in plain_text:
snake_case_ = (a + ord(__UpperCAmelCase... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class a ( __UpperCamelCase ):
snake_case_ = 42
snake_case_ = 42
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if not isinstanc... | 370 |
'''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
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] ... | 72 | 0 |
'''simple docstring'''
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 : Union[str, Any] = "src... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 72 | 0 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf... | 350 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a : int = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
for it... | 72 | 0 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a ( lowercase__ ):
def __init__( self... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : int = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMToken... | 72 | 0 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=__snake_case ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Dict , *lowercase_ : Optional[int] , **lowercase_ : Union[str, Any] ):
requires_backends(self... | 352 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case_ = _modexpt(__UpperCAmelCase, exponent // 2, ... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1000 ) -> Dict:
'''simple docstring'''
snake_case_ = 2**power
snake_case_ = str(_A )
snake_case_ = list(_A )
snake_case_ = 0
for i in list_num:
... | 353 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Union[str, Any] = 'src/transformers'
# This is to make sure the transf... | 72 | 0 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : int = 637_8137.0
a : Union[str, Any] = 635_6752.31_4245
a : str = 637_8137
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, ... | 354 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch... | 72 | 0 |
'''simple docstring'''
import os
import re
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 : str = logging.get_logger(__name__)
a : Lis... | 355 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unitte... | 72 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 356 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
snake_case_ = [
'''safety_checker/pytorch_model.bin''',
... | 72 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Any = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_AR... | 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 0 |
'''simple docstring'''
from ....utils import logging
a : Any = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
def __init__( self : str , lowercase_ : Union[str, Any] , lowercase_ : str=None , lowercase_ : int=2048 ):
snake_case_ ... | 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/x... | 72 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction... | 359 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 72 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaT... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple ... | 72 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : int = logging.get_logger(__name__)
a : List[str] = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
clas... | 361 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, ... | 72 | 0 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
def A_ ( self : str ):
snake_case_ = get_activation('''swish''' )
self.assertIsIns... | 362 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n... | 72 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a ( u... | 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 0 |
'''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 a ( SCREAMING... | 364 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.model... | 72 | 0 |
'''simple docstring'''
class a ( snake_case__ ):
pass
class a ( snake_case__ ):
pass
class a :
def __init__( self : Optional[Any] ):
snake_case_ = [
[],
[],
[],
]
... | 365 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( __UpperCAmelCase ) -> bytes:
'''simple docstring'''
snake_case_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
snake_case_ = requests... | 72 | 0 |
import re
import string
import numpy as np
import datasets
a : int = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
a : int = '\nArgs:\n predictions: List of predicted te... | 366 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class ... | 72 | 0 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
a : Dict = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
d... | 367 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
a : Tuple = 6_378_137.0
a : int = 6_356_752.314_245
a : Dict = 637_8137
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> ... | 72 | 0 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
a : List[Any] = 'path-to-your-trained-model'
a : Optional[int] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
a : List[str] = 'A photo of sks dog ... | 368 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list[list]:
'''simple docstring'''
snake_case_ = current_set.copy()
for row_index, row in enumerate(__UpperCAmelCase ):
snake_case_ = row[0]
for column_index, column ... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [], []
while len(_snake_case ) > 1:
snake_case_ = min(_snake_case ), max(_snake_case )
start.append(_snake... | 369 |
'''simple docstring'''
a : Dict = 6_5521
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = 0
for plain_chr in plain_text:
snake_case_ = (a + ord(__UpperCAmelCase... | 72 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_a ) ... | 370 |
'''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
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] ... | 72 | 0 |
'''simple docstring'''
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... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 72 | 0 |
'''simple docstring'''
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():
fr... | 350 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a : int = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
for it... | 72 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a : Optional[Any] = logging.get_logger(__name__)
a : Optional[int] = {
"""mic... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : int = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMToken... | 72 | 0 |
def __magic_name__ ( __UpperCAmelCase = 100 ) -> int:
'''simple docstring'''
snake_case_ = 0
snake_case_ = 0
for i in range(1, n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if ... | 352 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case_ = _modexpt(__UpperCAmelCase, exponent // 2, ... | 72 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_att... | 353 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Union[str, Any] = 'src/transformers'
# This is to make sure the transf... | 72 | 0 |
'''simple docstring'''
import string
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
snake_case_ = ''''''
for symbol in message:
if symbol in stri... | 354 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = [1]
for i in range(2, _A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bo... | 355 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unitte... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, ) -> Dict:
'''simple docstring'''
snake_case_ ... | 356 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
snake_case_ = [
'''safety_checker/pytorch_model.bin''',
... | 72 | 0 |
'''simple docstring'''
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 ..... | 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 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 tra... | 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/x... | 72 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a : List[Any] = logging.get_logger(__name__)
a : int = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.... | 359 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 72 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
snake_case_ = HfArgumentParser(a__ )
snake_case_ = parser.parse_args_into_data... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple ... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
a : List[str] = 100
a : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a : Optional[int] = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prim... | 361 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, ... | 72 | 0 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from t... | 362 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n... | 72 | 0 |
'''simple docstring'''
class a ( _A ):
pass
class a ( _A ):
pass
class a :
def __init__( self : Tuple ):
snake_case_ = [
[],
[],
[],
]
def A_ ( self : U... | 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 2, __UpperCAmelCase = 1, __UpperCAmelCase = 3, ) -> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('''The input va... | 364 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.model... | 72 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
a : str = logging.get_logger(__name__... | 365 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( __UpperCAmelCase ) -> bytes:
'''simple docstring'''
snake_case_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
snake_case_ = requests... | 72 | 0 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.... | 366 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class ... | 72 | 0 |
'''simple docstring'''
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def __magic_name__ ( *__UpperCAmelCase ) -> Tuple:
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE__, '''r''' ) as fh:
fcntl.flock(SCREAMING_SNAKE_CASE__, ... | 367 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
a : Tuple = 6_378_137.0
a : int = 6_356_752.314_245
a : Dict = 637_8137
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> ... | 72 | 0 |
'''simple docstring'''
import enum
import shutil
import sys
a : Tuple = shutil.get_terminal_size()
a : List[Any] = {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}
class a ( enum.Enum ):
snake_case_ = 0
snake... | 368 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list[list]:
'''simple docstring'''
snake_case_ = current_set.copy()
for row_index, row in enumerate(__UpperCAmelCase ):
snake_case_ = row[0]
for column_index, column ... | 72 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 369 |
'''simple docstring'''
a : Dict = 6_5521
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = 0
for plain_chr in plain_text:
snake_case_ = (a + ord(__UpperCAmelCase... | 72 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase__ : Optional[Any] = 50_0000
UpperCamelCase__ ,UpperCamelCase__ : List[Any] = os.path.split(__file__)
UpperCamelCa... | 370 |
'''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
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] ... | 72 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 72 | 0 |
'''simple docstring'''
from math import isclose, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = point_y / 4 / point_x
snake_case_ = 2 * normal_gradi... | 350 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a : int = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
for it... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 100_0000 ) -> int:
'''simple docstring'''
snake_case_ = set(range(3, _SCREAMING_SNAKE_CASE, 2 ) )
primes.add(2 )
for p in range(3, _SCREAMING_SNAKE_CASE, 2 ):
if p ... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : int = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMToken... | 72 | 0 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
a : int = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : str , *lowercase_ : Tuple , **lowercase_ : Dict ):
warning... | 352 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case_ = _modexpt(__UpperCAmelCase, exponent // 2, ... | 72 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = Omeg... | 353 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Union[str, Any] = 'src/transformers'
# This is to make sure the transf... | 72 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTe... | 354 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch... | 72 | 0 |
'''simple docstring'''
class a :
def __init__( self : List[str] , lowercase_ : Any ):
snake_case_ = set_counts
snake_case_ = max(lowerCamelCase_ )
snake_case_ = len(lowerCamelCase_ )
snake_case_ = [1... | 355 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unitte... | 72 | 0 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=5 ) -> Optional[Any]:
'''simple docstring'''
assert masked_input.count('''<mask>'''... | 356 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
snake_case_ = [
'''safety_checker/pytorch_model.bin''',
... | 72 | 0 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
... | 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {
'huggingface/time-series-transformer-tourism-monthly': (
'https:... | 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/x... | 72 | 0 |
'''simple docstring'''
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,... | 359 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 72 | 0 |
'''simple docstring'''
import torch
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
if torch.cuda.is_available():
snake_case_ = torch.cuda.device_count()
else:
snake_case_ = 0
print(F"Successfully ran on {num_gpus} GP... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple ... | 72 | 0 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torc... | 361 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, ... | 72 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = HfArgumentParser(_lowercase )
snake_case_ = parser.parse_args... | 362 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n... | 72 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : str = {
"""configuration_rembert""": ["""REMBERT_P... | 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 0 |
'''simple docstring'''
a : Tuple = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/hu... | 364 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.model... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
snake_case_ = 1
for i in range(1, num + 1 ):
fact *= i
return fact
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docst... | 365 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( __UpperCAmelCase ) -> bytes:
'''simple docstring'''
snake_case_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
snake_case_ = requests... | 72 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
a : Tuple = '\\n\n'
a : int = '\nPerplexity (PPL) is one of the most common metrics for evaluating language m... | 366 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class ... | 72 | 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_video_inputs
if is_torch_av... | 367 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
a : Tuple = 6_378_137.0
a : int = 6_356_752.314_245
a : Dict = 637_8137
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> ... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = []
create_all_state(1, __UpperCamelCase, __UpperCamelCase, [], __UpperCamelCase )
... | 368 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list[list]:
'''simple docstring'''
snake_case_ = current_set.copy()
for row_index, row in enumerate(__UpperCAmelCase ):
snake_case_ = row[0]
for column_index, column ... | 72 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, ... | 369 |
'''simple docstring'''
a : Dict = 6_5521
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = 0
for plain_chr in plain_text:
snake_case_ = (a + ord(__UpperCAmelCase... | 72 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...m... | 370 |
'''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
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] ... | 72 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __magic_name__ ( ) -> int:
'''simple docstring'''
snake_case_ = ArgumentParser(
desc... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 72 | 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, ... | 350 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a : int = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
for it... | 72 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a : Optional[Any] = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : int = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMToken... | 72 | 0 |
def __magic_name__ ( __UpperCAmelCase = 50 ) -> str:
'''simple docstring'''
snake_case_ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start in range(row_len... | 352 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case_ = _modexpt(__UpperCAmelCase, exponent // 2, ... | 72 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = []
snake_case_ = []
snake_case_ = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
... | 353 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Union[str, Any] = 'src/transformers'
# This is to make sure the transf... | 72 | 0 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''', [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''', num_bytes=1337, num_examples=42, data... | 354 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch... | 72 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
... | 355 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unitte... | 72 | 0 |
'''simple docstring'''
a : List[Any] = 8.3_144_598
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
... | 356 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
snake_case_ = [
'''safety_checker/pytorch_model.bin''',
... | 72 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a : List[Any] = logging.get_logger(__name__)
class a ( lowerCamelCase__ ):
def __init__( self : Optional[Any] , *lower... | 357 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 0 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __... | 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/x... | 72 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class a ( __a ):
def __init__( self : Any , *lowercase_ : Dict , **lowercase_ : List[str] ):
super().__init__(*a__ , **a__ )
def A_ ( self : str , lowerca... | 359 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 72 | 0 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a : List[str] = re.compile(r'\b(a|an|the)\b', re.UNICODE)
a : Optional[int] = None
def __magic_name__ ( ) -> Tuple:
'''simple docstri... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple ... | 72 | 0 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone imp... | 361 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
_enforce_args(__UpperCAmelCase, __UpperCAmelCase )
if n == 0:
return 0
snake_case_ = float('''-inf''' )
for i in range(1, ... | 72 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import req... | 362 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n... | 72 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# Se... | 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 0 |
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