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 argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowercase : Optional[Any] = ['small', 'medium', 'large']
_lowercase : Optional[int] = 'lm_head.decoder.weight'
_lowercase : Any = 'lm_head.weight'
... | 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _Up... | 86 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_lowercase : Tuple = logging.getLogger(__name__)
... | 371 |
"""simple docstring"""
from __future__ import annotations
import bisect
def lowercase__ ( snake_case_ :list[int] , snake_case_ :int , snake_case_ :int = 0 , snake_case_ :int = -1 ):
if hi < 0:
__UpperCAmelCase = len(snake_case_ )
while lo < hi:
... | 86 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
f... | 350 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 86 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Co... | 351 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except ... | 86 | 0 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowercase : List[str] = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False... | 352 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requir... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowercase : Any = {
'configuration_trocr': ['TROCR_PRETRAINED... | 353 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Optional[Any] , snake_case_ :str ):
while a != 0:
__UpperCAmelCase , __UpperCAmelCase = b % a, a
return b
def lowercase__ ( snake_case_ :Optional[int] , snake_case_ :Union[str, Any]... | 354 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 86 | 0 |
"""simple docstring"""
from timeit import timeit
def lowercase__ ( snake_case_ :int ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__UpperCAmelCase = 0
while number:
number &= number - 1
result += 1
return result
... | 355 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from... | 86 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Dict = logging.get_logger(__name__)
_lowercase : Tuple = {
'google/pix2struct-textcaps-base': (
... | 356 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
... | 86 | 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 lowercase__ ( snake_case_ :Optional[Any] , snake_... | 357 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 86 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowercase : Optional[int] = logging.get_logger(__name__) # pylint: disable=in... | 358 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Any ):
if num < 0:
return False
__UpperCAmelCase = num
__UpperCAmelCase = 0
while num > 0:
__UpperCAmelCase = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__mai... | 359 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowercase__ ( ... | 86 | 0 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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... | 360 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowercase : str = {
# 1536-bit
5: {
... | 86 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( __lo... | 361 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowercase : List[Any] = TypeVar('T')
class _UpperCAmelCase ( Generic[T] ):
a__ : deque[T] # Cache store of keys
a__ : s... | 86 | 0 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowercase__ ( snake_case_ :Any ): # pi... | 362 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self : str , _lowercase : list ):
__UpperCAmelCase = set_counts
__UpperCAmelCase = max(_lowercase )
__UpperCAmelCase = len(_lowercase )
__UpperCAmelCase ... | 86 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowercase = logging.get_lo... | 363 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :float , snake_case_ :float , snake_case_ :float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise Va... | 86 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
_lowercase : ... | 364 |
"""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
_lowercase : Tuple = l... | 86 | 0 |
"""simple docstring"""
from manim import *
class _UpperCAmelCase ( __snake_case ):
def a ( self : List[str] ):
__UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase = Rectangle(height=0.46 , width=0.46 ).set... | 365 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import... | 86 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _UpperCAmelCase ( unittest.TestCase )... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.st... | 367 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import... | 86 | 0 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
_lowercase : List[str] = HfArgumentParser(InitializationArguments)
_lowercase : List[str] = parser.... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 86 | 0 |
"""simple docstring"""
from timeit import timeit
_lowercase : Optional[Any] = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# ... | 369 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase : int = pd.read_csv('sample_data.csv', hea... | 86 | 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
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin... | 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _Up... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :Tuple ):
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(snake... | 371 |
"""simple docstring"""
from __future__ import annotations
import bisect
def lowercase__ ( snake_case_ :list[int] , snake_case_ :int , snake_case_ :int = 0 , snake_case_ :int = -1 ):
if hi < 0:
__UpperCAmelCase = len(snake_case_ )
while lo < hi:
... | 86 | 0 |
"""simple docstring"""
import math
class _UpperCAmelCase :
def a ( self : Tuple , _lowercase : Tuple , _lowercase : Dict ):
__UpperCAmelCase = 0.0
__UpperCAmelCase = 0.0
for i in range(len(_lowercase ) ):
da += m... | 350 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 86 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transform... | 351 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except ... | 86 | 0 |
"""simple docstring"""
import string
def lowercase__ ( snake_case_ :str ):
__UpperCAmelCase = ''''''
for i in sequence:
__UpperCAmelCase = ord(UpperCamelCase__ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:... | 352 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requir... | 86 | 0 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_lowercase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_lowercase ... | 353 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase__ ( snake_case_ :dict , snake_case_ :str , snake_case_ :set , snake_case_ :set , snake_case_ :dict , snake_case_ :dict , snake_case_ :PriorityQ... | 354 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 86 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase : str = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class _Upper... | 355 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from... | 86 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,... | 356 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
... | 86 | 0 |
"""simple docstring"""
import socket
def lowercase__ ( ):
__UpperCAmelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__UpperCAmelCase = socket.gethostname()
__UpperCAmelCase = 12_312
sock.connect((host, port) )
sock.send(b'''Hello server... | 357 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 86 | 0 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata a... | 358 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase : str = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', '... | 359 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowercase__ ( ... | 86 | 0 |
"""simple docstring"""
from timeit import timeit
_lowercase : Union[str, Any] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama... | 360 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowercase : str = {
# 1536-bit
5: {
... | 86 | 0 |
"""simple docstring"""
_lowercase : str = 8.3144598
def lowercase__ ( snake_case_ :float , snake_case_ :float ):
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass canno... | 361 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowercase : List[Any] = TypeVar('T')
class _UpperCAmelCase ( Generic[T] ):
a__ : deque[T] # Cache store of keys
a__ : s... | 86 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : ... | 362 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self : str , _lowercase : list ):
__UpperCAmelCase = set_counts
__UpperCAmelCase = max(_lowercase )
__UpperCAmelCase = len(_lowercase )
__UpperCAmelCase ... | 86 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( a__ ):
a__ : str = """SpeechT5FeatureExtractor"""
a__ : Optional[int] = """SpeechT5Tokenizer"""
def __init__( self : Any , _lowercase : ... | 363 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :float , snake_case_ :float , snake_case_ :float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise Va... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :list ):
def merge(snake_case_ :list , snake_case_ :list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len... | 364 |
"""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
_lowercase : Tuple = l... | 86 | 0 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowe... | 365 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import... | 86 | 0 |
"""simple docstring"""
import os
from math import logaa
def lowercase__ ( snake_case_ :str = "base_exp.txt" ):
__UpperCAmelCase = 0
__UpperCAmelCase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase_... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Optional[Any] ):
if n == 1 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return 0
elif n == 2:
return 1
else:
__UpperCAmelCase = [0, 1]
for i in range(2 , n + 1 ):
sequen... | 367 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import... | 86 | 0 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCAmelCase ( _a ):
a__ : str = CustomTokenizer
pass
| 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 86 | 0 |
"""simple docstring"""
import re
def lowercase__ ( snake_case_ :str ):
__UpperCAmelCase = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(__UpperCamelCase , __UpperCamelCase ):
return match.string == phone
return False
if _... | 369 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase : int = pd.read_csv('sample_data.csv', hea... | 86 | 0 |
"""simple docstring"""
import random
from typing import Any
def lowercase__ ( snake_case_ :list ):
for _ in range(len(a__ ) ):
__UpperCAmelCase = random.randint(0 , len(a__ ) - 1 )
__UpperCAmelCase = random.randint(0 , len(a__ ... | 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _Up... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase : List[Any] = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctr... | 371 |
"""simple docstring"""
from __future__ import annotations
import bisect
def lowercase__ ( snake_case_ :list[int] , snake_case_ :int , snake_case_ :int = 0 , snake_case_ :int = -1 ):
if hi < 0:
__UpperCAmelCase = len(snake_case_ )
while lo < hi:
... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :str = "The quick brown fox jumps over the lazy dog" , ):
__UpperCAmelCase = set()
# Replace all the whitespace in our sentence
__UpperCAmelCase = input_str.replace(''' ''' , '''''' )
for alpha in in... | 350 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowercase : str = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to ... | 351 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except ... | 86 | 0 |
"""simple docstring"""
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _lowerCamelCase ( _lowerCAmelCase ):
a__ : Dict = "facebook/bart-large-mnli"
a__ : Optional[int] = (
... | 352 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requir... | 86 | 0 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
... | 353 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase : str = {
'google/umt5-small': ... | 354 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int = 1_000 ):
__UpperCAmelCase = 1, 1
__UpperCAmelCase = 2
while True:
__UpperCAmelCase = 0
__UpperCAmelCase = fa + fa
__UpperCAmelCase = fa, f
index += 1
fo... | 355 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from... | 86 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : List[str] , _lowercase : Tuple , _lowercase : Any ):
__UpperCAmelCas... | 356 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
... | 86 | 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 PreTrainedTokenizer
from ...utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : ... | 357 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :List[str] ):
__UpperCAmelCase = 1
__UpperCAmelCase = 2
while i * i <= n:
__UpperCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
... | 358 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Any = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is... | 359 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowercase__ ( ... | 86 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCamelCase__ ):
a__ : Union[str, Any] = ["sentencepiece"]
def __init__( self : List[str] , *_lowercase : Optional[Any] , **_lower... | 360 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowercase : str = {
# 1536-bit
5: {
... | 86 | 0 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 361 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowercase : List[Any] = TypeVar('T')
class _UpperCAmelCase ( Generic[T] ):
a__ : deque[T] # Cache store of keys
a__ : s... | 86 | 0 |
"""simple docstring"""
_lowercase : int = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'To use `datasets`, Python>=3.7 is required, and the current versio... | 362 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self : str , _lowercase : list ):
__UpperCAmelCase = set_counts
__UpperCAmelCase = max(_lowercase )
__UpperCAmelCase = len(_lowercase )
__UpperCAmelCase ... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowercase = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}... | 363 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :float , snake_case_ :float , snake_case_ :float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise Va... | 86 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,... | 364 |
"""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
_lowercase : Tuple = l... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : int = {
"configuration_roberta":... | 365 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import... | 86 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
_lowercase : Dict = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging im... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
_lowercase : Tuple = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable... | 367 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import... | 86 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_lowercase : str = datasets.load_iris()
_lowercase : Optional[Any] = np.array(data['data'])
_lowercase : Optional... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase : Any = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
im... | 369 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase : int = pd.read_csv('sample_data.csv', hea... | 86 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from ... | 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _Up... | 86 | 0 |
"""simple docstring"""
import argparse
import os
import re
_lowercase : Union[str, Any] = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowercase : Tuple =... | 371 |
"""simple docstring"""
from __future__ import annotations
import bisect
def lowercase__ ( snake_case_ :list[int] , snake_case_ :int , snake_case_ :int = 0 , snake_case_ :int = -1 ):
if hi < 0:
__UpperCAmelCase = len(snake_case_ )
while lo < hi:
... | 86 | 0 |
"""simple docstring"""
import os
import platform
import sys
_lowercase : Dict = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version_... | 350 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 86 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
_lowercase : List[Any] = TypeVar('_T')
class _UpperCAmelCase ( Generic[_T] ):
def __init__( self : int , _lowercase : Iterable[_T] | None = None ):
__Upp... | 351 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except ... | 86 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
... | 352 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requir... | 86 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffus... | 353 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetic... | 354 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 86 | 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
_lowercase : Tuple = l... | 355 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
_lowercase : Dict = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_lowercase : Optional[int] ... | 356 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
... | 86 | 0 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime... | 357 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Optional[int] = {'configuration_fnet': [... | 358 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 0 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn ... | 359 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowercase__ ( ... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def lowercase__ ( ):
__UpperCAmelCase = {}
__UpperCAmelCase = 2
while True:
__UpperCAmelCase = factor_map.pop(snake_case_ , snake_... | 360 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowercase : str = {
# 1536-bit
5: {
... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int ):
__UpperCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowercase__ ( snake_case_ :int ):
__UpperCAmelCase = 0
while number > 0:
__UpperCAmelCase ... | 361 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowercase : List[Any] = TypeVar('T')
class _UpperCAmelCase ( Generic[T] ):
a__ : deque[T] # Cache store of keys
a__ : s... | 86 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx impor... | 362 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self : str , _lowercase : list ):
__UpperCAmelCase = set_counts
__UpperCAmelCase = max(_lowercase )
__UpperCAmelCase = len(_lowercase )
__UpperCAmelCase ... | 86 | 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, ImageClassifierOutputWithNoAtten... | 363 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :float , snake_case_ :float , snake_case_ :float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise Va... | 86 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requires_backends(se... | 364 |
"""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
_lowercase : Tuple = l... | 86 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : Optional[Any] ):
# test for the above condition
self.test()
def a ( self : ... | 365 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import... | 86 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int ):
return int((input_a, input_a).count(0 ) != 0 )
def lowercase__ ( ):
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert... | 367 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import... | 86 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 86 | 0 |
"""simple docstring"""
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))
| 369 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase : int = pd.read_csv('sample_data.csv', hea... | 86 | 0 |
"""simple docstring"""
from collections import defaultdict
class _UpperCAmelCase :
def __init__( self : int , _lowercase : str , _lowercase : List[Any] ):
__UpperCAmelCase = total # total no of tasks (N)
# DP table will have a dimension of ... | 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _Up... | 86 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowercase__ ( snake_case_ :Dict ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class _UpperCAmelCase... | 371 |
"""simple docstring"""
from __future__ import annotations
import bisect
def lowercase__ ( snake_case_ :list[int] , snake_case_ :int , snake_case_ :int = 0 , snake_case_ :int = -1 ):
if hi < 0:
__UpperCAmelCase = len(snake_case_ )
while lo < hi:
... | 86 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowercase : int = pd.read_csv('sample_data.csv', head... | 350 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 86 | 0 |
"""simple docstring"""
import numpy as np
def lowercase__ ( snake_case_ :np.ndarray ):
return 1 / (1 + np.exp(-vector ))
def lowercase__ ( snake_case_ :np.ndarray ):
return vector * sigmoid(snake_case_ )
if __name__ == "__main__":
import doctest
... | 351 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except ... | 86 | 0 |
"""simple docstring"""
import math
def lowercase__ ( snake_case_ :int ):
__UpperCAmelCase = []
__UpperCAmelCase = 2
__UpperCAmelCase = int(math.sqrt(snake_case_ ) ) # Size of every segment
__UpperCAmelCase = [True] * (end + ... | 352 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCAmelCase ):
a__ : Union[str, Any] = ["onnx"]
def __init__( self : Any , *_lowercase : Dict , **_lowercase : Any ):
requir... | 86 | 0 |
"""simple docstring"""
import math
import os
import sys
def lowercase__ ( snake_case_ :str ):
__UpperCAmelCase = ''''''
try:
with open(snake_case_ , '''rb''' ) as binary_file:
__UpperCAmelCase = binar... | 353 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Tuple ... | 86 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Tuple ):
__UpperCAmelCase = [False] * len(snake_case_ )
__UpperCAmelCase = [-1] * len(snake_case_ )
def dfs(snake_case_ :Union[str, Any] , snake_case_ :Tuple ):
__UpperCAmelCase = True
... | 354 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_av... | 355 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from... | 86 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from t... | 356 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
... | 86 | 0 |
"""simple docstring"""
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _UpperCAmelCase ( _lowerCAmelCase , _lowerCAmelCase ):
a__ : Tu... | 357 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ... | 86 | 0 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Any ... | 358 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : Tuple = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json'... | 359 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowercase__ ( ... | 86 | 0 |
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