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 |
|---|---|---|---|---|
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
import numpy as np
_snake_case : str = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class a :
"""simple docstring"""
def __init__( self : Opti... | 81 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
... | 82 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
"""simple docstring"""
# Copyright 2021 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/LICENS... | 83 |
'''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 acceler... | 640 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
UpperCAmelCase = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_to... | 84 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester... | 85 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
from __future__ import annotations
class _a :
"""simple docstring"""
def __init__( self : Dict , UpperCAmelCase : int ):
A_ = order
# a_{0} ... a_{k}
A_ = [1.0] + [0.0] * order
# b_{... | 86 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessing... | 87 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
"""simple docstring"""
_lowerCamelCase : Any = HfArgumentParser(__snake_case )
_lowerCamelCase : int = parser.pa... | 88 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
import requests
SCREAMING_SNAKE_CASE : str = "" # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE : Any = "https://api.openweathermap.org/data/2.5/"
def UpperCamelCase_( lowerCamelCase_ = "Chicago" , lowerCamelCase_ = APPID ) -> dict:
return requ... | 89 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class a__ ( a__ ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self , lower... | 90 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTeste... | 91 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffus... | 92 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__A = TypeVar("""KT""")
__A = TypeVar("""VT""")
class _lowerCAmelCase ( Generic[KT, VT] ):
"""simple docstring"""
def __init__(... | 93 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_doc... | 94 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/b... | 95 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
"""simple docstring"""
import math
def a ( __UpperCAmelCase : int ) -> 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... | 96 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _lowercase ( SCREAMING_SNAKE_CASE_ : ArgumentParser ) -> Optional[int]:
raise NotImplement... | 97 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowercase__ : Union[str, Any] = 0
lowercase__ : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are ob... | 98 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 99 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import P... | 100 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 0 |
def a__ ( A__, A__ ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_00, 0.2_5) = }""")
print(F"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")
| 101 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
"""simple docstring"""
import requests
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : List[Any] = {"""Content-Type""": """application/json"""}
UpperCamelCase : str = requests.post(SCREAMING_SNAKE_CASE... | 102 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Ten... | 103 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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 ... | 104 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake_case_... | 105 |
'''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 acceler... | 640 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 106 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : int , __snake_case : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 107 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
import argparse
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 accelerate import Accelerator, Distribu... | 108 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ren... | 109 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
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
__magic_name__ =namedtuple(
'''_TestCommandArgs''',
[
'''data... | 415 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
a : str = ... | 273 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A ( _SCREAMING_SNAKE_CASE ) -> Dict:
lowerCamelCase : Any = os.path.join(args.tf_model_dir ,"parameters.json" )
... | 311 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse... | 398 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 0 |
def UpperCamelCase_ ( lowerCAmelCase__ = 2_00 ):
"""simple docstring"""
_lowerCAmelCase : Optional[Any] = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
_lowerCAmelCase : Optional[int] = [0] * (pence + 1)
_lowerCAmelCase : Optional[Any] = 1 # base case: 1 ... | 424 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
"""simple docstring"""
from PIL import Image
def A_ (__a , __a ):
'''simple docstring'''
def brightness(__a ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white... | 115 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
A__ : List[Any] = 'docs/source/en/_toctree.yml'
def a_ ( _UpperCAmelCase : Optional[int] ) -> str:
__snake_case : List[Any] = defaultdict(__UpperCAmelCase... | 286 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( _lowerCamelCase ):
'''simple docstring'''
lowerCamelCase_ : int = ["""image_processor""", """tokenizer"""]
lowerCamelCase_ : int ... | 520 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokeniz... | 441 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCAmelCase ( lowerCa... | 105 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Tuple ):
... | 447 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
def __UpperCamelCase ( A ):
if n_term == "":
return []
UpperCamelCase__ = []
for temp in range(int(__UpperCAmelCase ) ):
series.append(f"1/{temp + 1}" if series else '''1''' )
return series
if __name__ == "__main__":
__magic_name... | 415 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
"""simple docstring"""
def __magic_name__ ( UpperCamelCase : Tuple , UpperCamelCase : Optional[int] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Tuple ) -> Optional[Any]:
a__ = [False] * len(__UpperCAmelCase )
a__ = []
qu... | 273 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 0 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import P... | 311 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
import json
import sys
def __lowerCAmelCase ( _A ,_A ):
"""simple docstring"""
with open(__UpperCAmelCase ,encoding="""utf-8""" ) as f:
_lowercase = json.load(__UpperCAmelCase )
_lowercase = ["""<details>""", """<summary>Show... | 398 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resol... | 424 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : Tuple = logging.get_logger(__name__)
UpperCamelCase_ : Optional[Any] = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transform... | 115 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
A__ : str = logging.getLogger(__name__)
A__ : Optional[int] = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/res... | 286 |
'''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 acceler... | 640 | 0 |
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase ) -> int:
def update_area_of_max_square(_lowercase , _lowercase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__A : Tuple = update_area_of_max_squa... | 520 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
def __snake_case ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
def get_matched_characters(__magic_name__ , __magic_name__ ) -> str:
lowercase = []
lowercase = min(len(_stra ) , len(_stra ) ... | 441 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __UpperCAmelCa... | 105 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.mod... | 447 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ =[
'word_embeddings_layernorm.weight... | 415 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
"""simple docstring"""
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class lowercase:
def __init__( self ) -> Tuple:
"""simple docstring"""
a__ = []
a__ = set()
def lowercase__ ( ... | 273 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar('KEY')
SCREAMING_SNAKE_CASE__ : str = TypeVar('VAL')
@dataclass(frozen=_lowerCamelCase , slots=_lowerCamelCas... | 311 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
import heapq
def __lowerCAmelCase ( _A ):
"""simple docstring"""
_lowercase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with ... | 398 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 0 |
import math
import tensorflow as tf
from packaging import version
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
_lowerCAmelCase : Dict = tf.convert_to_tensor(__UpperCAmelCase )
_lowerCAmelCase : Tuple = 0.5 * (1.0 + tf.math.erf(x / tf... | 424 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ : List[str] = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
im... | 115 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class snake_case__ ( tf.keras.layers.Layer ):
def __init__( self : Dict , __a : int , __a : List[str] , __a : int , __a : ... | 286 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_comm... | 520 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
def __snake_case ( ):
'''simple docstring'''
lowercase = 0
for i in range(1 , 1001 ):
total += i**i
return str(__UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 441 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase__ : Optional[Any] = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that gene... | 105 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
_a : List[Any] = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_a : int = BASE_URL + '/user'
# ... | 447 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A ( _lowerCamelCase ):
@staticmethod
@abstractmethod
def _a (SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
raise NotImplementedError()
... | 415 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( UpperCamelCase : List[str] ) -> str:
if "img_encoder.pos_embed" in name:
a__ = name.re... | 273 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 311 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
def __lowerCAmelCase ( _A ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
_lowercase = sorted(string.lower() )
return len(__UpperCAmel... | 398 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils impo... | 424 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizer... | 115 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Union[str, Any] ) -> list:
if len(__UpperCAmelCase ) != 2 or len(a[0] ) != 2 or len(__UpperCAmelCase ) != 2 or len(b[0] ) != 2:
... | 286 |
'''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 acceler... | 640 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Spl... | 520 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCamelCase_ ( ... | 441 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from... | 105 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvisio... | 447 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler... | 415 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import D... | 273 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def A ( _SCREAMING_SNAKE_CASE=None ) -> List[Any]:
... | 311 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 398 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_... | 424 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ) -> None:
'''simple docstring'''
assert and_... | 640 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformer... | 115 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self : List[str] , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Dict , lowercase_ : int=None , ... | 640 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> bool:
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 286 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCamelCase )
class _a ( _lowerCamelCase ):
'''simple docstring'''
lowerCamelCase_ : int = field(defau... | 520 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image:
'''simple docstring'''
def brightness(__UpperCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
... | 640 | 0 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KE... | 441 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision... | 640 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ : str = {'processing_layoutxlm': ['LayoutXLMProcessor']}
try... | 105 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowercase_ :
'''simple docstring'''
def __init__( self , a_ = 6 ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = None
UpperCAmelCase = None
... | 447 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__Upp... | 640 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class _A ( unittest.TestCase ):
def _a (self ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ = [10, 20, 30, 40, 50, 60]
UpperCamelCase__ ... | 415 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...mo... | 273 |
'''simple docstring'''
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case_ ,snake_case_ = 0, 1
while True:
snake_case_ ,snake_case_ = b, a + b
y... | 640 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : O... | 311 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __na... | 640 | 0 |
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 BartForConditionalGeneration, BartTokenizer
... | 398 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class __A ( _lowerCamelCase ):
'''s... | 424 |
'''simple docstring'''
import operator as op
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda __UpperCAmelCase, __UpperCAmelCase : int(x / y ) # noqa: E731 integer divisi... | 640 | 0 |
"""simple docstring"""
import argparse
import struct
import unittest
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , _snake_case : bytes ) -> List[str]:
"""simple docstring"""
A_ = data
... | 115 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 640 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A__ : Optional[int] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-tra... | 286 |
'''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 acceler... | 640 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCamelCase = _sy... | 520 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
from __future__ import annotations
def __snake_case ( __magic_name__ , __magic_name__ = None , __magic_name__ = None ):
'''simple docstring'''
if start is None:
lowercase = 0
if end is None:
lowercase = len(__UpperCA... | 441 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 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, PriorTransfor... | 105 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 640 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Optional[int] ):
UpperCAmelCase , UpperCAmelCase = np.shape(__UpperCAmelCase )
if rows != columns:
UpperCAmelCase = (
'\'table\'... | 447 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 640 | 0 |
def __UpperCamelCase ( A , A ):
return int((input_a, input_a).count(0 ) == 0 )
def __UpperCamelCase ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , ... | 415 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = [False] * len(__UpperCAmelCase )
snake_case_ = []
queue.append... | 640 | 0 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowercase:
def __init__( self , __SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
a__ = list_of_points
# Degree ... | 273 |
'''simple docstring'''
import heapq
import sys
import numpy as np
a : Dict = tuple[int, int]
class a :
def __init__( self : Dict ):
snake_case_ = []
snake_case_ = set()
def A_ ( self : in... | 640 | 0 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE__ : List[str] = 1000003
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> bool:
lowerCamelCase : Optional[int] = len(__UpperCAmelCase )
... | 311 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 0 |
from math import isclose, sqrt
def __lowerCAmelCase ( _A ,_A ,_A ):
"""simple docstring"""
_lowercase = point_y / 4 / point_x
_lowercase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
_lowercase = (1 - normal_gradient *... | 398 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 640 | 0 |
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