code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
__snake_case :int
__snake_case :int
class __UpperCamelCase :
def __init__( self : Any ... | 80 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCAmelCase_ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , ... | 81 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order speci... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCamelCase = numpy.array([0, 0])
lowerCamelCase = numpy.array([0.5, 0.8_660_254])
lowerCamelCase = numpy.array([1, 0... | 82 |
'''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,
ViltForImagesAn... | 90 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
... | 83 |
'''simple docstring'''
import re
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if _... | 90 | 0 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 84 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase... | 90 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[str] = TypeVar("KT")
SCREAMING_SNAKE_CASE__ : str = TypeVar("VT")
class snake_case ( Generic[KT, VT] ):
def __init__( ... | 85 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines... | 90 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__a :Any = ''
__a :int = ''
__a :str = ''
__a :Optional[Any] = 1 # (0 is vertical, 1 is horizontal)
def __snake_case ( ):
"""simple docstring"""
... | 86 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
return x if y == 0 else greatest_common_divisor(A , x % y )
def _snake_case ( A , A ) -> int:
return (x * y) // greatest_common_divisor(A , A )
def ... | 90 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determ... | 87 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 90 | 0 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def _snake_case ( __snake_case : Optional[int] ):
"""simple docstring"""
_lowerCamelCase , _lowerCamelCase : Any = img.shape[0], img.shape[1]
# converting e... | 88 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
... | 90 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigT... | 89 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0... | 90 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_lowercase = logging.get_logger(__name__)
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : List[str] ,*A_ ... | 91 |
'''simple docstring'''
def _snake_case ( A ) -> int:
if n == 1 or not isinstance(A , A ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase__ = [0, 1]
for i in range(2 , n... | 90 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _lowerCAmelCase ( __ma... | 92 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def _snake_case ( A ) -> int:
return choice(A )
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = random_pivot(A )
# partition... | 90 | 0 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_check... | 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig... | 90 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',... | 94 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('''KEY''')
__UpperCAmelCase = TypeVar('''VAL''')
@dataclass(frozen=a__ , ... | 90 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 95 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = OmegaConf.load(A )
... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class __A :
def __init__( self : Optional[Any] , __snake_case : int ) -> None:
__magic_name__: Optional[int] = size
# approximate the... | 96 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__U... | 90 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_t... | 97 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
... | 90 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
l... | 98 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = str(A )
return len(A ) == 9 and set(A ) == set('''123456789''' )
def _snake_case ( ) -> int | None:
for base_num in ra... | 90 | 0 |
SCREAMING_SNAKE_CASE = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_l... | 99 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly ... | 90 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 100 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _snake_case ( ) -> Union[str, Any]:
raise RuntimeError('''CUDA out... | 90 | 0 |
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : Dict = len(A__ ) + 1
SCREAMING_SNAKE_CASE_ : List[str] = len(A__ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of len... | 101 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_mo... | 90 | 0 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 102 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _snake_case ( A , A , A , A , A ) -> np.array:
lowerCAmelCase__ = int(np.ceil((x_end - xa) / step_size ) )
lowerCAmelCase__ ... | 90 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ ) -> list[list[float]]:
_snake_case = []
for data in source_data:
for i, el in enumerate(lowerCAmelCase_ ):
if len(lowerCAmelCase_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(fl... | 103 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCamelCase ( UpperCAmelCase_ : Tuple="ro", UpperCAmelCase_ : Any="en", UpperCAmelCase_ : List[str]="wmt16", UpperCAmelCase_ : Dict=None ) -> None:
... | 104 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order speci... | 90 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase_... | 105 |
'''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,
ViltForImagesAn... | 90 | 0 |
__snake_case :str ={
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def lowerCamelCase_ ( lowerCAmelCase__ : float ) -> str... | 106 |
'''simple docstring'''
import re
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if _... | 90 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
_A = [
'encoder.version',
'decoder.version',
'model.encoder.... | 107 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase... | 90 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case = 5_0_0_0_0_0_0_0 ) -> int:
_UpperCAmelCase = set()
_UpperCAmelCase = int((limit - 2_4) ** (1 / 2) )
_UpperCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
... | 108 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines... | 90 | 0 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
fro... | 109 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
return x if y == 0 else greatest_common_divisor(A , x % y )
def _snake_case ( A , A ) -> int:
return (x * y) // greatest_common_divisor(A , A )
def ... | 90 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 90 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"pro... | 68 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
... | 90 | 0 |
'''simple docstring'''
__lowerCAmelCase : Optional[int] ={
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter... | 440 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0... | 90 | 0 |
'''simple docstring'''
def __lowercase (_SCREAMING_SNAKE_CASE :Tuple ):
if length <= 0 or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(_SCREAMING_SN... | 507 |
'''simple docstring'''
def _snake_case ( A ) -> int:
if n == 1 or not isinstance(A , A ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase__ = [0, 1]
for i in range(2 , n... | 90 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( __snake_case :Optional[Any] ) -> Any:
"""simple docstring"""
def is_in_circle(__snake_case :Any , __snake_case :List[str] ) -> bool:
... | 693 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def _snake_case ( A ) -> int:
return choice(A )
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = random_pivot(A )
# partition... | 90 | 0 |
'''simple docstring'''
import os
import string
import sys
UpperCAmelCase = 1 << 8
UpperCAmelCase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''lef... | 119 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig... | 90 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_ext... | 262 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('''KEY''')
__UpperCAmelCase = TypeVar('''VAL''')
@dataclass(frozen=a__ , ... | 90 | 0 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=Fals... | 673 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = OmegaConf.load(A )
... | 90 | 0 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCamelCase ( snake_case__ : Tuple ,snake_case__ : Optional[int] ,snake_case__ : Union[str, Any] = False ):
'''simple doc... | 455 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__U... | 90 | 0 |
import itertools
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 ar... | 562 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
... | 90 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( a__ , unittest.Te... | 480 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = str(A )
return len(A ) == 9 and set(A ) == set('''123456789''' )
def _snake_case ( ) -> int | None:
for base_num in ra... | 90 | 0 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowercase ( UpperCamelCase : Union[str, Any] ... | 656 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly ... | 90 | 0 |
def lowercase__ ( A_: Tuple , A_: Dict ) -> int:
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(A_ , x % y )
def lowercase__ ( A_: Dict , A_: str ) -> int:
"""simple docstrin... | 68 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _snake_case ( ) -> Union[str, Any]:
raise RuntimeError('''CUDA out... | 90 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
__lowerCAmelCase : Optional[int] =(
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__lowerCAmelCase : int =(((515, 22, 13), 555), ((61, 35, 49)... | 440 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_mo... | 90 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureE... | 507 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _snake_case ( A , A , A , A , A ) -> np.array:
lowerCAmelCase__ = int(np.ceil((x_end - xa) / step_size ) )
lowerCAmelCase__ ... | 90 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_snake_case : Optional[int] ... | 693 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from ... | 119 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order speci... | 90 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp... | 262 |
'''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,
ViltForImagesAn... | 90 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCamelCase_ :
def __init__( self , snake_case__=2 , snake_case__=3 , snake_case__=64 , snake_case__=None ) ... | 673 |
'''simple docstring'''
import re
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if _... | 90 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase__ = collections.namedtuple("""_Datasets... | 455 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase... | 90 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 562 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines... | 90 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a_ = ['''small''', '''medium''', '''large''']
a_ = '''lm_head.decoder.weight'''
a_ = '''lm_head.weight'''
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CAS... | 480 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
return x if y == 0 else greatest_common_divisor(A , x % y )
def _snake_case ( A , A ) -> int:
return (x * y) // greatest_common_divisor(A , A )
def ... | 90 | 0 |
"""simple docstring"""
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
__A : str = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",
"NVFUSER",
... | 656 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 90 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A = logging.getLogger(__name__)
if __name__ == "__main__":
__A = argparse.Argument... | 68 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
... | 90 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : Optional[Any] ={
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_M... | 440 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0... | 90 | 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_convbert import ConvBertTokenizer
snake_case_ = logging.get_logger(__name__)
snake... | 507 |
'''simple docstring'''
def _snake_case ( A ) -> int:
if n == 1 or not isinstance(A , A ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase__ = [0, 1]
for i in range(2 , n... | 90 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _A ( __snake_case :List[str] ) -> str:
"""simple docstring"""
return "".join(sorted(__snake_case ) )
def _A ( __snake_case :Tuple ) -> l... | 693 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def _snake_case ( A ) -> int:
return choice(A )
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = random_pivot(A )
# partition... | 90 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
... | 119 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig... | 90 | 0 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase ( UpperCamelCase__ : Optional[int] = "laptop" ):
"""simple docstring"""
__UpperCAmelCase = f"""https://ww... | 262 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('''KEY''')
__UpperCAmelCase = TypeVar('''VAL''')
@dataclass(frozen=a__ , ... | 90 | 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 UpperCamelCase_ ( ... | 673 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = OmegaConf.load(A )
... | 90 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case__ ( a__):
'''simple docstring'''
def __init__( self , a__="" , a__="train" ) -> Optional[int]:
'''si... | 455 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__U... | 90 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class _UpperCamelCase ( a__ ):
... | 562 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
... | 90 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG... | 480 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = str(A )
return len(A ) == 9 and set(A ) == set('''123456789''' )
def _snake_case ( ) -> int | None:
for base_num in ra... | 90 | 0 |
"""simple docstring"""
def lowercase ( UpperCamelCase : Tuple = 3 , UpperCamelCase : List[Any] = 7 , UpperCamelCase : List[Any] = 1000000 ):
"""simple docstring"""
A__ : Union[str, Any] =0
A__ : int =1
for current_denomi... | 656 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly ... | 90 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransf... | 68 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _snake_case ( ) -> Union[str, Any]:
raise RuntimeError('''CUDA out... | 90 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
fr... | 440 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_mo... | 90 | 0 |
'''simple docstring'''
from math import isqrt
def __lowercase (_SCREAMING_SNAKE_CASE :Tuple ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(_SCREAMING_SNAKE_CASE ) + 1 ) )
def __lowercase (_SCREAMING_SNAKE_CASE :List[str] = 10**6 )... | 507 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _snake_case ( A , A , A , A , A ) -> np.array:
lowerCAmelCase__ = int(np.ceil((x_end - xa) / step_size ) )
lowerCAmelCase__ ... | 90 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_... | 693 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {'''vocab_file''': '''sent... | 119 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order speci... | 90 | 0 |
'''simple docstring'''
__lowerCAmelCase : List[str] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
__lowerCAmelCase : List[str] = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
... | 262 |
'''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,
ViltForImagesAn... | 90 | 0 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ : Union[str, Any] = '''naver-clova-ix/donut-base'''
class UpperCamelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self ) -> Any:
""... | 673 |
'''simple docstring'''
import re
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if _... | 90 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_c... | 455 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase... | 90 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (... | 562 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines... | 90 | 0 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassi... | 480 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
return x if y == 0 else greatest_common_divisor(A , x % y )
def _snake_case ( A , A ) -> int:
return (x * y) // greatest_common_divisor(A , A )
def ... | 90 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
f... | 656 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 90 | 0 |
from __future__ import annotations
def lowercase__ ( A_: List[Any] , A_: int , A_: Dict , A_: int , A_: List[Any] , ) -> None:
"""simple docstring"""
__UpperCAmelCase =len(A_ )
# If row is equal... | 68 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
... | 90 | 0 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, 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_mod... | 440 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0... | 90 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 507 |
'''simple docstring'''
def _snake_case ( A ) -> int:
if n == 1 or not isinstance(A , A ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase__ = [0, 1]
for i in range(2 , n... | 90 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _A ( ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = HfArgumentParser(__snake_case )
__SCREAMING_SNAKE_CASE = parser.parse_args_into_datacl... | 693 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def _snake_case ( A ) -> int:
return choice(A )
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = random_pivot(A )
# partition... | 90 | 0 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCAmelCase ( a__ ):
def __init__( self : Tu... | 119 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig... | 90 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartF... | 262 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('''KEY''')
__UpperCAmelCase = TypeVar('''VAL''')
@dataclass(frozen=a__ , ... | 90 | 0 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class UpperCamelCase_ ... | 673 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _snake_case ( A , A , A ) -> Union[str, Any]:
lowerCAmelCase__ = OmegaConf.load(A )
... | 90 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 455 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__U... | 90 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatu... | 562 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCAmelCase = False
... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 480 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = str(A )
return len(A ) == 9 and set(A ) == set('''123456789''' )
def _snake_case ( ) -> int | None:
for base_num in ra... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
__A : List[Any] = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked conte... | 656 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly ... | 90 | 0 |
import numpy as np
class _A :
"""simple docstring"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Di... | 68 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _snake_case ( ) -> Union[str, Any]:
raise RuntimeError('''CUDA out... | 90 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 440 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_mo... | 90 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_... | 507 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _snake_case ( A , A , A , A , A ) -> np.array:
lowerCAmelCase__ = int(np.ceil((x_end - xa) / step_size ) )
lowerCAmelCase__ ... | 90 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case : Optional[Any] = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokeniz... | 693 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=... | 90 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 119 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order speci... | 90 | 0 |
'''simple docstring'''
import re
def lowerCAmelCase ( UpperCamelCase__ : List[Any] ):
"""simple docstring"""
__UpperCAmelCase = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(UpperCamelCase__ , UpperCamelCase__ ... | 262 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAn... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return len(set(lowerCAmelCase ) ) == len(lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 673 |
'''simple docstring'''
import re
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(A , A ):
return match.string == phone
return False
if _... | 90 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 455 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase... | 90 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class ... | 562 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines... | 90 | 0 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class __lowercase :
"""simple docstring"""
def __init__(self ):
snake_case_ : Dict = [2, 1, 2, -1]
snake_case_ : Tuple = ... | 480 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
return x if y == 0 else greatest_common_divisor(A , x % y )
def _snake_case ( A , A ) -> int:
return (x * y) // greatest_common_divisor(A , A )
def ... | 90 | 0 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __lowerCAmelCase ( a__):
'''simple docstring'''
def __lt__( self : Any , UpperCamelCase__ : L... | 656 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licen... | 90 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny... | 68 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
... | 90 | 0 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : Dict ):
return "".join([hex(_lowerCamelCase )[2:].zfill(2 ).upper() for byte in list(_lowerCamelCase )] )
def UpperCamelCase ( _lowerCamelCase : str ):
# Check data validity, following RFC3548
... | 440 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0... | 90 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 507 |
'''simple docstring'''
def _snake_case ( A ) -> int:
if n == 1 or not isinstance(A , A ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase__ = [0, 1]
for i in range(2 , n... | 90 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_ut... | 693 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def _snake_case ( A ) -> int:
return choice(A )
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = random_pivot(A )
# partition... | 90 | 0 |
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