code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int ):
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 637 | 0 |
"""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 accel... | 713 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : List[Any]=None ):
lowerCAmelCase : Any = (path or []) + [u]
for ... | 637 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _snake_case ( ):
print('''Making key files...''' )
make_key_files('''rsa''' , 1024 )
print('... | 714 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : Union[str, Any] ):
lowerCAmelCase : Optional[int] = """"""
for i in table:
res += inp[i - 1]
return res
def _snake_case ( _snake_case : Dict ... | 715 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSchedul... | 716 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 0 |
"""simple docstring"""
from manim import *
class snake_case_( _A ):
def lowerCamelCase__ ( self : Dict ):
lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Any = Rectangle(height=0.46 ,... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case_( metaclass=_UpperCAmelCase ):
__UpperCamelCase = ['''sentencepiece''']
def __init__( self : Union[str, Any] , *UpperCamelCase_ : Union[str, Any] , **UpperCamelCas... | 718 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 0 |
def _snake_case ( _snake_case : List[str] ):
lowerCAmelCase : List[str] = len(_A )
for i in range(_A ):
for j in range(i + 1 , _A ):
if numbers[j] < numbers[i]:
lowerCAmelCase, lowerCAmelCase : Union[... | 719 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case__ : Optional[Any] = namedtuple(
''... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : Any ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 720 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return base * power(_snake_case , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : Un... | 637 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : Union[str, ... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : list[int] , _snake_case : list[int] ):
lowerCAmelCase : Dict = len(snake_case__ )
print('''The following activities are selected:''' )
# The first activity is always selected
lowerCAmelCa... | 700 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 637 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
def _snake_case ( _snake_case : int , _snake_case : Union[str, Any] ):
l... | 701 |
"""simple docstring"""
from __future__ import annotations
class snake_case_:
def __init__( self : int , UpperCamelCase_ : str , UpperCamelCase_ : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = text, pattern
lowerCAmelCas... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : float , _snake_case : float ):
return round(float(moles / volume ) * nfactor )
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : ... | 702 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_( a__ ):
pass
class snake_case_:
def __init__( self : Any , UpperCamelCase_ : Any ):
lowerCAmelCase : Any = data
lowerCAmelCa... | 637 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers... | 703 |
"""simple docstring"""
from torch import nn
class snake_case_( nn.Module ):
def __init__( self : int , UpperCamelCase_ : int , UpperCamelCase_ : int ):
super().__init__()
lowerCAmelCase : str = class_size
lowerC... | 637 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
snake_case__ : int = "examples/"
snake_case__ : List[Any] = {
"examples": (re.compile(R'''^check_min_version\(\"[^\"]+\"\)\s*$''', re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"ini... | 704 |
"""simple docstring"""
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : str ):
lowerCAmelCase : Dict = val
lowerCAmelCase : str = None
lowerCAmelCase : Dict = None
def ... | 637 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.... | 705 |
"""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
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__... | 637 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : int = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_... | 706 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteri... | 637 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 707 |
"""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
@datacla... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return int((input_a, input_a).count(0 ) == 0 )
def _snake_case ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , ... | 708 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_comm... | 637 | 0 |
"""simple docstring"""
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy... | 709 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_inter... | 710 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 637 | 0 |
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
... | 711 |
"""simple docstring"""
snake_case__ : List[Any] = '''Tobias Carryer'''
from time import time
class snake_case_:
def __init__( self : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_( UpperCamelCase_ ):
pass
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : Any ):
lowerCAmelCase : Any = data... | 712 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 637 | 0 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDIMParallelScheduler,)
__UpperCamelCase = (('''eta''', 0.0), ('''num_inference_steps''', 50))
... | 713 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : List[Any]=None ):
lowerCAmelCase : Any = (path or []) + [u]
for ... | 637 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class snake_case_( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , UpperCamelCase_ : List[str] ):
lowerCAmelCase ... | 714 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 637 | 0 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
snake_case__ : Optional[int] = logging.get_logger(__name__)
def _snake_case ( _snake_case... | 715 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 0 |
"""simple docstring"""
def _snake_case ( ) -> Optional[Any]:
lowerCAmelCase : List[str] = []
lowerCAmelCase : Any = 1
while len(_lowerCAmelCase ) < 1E6:
constant.append(str(_lowerCAmelCase ) )
i += 1
lowerCAmelC... | 716 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 0 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
snake_case__ = False
class snake_case_( unittes... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : str = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config... | 718 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : int = {
'''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
'''processing_mgp_str''': ['''MgpstrProcessor'''],
... | 719 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case__ : Optional[Any] = namedtuple(
''... | 637 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ):
lowerCAmelCase : Any = ascii_letters + digits + punctuation
return "... | 720 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return base * power(_snake_case , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : Un... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] ):
if len(_UpperCamelCase ) == 0:
return array
lowerCAmelCase, lowerCAmelCase : Optional[int] = min(_UpperCamelCase ), max(_UpperCamelCase ... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 637 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transfor... | 700 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 637 | 0 |
def _snake_case ( _snake_case : List[Any] , _snake_case : Optional[Any] ):
return base * power(_SCREAMING_SNAKE_CASE , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : List... | 701 |
"""simple docstring"""
from __future__ import annotations
class snake_case_:
def __init__( self : int , UpperCamelCase_ : str , UpperCamelCase_ : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = text, pattern
lowerCAmelCas... | 637 | 0 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _snake_case ( _snake_case : List[str] ):
# A local function to see if a dot lands in the circle.
def is_in_circle(_snake_case :... | 702 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_( a__ ):
pass
class snake_case_:
def __init__( self : Any , UpperCamelCase_ : Any ):
lowerCAmelCase : Any = data
lowerCAmelCa... | 637 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_( a__ ):
... | 703 |
"""simple docstring"""
from torch import nn
class snake_case_( nn.Module ):
def __init__( self : int , UpperCamelCase_ : int , UpperCamelCase_ : int ):
super().__init__()
lowerCAmelCase : str = class_size
lowerC... | 637 | 0 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : Dict ):
for param in module.parameters():
lowerCAmelCase : Tuple = False
def _snake_case ( ):
lower... | 704 |
"""simple docstring"""
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : str ):
lowerCAmelCase : Dict = val
lowerCAmelCase : str = None
lowerCAmelCase : Dict = None
def ... | 637 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Union[str, Any] = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskForme... | 705 |
"""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
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__... | 637 | 0 |
"""simple docstring"""
from math import factorial
def _snake_case ( _snake_case : int = 20 ):
lowerCAmelCase : Dict = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase : Union[str, Any... | 706 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteri... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : Dict , _snake_case : Optional[int] , _snake_case : Optional[Any] ):
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not alre... | 707 |
"""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
@datacla... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : str ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 708 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_comm... | 637 | 0 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won'... | 709 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 637 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tr... | 710 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 637 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transfor... | 711 |
"""simple docstring"""
snake_case__ : List[Any] = '''Tobias Carryer'''
from time import time
class snake_case_:
def __init__( self : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int... | 637 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.util... | 712 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 637 | 0 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 713 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : List[Any]=None ):
lowerCAmelCase : Any = (path or []) + [u]
for ... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10 ):
if not isinstance(a__ , a__ ) or n < 0:
raise ValueError('''Invalid input''' )
lowerCAmelCase : str = 10**n
lowerCAmelCase : Any = 28433 * (pow(2 , 7830457... | 714 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 637 | 0 |
"""simple docstring"""
from math import ceil, sqrt
def _snake_case ( _snake_case : int = 1000000 ):
lowerCAmelCase : Dict = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerCAmelCase : Lis... | 715 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 0 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : Tuple ) -> Any:
return 1 / (1 + np.exp(-vector ))
def _snake_case ( _snake_case : Union[str, Any] ) -> List[Any]:
return vector * sigmoid(1.702 * vector )
if __... | 716 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequ... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .te... | 718 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : int = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if n... | 719 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case__ : Optional[Any] = namedtuple(
''... | 637 | 0 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
snake_case__ : Any = '__DUMMY_TRANSFORMERS_USER__'
snake_case__ : Tuple = 'Dummy User'
snake_case__ ... | 720 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return base * power(_snake_case , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : Un... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_:
def __init__( self : Any , UpperCamelCase_ : str ):
lowerCAmelCase : Optional[Any] = num_of_nodes
lowerCAmelCase : Optional[Any] = ... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 637 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _snake_case ( _snake_case : str , _snake_case : Union[str, Any] , _snake_case : Union[str... | 700 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 637 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class snake_case_( snake_case_ ):
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase_ : Tuple ):
with open(UpperCamelCase_ , enc... | 701 |
"""simple docstring"""
from __future__ import annotations
class snake_case_:
def __init__( self : int , UpperCamelCase_ : str , UpperCamelCase_ : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = text, pattern
lowerCAmelCas... | 637 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case__ : Optional[int] = logging.get_logger(__name__)
snake_ca... | 702 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_( a__ ):
pass
class snake_case_:
def __init__( self : Any , UpperCamelCase_ : Any ):
lowerCAmelCase : Any = data
lowerCAmelCa... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : List[Any] = 1 , _snake_case : Optional[int] = 1000 ):
lowerCAmelCase : List[Any] = 1
lowerCAmelCase : Optional[Any] = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE_ , digit + 1 ):... | 703 |
"""simple docstring"""
from torch import nn
class snake_case_( nn.Module ):
def __init__( self : int , UpperCamelCase_ : int , UpperCamelCase_ : int ):
super().__init__()
lowerCAmelCase : str = class_size
lowerC... | 637 | 0 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case_( tf.keras.optimizers.schedules.LearningRateSched... | 704 |
"""simple docstring"""
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : str ):
lowerCAmelCase : Dict = val
lowerCAmelCase : str = None
lowerCAmelCase : Dict = None
def ... | 637 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : str = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/con... | 705 |
"""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
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__... | 637 | 0 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
snake_case__ : Optional[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 m... | 706 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteri... | 637 | 0 |
"""simple docstring"""
def _snake_case ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowerCAmelCase_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solu... | 707 |
"""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
@datacla... | 637 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline... | 708 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_comm... | 637 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class snake_case_( unittest.TestCa... | 709 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 637 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[Any] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():... | 710 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 637 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.... | 711 |
"""simple docstring"""
snake_case__ : List[Any] = '''Tobias Carryer'''
from time import time
class snake_case_:
def __init__( self : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int... | 637 | 0 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
snake_case__ : Optional[int] = '''\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popo... | 712 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class snake_case_( lowercase_ ):
__UpperCamelCase = 42
__UpperCamelCase = 42
def _snake_case ( _snake_case : Dict ):
if not isinstance(lowerCamelCase_ , lo... | 713 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : List[Any]=None ):
lowerCAmelCase : Any = (path or []) + [u]
for ... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
lowerCAmelCase : int = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 714 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 637 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : int = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config... | 715 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 716 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import clas... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : Optional[Any] = 8 ):
lowerCAmelCase : Tuple = ascii_letters + digits + punctuation
... | 718 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( _snake_case : int , _snake_case : Dict ):
lowerCAmelCase : List[Any] = f'''{sampling_rate}'''
lowerCAmelCase : Union[str, Any] ... | 719 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case__ : Optional[Any] = namedtuple(
''... | 637 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import clas... | 720 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return base * power(_snake_case , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : Un... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterM... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
lowerCAmelCase : List[Any] = [True] * (num + 1)
lowerCAmelCase : Optional[int] = 2
while p *... | 700 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 637 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineT... | 701 |
"""simple docstring"""
from __future__ import annotations
class snake_case_:
def __init__( self : int , UpperCamelCase_ : str , UpperCamelCase_ : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = text, pattern
lowerCAmelCas... | 637 | 0 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case_( lowerCamelCase__ ):
def __init__( self : Optional[Any] , UpperCamelCase_ : int="" , UpperCamelCase_ : List[Any]="train" ):
... | 702 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case_( a__ ):
pass
class snake_case_:
def __init__( self : Any , UpperCamelCase_ : Any ):
lowerCAmelCase : Any = data
lowerCAmelCa... | 637 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokeniz... | 703 |
"""simple docstring"""
from torch import nn
class snake_case_( nn.Module ):
def __init__( self : int , UpperCamelCase_ : int , UpperCamelCase_ : int ):
super().__init__()
lowerCAmelCase : str = class_size
lowerC... | 637 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 704 |
"""simple docstring"""
class snake_case_:
def __init__( self : Union[str, Any] , UpperCamelCase_ : str ):
lowerCAmelCase : Dict = val
lowerCAmelCase : str = None
lowerCAmelCase : Dict = None
def ... | 637 | 0 |
"""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 snake_case_( enu... | 705 |
"""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
snake_case__ : Tuple = logging.get_logger(__name__)
snake_case__... | 637 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_avai... | 706 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteri... | 637 | 0 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 707 |
"""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
@datacla... | 637 | 0 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precis... | 708 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_comm... | 637 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
Vi... | 709 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 637 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _snake_case ... | 710 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
snake_case__ : Optional[int] = TypeVar('''KT''')
snake_case__ : str = TypeVar('''VT''')
class snake_case_( Generic[KT, VT] ):
def __init... | 711 |
"""simple docstring"""
snake_case__ : List[Any] = '''Tobias Carryer'''
from time import time
class snake_case_:
def __init__( self : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int... | 637 | 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 _snake_case ( _snake_case : Any , _snake_... | 712 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 637 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case_( _UpperCamelCase ):
__UpperCamelCase = '''WhisperFeatureExtractor'''
__UpperCamelCase = '''WhisperTokenizer'''
def __init__( self : List[str] , UpperCamelCase_ : L... | 713 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : str , _snake_case : List[Any]=None ):
lowerCAmelCase : Any = (path or []) + [u]
for ... | 637 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _snake_case ( _snake_case : Optional[int] , _snake_case : Union[str, Any]=() , ... | 714 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : float , _snake_case : float ):
return round(float(moles / volume ) * nfactor )
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case :... | 715 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_c... | 716 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 0 |
"""simple docstring"""
from math import factorial
def _snake_case ( _snake_case : str = 20 ):
lowerCAmelCase : Optional[Any] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase : Optional[Any] ... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermark... | 718 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTe... | 719 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case__ : Optional[Any] = namedtuple(
''... | 637 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case__ : Any = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxC... | 720 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return base * power(_snake_case , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : Un... | 637 | 0 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgum... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 637 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case_( a__ ):
__UpperCamelCase = ['''image_processor''', '''feature_extractor''']
__UpperCamelCase = '''TvltImageProcessor'''
__UpperCamelCase = '''TvltFeatureExtractor'''
def ... | 700 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 637 | 0 |
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_common import ModelTesterMixin, ids_tensor, ... | 701 |
"""simple docstring"""
from __future__ import annotations
class snake_case_:
def __init__( self : int , UpperCamelCase_ : str , UpperCamelCase_ : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = text, pattern
lowerCAmelCas... | 637 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.