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 sys
_lowerCAmelCase : str = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''1254069874715852386305071569329096329522744304355... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : int = get_tests_di... | 340 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_lowerCamelCase : str = u
for i in range(1 , _lowerCamelCase ):
_lowerCamelCase :... | 340 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowerCAmelCase : Optional[int] = '''__DUMMY_TRANSFORMERS_USER__'''
_lowerCAmelCase : Dict = '''Dummy User'... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowerCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowerCAmelCase : list[int] = [ord(letter) ... | 340 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 340 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForS... | 340 | 1 |
"""simple docstring"""
import argparse
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 acce... | 340 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 1 |
"""simple docstring"""
from collections import UserDict
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_availab... | 340 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" ... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowe... | 340 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
_lowerCAmelCas... | 340 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import... | 340 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 340 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers... | 340 | 1 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def ... | 340 |
"""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
@datac... | 340 | 1 |
"""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,
)
if is_sentencepiece_available():
from ..ta.... | 340 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 340 | 1 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_lowerCAmelCase : Optional[Any] = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster s... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_validate_point(_lowerCamelCase )
_validate_point(_lowerCamelCase )
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError("Both poin... | 340 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : Optional[Any] = {
'''huggingface/time-series-transformer-tourism... | 340 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modu... | 340 | 1 |
"""simple docstring"""
import math
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
return math.sqrt(_lowerCamelCase ) * math.sqrt(_lowerCamelCase ) == num
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring''... | 340 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 1 |
"""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_distilbert import DistilBertTokenizer
_lowerCAmelCase : int = logging.get_... | 340 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extractio... | 340 | 1 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class A_ ( _a ):
lowerCAmelCase__ = 'EncodecFeatureExtractor'
lowerCAmelCase__ = ('T5Tokenizer', 'T5TokenizerFast')
... | 340 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmel... | 340 | 1 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = int(_lowerCamelCase )
assert n... | 340 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax imp... | 340 | 1 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
_lowerCAmelCase : Tuple = '''src/transformers'''
# Matches is_xxx_available()
_lowerCAmelCase : str = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCA... | 340 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class A_ ( _a ):
def __init__( self: List[Any] ,*__lowerCAmelCase: Dict ,**__lowe... | 340 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def ... | 340 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[int] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmel... | 340 |
"""simple docstring"""
_lowerCAmelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_lowerCamelCase , _lowerCamelCas... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 | 1 |
"""simple docstring"""
import numpy as np
import datasets
_lowerCAmelCase : int = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distan... | 340 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" ... | 340 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowerCAmelCase : Optional[int] = '''__DUMMY_TRANSFORMERS_USER__'''
_lowerCAmelCase : Dict = '''Dummy User'... | 340 | 1 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
_lowerCAmelCase : List[Any] = '''
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 classifi... | 340 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 | 1 |
"""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_convbert import ConvBertTokenizer
_lowerCAmelCase : List[Any] = logging.ge... | 340 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForS... | 340 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowerCAmelCase : Optional[Any] = (720, 1280) # Height, Width
_lowerCAmelCase : int = (0.4, 0.6) # if height or width lower than this scale... | 340 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_low... | 340 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" ... | 340 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ..... | 340 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowe... | 340 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_lowerCAmelCase : Optio... | 340 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import... | 340 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : int = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/as... | 340 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers... | 340 | 1 |
"""simple docstring"""
_lowerCAmelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase... | 340 |
"""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
@datac... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
_lowerCamelCase : List[str] = int(_lowerCamelCase )
_lowerCam... | 340 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 340 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : str = logging.get_logger(__name__)
_lowerCAmelCase : Tuple = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_validate_point(_lowerCamelCase )
_validate_point(_lowerCamelCase )
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError("Both poin... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase = 1000 ) -> int:
'''simple docstring'''
_lowerCamelCase : Tuple = 3
_lowerCamelCase : List[Any] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == ... | 340 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modu... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class A_ :
def __init__( self: Union[str, Any] ,__lowerCAmelCase: int ):
'''simple docstring'''
_lowerCamelCase : List[Any] = value
_lowerCamelCase : ... | 340 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
_lowerCAmelCase : Union[str, Any] = 8.988e9 # units = N * m^s * C^-2
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> dict[str, float]:
'''simple docstr... | 340 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extractio... | 340 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmel... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) -> None:
'''simple docstring'''
_lowerCamelCase : List[str] = len(_lo... | 340 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax imp... | 340 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCA... | 340 | 1 |
"""simple docstring"""
import argparse
import copy
def lowerCamelCase_( _lowerCamelCase ) -> List[Any]:
'''simple docstring'''
_lowerCamelCase : Dict = {}
with open(_lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neig... | 340 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def ... | 340 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 1 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 |
"""simple docstring"""
_lowerCAmelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase... | 340 | 1 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
_lowerCAmelCase : Tuple = HfArgumentParser(InitializationArguments)
_lowerCAmelCase : Optional[Any] = parser.p... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
... | 340 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
... | 340 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase_( _lowerCamelCase ) -> Optional[int]:
'''simple docstring'''
def wrapper(*_lowerCamelCase ... | 340 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowerCAmelCase : Optional[int] = '''__DUMMY_TRANSFORMERS_USER__'''
_lowerCAmelCase : Dict = '''Dummy User'... | 340 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCAmelCase : Optional[int] = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Mach... | 340 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase = 100 ) -> int:
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = n * (n + 1) * (2 * n + 1) / 6
_lowerCamelCase : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_o... | 340 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForS... | 340 | 1 |
"""simple docstring"""
from statistics import mean
import numpy as np
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> list:
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = 0
# Number of... | 340 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase : Optional[int] = {
'''configuration_vision_encoder_decoder''': ['''VisionE... | 340 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" ... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
_lowerCAmelCase : Tuple = tuple[int, int]
class A_ :
def __init__( self: Union[str, Any] ,__lowerCAmelCase: set[int] ,__lowerCAmelCase: Mapping[EdgeT, int] ):
... | 340 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowe... | 340 | 1 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_lowerCamelCase : List[Any] = x
_lowerCamelCase : int =... | 340 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import... | 340 | 1 |
"""simple docstring"""
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCAmelCase : Union[str, Any] = '''Usage of script: script_name <size_of_canvas:int>'''
_lowerCAmelCase : List[str] = [0] * 100 + [1]... | 340 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers... | 340 | 1 |
"""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`''') | 340 |
"""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
@datac... | 340 | 1 |
"""simple docstring"""
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_lowerCAmelCase : Optional[int] = '''.'''
if __name__ == "__main__":
_lowerCAmelCase : Tuple = os.path.join... | 340 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
if not head:
return True
# split the list to two parts
_lowerCamelCase, _lowerCamelCase : str = head.next, head
while fast and fast.next:
_lowerCamelCase ... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_validate_point(_lowerCamelCase )
_validate_point(_lowerCamelCase )
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError("Both poin... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fr... | 340 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modu... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_lowerCamelCase ,... | 340 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : int = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8_660_254])
_lowerCAmelCase : str =... | 340 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extractio... | 340 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCA... | 340 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmel... | 340 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Dict = {
'''Salesforce/blip-vqa-base''': '''https... | 340 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax imp... | 340 | 1 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline ... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCA... | 340 | 1 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accele... | 340 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def ... | 340 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from tran... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 1 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCamelCase_( _lowerCamelCase = 3 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance(_lowerCamelC... | 340 |
"""simple docstring"""
_lowerCAmelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase... | 340 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.k... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 | 1 |
"""simple docstring"""
import os
import sys
import unittest
_lowerCAmelCase : Any = 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_dummies # noqa: E402
from check_dummies import create_dummy_file... | 340 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
... | 340 | 1 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase : Union... | 340 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowerCAmelCase : Optional[int] = '''__DUMMY_TRANSFORMERS_USER__'''
_lowerCAmelCase : Dict = '''Dummy User'... | 340 | 1 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> np.array:
'''simple docstring'''
_lowerCamelCase : List[str] = ... | 340 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : List[Any] = {
'''configuration_mobilebert''': [
'''MOBILEBE... | 340 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForS... | 340 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
# TODO: upload to AWS
_lowerCAmelCase : Union[str, Any] = {
'''yjernite/retribert-base-uncased''': (
'''h... | 340 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 1 |
"""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()
... | 340 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" ... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not... | 340 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowe... | 340 | 1 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowerCAmelCase : Op... | 340 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import... | 340 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_d... | 340 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers... | 340 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
lowerCAmelCase__ ... | 340 |
"""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
@datac... | 340 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( _a ... | 340 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 340 | 1 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# 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
_lowerCAmelCase : Dict = '''.'''
#... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
_validate_point(_lowerCamelCase )
_validate_point(_lowerCamelCase )
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError("Both poin... | 340 | 1 |
"""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 ... | 340 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modu... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> list:
'''simple docstring'''
if n_term == "":
return []
_lowerCamelCase : list = []
for temp in range(int(_lowerCamelCase ) ):
series.append(F"""1/{temp + 1}""" if series else "1" )... | 340 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
fr... | 340 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extractio... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Optional[Any]:
'''simple docstring'''
_lowerCamelCase : Optional[int] = [False] * len(_lowerCamelCase )
_lowerCamelCase : Lis... | 340 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
class A_ ( _a ):
lowerCAmelCase__ = 'masked_bert'
def __init__( self: Union[str, Any] ,__lowerCAmel... | 340 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForS... | 340 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax imp... | 340 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCA... | 340 | 1 |
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowerCAmelCase : Optional[int] = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname ... | 340 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
for param in module.parameters():
_lowerCamelCase : Optional[int] = False
def ... | 340 | 1 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase_( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = 9
_lowerCamelCase : Tuple = [
[0, 1, 4],
[0, 7, 8],
[... | 340 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = len(_lowerCamelCase )
_lowerCamelCase : int = ... | 340 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_availab... | 340 |
"""simple docstring"""
_lowerCAmelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase... | 340 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> list:
'''simple docstring'''
_lowerCamelCase : Optional[int] = []
_lowerCamelCase, _lower... | 340 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : str = abs(_lowerCamelCase )
_lowerCamelCase : List[Any] = 0
while n > 0:
res += n % 10
n //= 10
return res
def l... | 340 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
... | 340 | 1 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState... | 340 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_lowerCAmelCase : Optional[int] = '''__DUMMY_TRANSFORMERS_USER__'''
_lowerCAmelCase : Dict = '''Dummy User'... | 340 | 1 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkp... | 340 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_lowerCAmelCase : Optional[Any] = logging.getLogger()
... | 340 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForS... | 340 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> None:
'''simple docstring'''
_lowerCamelCase : Any = len(_lowerCamelCase )
print("The following activities are selected:" )
# The first activity is always selected
_low... | 340 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils impo... | 340 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : int = str(_lowerCamelCase )
return len(_lowerCamelCase ) == 9 and set(_lowerCamelCase ) == set("123456789" ... | 340 | 1 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_lowerCAmelCase : str = logging.get_logger(__name__)
class A_ ( _a ):
lowerCAmelCase__ ... | 340 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class A_ ( _a ):
lowe... | 340 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowerCamelCase_( _lowerCamelCase )... | 340 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import... | 340 | 1 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_... | 340 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers... | 340 | 1 |
"""simple docstring"""
import datasets
_lowerCAmelCase : List[Any] = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
... | 340 |
"""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
@datac... | 340 | 1 |
"""simple docstring"""
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing... | 340 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 340 | 1 |
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