code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Traini... | 631 |
'''simple docstring'''
_A : List[str] ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_di... | 631 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _lowercase :
@proper... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : Dict ={
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-h... | 631 | 1 |
'''simple docstring'''
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> float:
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
... | 631 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# ... | 631 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_A : Union[str, Any] ='''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
_A : ... | 631 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _lowercase ( _lowercase ):
def __init__( self: Optional[Any] , UpperCamelCase__: ... | 631 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : List[str] =logging.get_logger(__name__)
_A : str ={
... | 631 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 631 | 1 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_A : Any ={
'''configuration_trocr''': ['''TRO... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowerCamelCase__ : Tuple = str(bin(UpperCamelCase ... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[str] ={
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.c... | 631 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, tor... | 631 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_A : List[str] ='''examples/'''
_A : Any ={
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
... | 631 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import... | 631 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_A : Union[str, Any] =False
class _lowercase ( ... | 631 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = 1000000 ) -> int:
lowerCamelCase__ : Optional[int] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**... | 631 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
lowerCamelCase__ : Optional[int] = 0
# ... | 631 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : List[str] =logging.get_logger(__name__)
_A : int ={
... | 631 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_sec... | 631 |
'''simple docstring'''
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_t... | 631 | 1 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> Optional[int]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCa... | 631 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowercase ( _lowercase ):
a = """"""
a = (
None ... | 631 | 1 |
'''simple docstring'''
class _lowercase :
def __init__( self: Any , UpperCamelCase__: list ):
lowerCamelCase__ : Any = set_counts
lowerCamelCase__ : int = max(UpperCamelCase__ )
... | 631 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaCon... | 631 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
i... | 631 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vis... | 631 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_A : List[str] ={'''configuration_dpt''': ['''DPT_PRETRAINE... | 631 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.convers... | 631 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : int =logging.get_logger(__name__)
class _lowercase ( _lowercase ):
def __init__( self: Optional... | 631 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
... | 631 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
... | 631 |
'''simple docstring'''
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_sen... | 631 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_A : List[Any] =(3, 9, -11, 0, 7, 5, 1, -1)
_A : int =(4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/res... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list:
lowerCamelCase__ : str = int(UpperCamelCase )
if n_element < 1:
lowerCamelCase__ : int = ValueError("""a should be a positive number""" )
... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/... | 631 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : List[str] =logging.get_logger(__name__)
_A : str ={
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/mai... | 631 |
'''simple docstring'''
import sys
import turtle
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , Upper... | 631 | 1 |
'''simple docstring'''
_A : Optional[int] ='''Input must be a string of 8 numbers plus letter'''
_A : Tuple ='''TRWAGMYFPDXBNJZSQVHLCKE'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> bool:
if not isinstance(UpperCamelCase , Upp... | 631 |
'''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 ...tes... | 631 | 1 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[Any]:
lowerCamelCase__ : List[Any] = 0
... | 631 |
'''simple docstring'''
_A : List[str] ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_di... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list:
if len(UpperCamelCase ) < 2:
return collection
def circle_sort_util(UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> bool:
lowerCa... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : Dict ={
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-h... | 631 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_A : Tuple ={
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''... | 631 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# ... | 631 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A : List[str] ={
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MA... | 631 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _lowercase ( _lowercase ):
def __init__( self: Optional[Any] , UpperCamelCase__: ... | 631 | 1 |
'''simple docstring'''
import requests
_A : Dict ='''''' # <-- Put your OpenWeatherMap appid here!
_A : str ='''https://api.openweathermap.org/data/2.5/'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = "Chicago" , UpperCamelCase = APPID ) ... | 631 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 631 | 1 |
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE_ () -> Tuple:
with open(os.path.dirname(UpperCamelCase ) + """/p022_names.txt""" ) as file:
lowerCamelCase__ : Optional[Any] = str(file.readlines()[0] )
lowerC... | 631 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_A : Any ={
'''configuration_trocr''': ['''TRO... | 631 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_A : List[str] =importlib.util.find_spec('''s3fs''') i... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[str] ={
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.c... | 631 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_A : Any =logging.get_logger(__name__)
class _lowercase ( _lowercase ):
def __init__( self: Dict , ... | 631 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_A : List[str] ='''examples/'''
_A : Any ={
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
... | 631 | 1 |
'''simple docstring'''
import qiskit
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> qiskit.result.counts.Counts:
lowerCamelCase__ : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum... | 631 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_A : Union[str, Any] =False
class _lowercase ( ... | 631 | 1 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFo... | 631 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
lowerCamelCase__ : Optional[int] = 0
# ... | 631 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
_A : List[str] =logging.get_logger(__name__) # pylint: di... | 631 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_A : str =(720, 1_280) # Height, Width
_A : List[Any] =(0.4, 0.6) # if height or width lower than this scale,... | 631 |
'''simple docstring'''
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_t... | 631 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( _lowercase ):
a = (PNDMScheduler,)
a = (("""num_inference_steps""", 50),)
... | 631 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowercase ( _lowercase ):
a = """"""
a = (
None ... | 631 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 631 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaCon... | 631 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 631 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vis... | 631 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[int] =logging.get_logger(__name__)
_A : List[Any] ={
'''microsoft/xprophetnet-large-wiki1... | 631 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.convers... | 631 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class _lowercase :
a = 42 # [batch_size x 3]
a = 42 # [batch_size x 3]
a = 42 # [batch_size x 3]
... | 631 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
... | 631 | 1 |
'''simple docstring'''
import unittest
from transformers import 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_... | 631 |
'''simple docstring'''
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_sen... | 631 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_A : Optional[int] =10
def SCREAMING_SNAKE_CASE_ (UpperCame... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/res... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> int:
lowerCamelCase__ : int = [1]
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = 0, 0, 0
lowerCamelCase__ : Optional[int] ... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Dict:
# Return True if there is node that has not iterated.
lowerCamelCase__ : Dict = [False] * len(UpperC... | 631 |
'''simple docstring'''
import sys
import turtle
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , Upper... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = 10 , UpperCamelCase = 1000 , UpperCamelCase = True ) -> int:
assert (
isinstance(UpperCamelCase , UpperCamelCase )
and isinstance(UpperCamelCase , ... | 631 |
'''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 ...tes... | 631 | 1 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_A : int =datasets.logging.get_logger(__name__)
_A : Any ='''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust M... | 631 |
'''simple docstring'''
_A : List[str] ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_di... | 631 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[int] =logging.get_logger(__name__)
_A : Tuple ={
'''microsoft/git-base''': '''https:/... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : Dict ={
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-h... | 631 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_A : Optional[int] =logging.get_logger(__name__)
_A : T... | 631 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# ... | 631 | 1 |
'''simple docstring'''
from math import factorial, pi
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase = 30 ) -> float:
if not isinstance(UpperCamelCase , (int, float) ):
raise ValueError("""maclaurin_sin() requires e... | 631 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _lowercase ( _lowercase ):
def __init__( self: Optional[Any] , UpperCamelCase__: ... | 631 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly chec... | 700 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 631 | 0 |
'''simple docstring'''
from PIL import Image
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Union[str, Any]:
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = image.size
lowerCamelCase__ : int = 0
lowerCamelCase__... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_A : Any ={
'''configuration_trocr''': ['''TRO... | 631 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Union[str, Any] ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[str] ={
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.c... | 631 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffus... | 703 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_A : List[str] ='''examples/'''
_A : Any ={
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
... | 631 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> np.ndarray:
lowerCamelCase__ ... | 704 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_A : Union[str, Any] =False
class _lowercase ( ... | 631 | 0 |
'''simple docstring'''
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_pro... | 705 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
lowerCamelCase__ : Optional[int] = 0
# ... | 631 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> set:
lowerCamelCase__ : List[str] = set()
# edges = list of graph's edges
lowerCamelCase__ : Optional[int] = get_edges(lowerCamelCase_ )
# While there ... | 706 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_A : Tuple ={
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 707 |
'''simple docstring'''
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_t... | 631 | 0 |
'''simple docstring'''
class _lowercase :
def __init__( self: Dict , UpperCamelCase__: Any ):
lowerCamelCase__ : str = val
lowerCamelCase__ : Tuple = None
lowerCamelCase__ : List[str] ... | 708 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowercase ( _lowercase ):
a = """"""
a = (
None ... | 631 | 0 |
'''simple docstring'''
from math import factorial
_A : dict[str, int] ={str(digit): factorial(digit) for digit in range(10)}
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Tuple:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
... | 709 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaCon... | 631 | 0 |
'''simple docstring'''
from __future__ import annotations
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... | 710 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vis... | 631 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , Uppe... | 711 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.convers... | 631 | 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_distilbert import DistilBertTokenizer
_A : Optiona... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
... | 631 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
_A : Tuple =(
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_A : Dict =(((515, 22, 13), 555), ((61, 35, 49), 150))
_A : ... | 713 |
'''simple docstring'''
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_sen... | 631 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = "https://www.worldometers.info/coronavirus" ) -> str:
lowerCamelCase__ : List[str] = BeautifulSoup(requests.get(lowerCAmelCase__ ).text... | 714 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/res... | 631 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 715 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/... | 631 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torc... | 716 |
'''simple docstring'''
import sys
import turtle
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , Upper... | 631 | 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 im... | 717 |
'''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 ...tes... | 631 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[str]:
def wrapper(*UpperCa... | 718 |
'''simple docstring'''
_A : List[str] ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_di... | 631 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def SCREAMING_SNAKE_CASE_ (... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : Dict ={
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-h... | 631 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
... | 720 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# ... | 631 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : Union[str, Any] =logging.get_logger(__name__)
_A : Union[s... | 721 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _lowercase ( _lowercase ):
def __init__( self: Optional[Any] , UpperCamelCase__: ... | 631 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : List[str] ={
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/d... | 700 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 631 | 0 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkp... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_A : Any ={
'''configuration_trocr''': ['''TRO... | 631 | 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,
... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[str] ={
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.c... | 631 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils ... | 703 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_A : List[str] ='''examples/'''
_A : Any ={
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
... | 631 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = 100 ) -> str:
lowerCamelCase__ : Dict = n * (n + 1) * (2 * n + 1) / 6
lowerCamelCase__ : Dict = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
... | 704 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_A : Union[str, Any] =False
class _lowercase ( ... | 631 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
# Checks if the entire collection has been sorted
if len(UpperCamelCase ) <= 1 or n <= 1:
ret... | 705 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
lowerCamelCase__ : Optional[int] = 0
# ... | 631 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase = 1000 ) -> List[Any]:
return sum(e for e in range(3 , snake_case__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'{solution() = }') | 706 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A : List[Any] =[
'''good first issue''',
'''feature request''',
'''wip''',
]
def SCREAMING_SNAKE_CASE_ () -> str:
lowerCamelCase__ : Union[str, Any] = Gi... | 707 |
'''simple docstring'''
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_t... | 631 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test... | 708 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowercase ( _lowercase ):
a = """"""
a = (
None ... | 631 | 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
_A : Union[str, Any] =logging.get_logger(_... | 709 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaCon... | 631 | 0 |
'''simple docstring'''
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_sc... | 710 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vis... | 631 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> Tuple:
if len(_snake_case ) < k or k < 0:
raise ValueError("""Invalid Input""" )
lowerCamelCase__ : Dict = sum(array[:k] )
... | 711 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.convers... | 631 | 0 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
_A : Dict =datasets.logging.get_logger(__name__)
_A : List[str] ='\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, ... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
... | 631 | 0 |
'''simple docstring'''
class _lowercase :
def __init__( self: Any ):
lowerCamelCase__ : Dict = """"""
lowerCamelCase__ : Any = """"""
lowerCamelCase__ : List[str] = []
... | 713 |
'''simple docstring'''
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_sen... | 631 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def SCREAMING_SNAKE_CASE_ () -> Optional[int]:
... | 714 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/res... | 631 | 0 |
from math import asin, atan, cos, radians, sin, sqrt, tan
_A : Optional[Any] =637_8137.0
_A : Union[str, Any] =635_6752.31_4245
_A : Dict =6_378_137
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , ... | 715 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/... | 631 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> int:
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE_ () -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 ... | 716 |
'''simple docstring'''
import sys
import turtle
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , Upper... | 631 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Tuple:
def wrapper(*UpperCamelCase ... | 717 |
'''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 ...tes... | 631 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> str:
if len(__lowerCAmelCase ) <= 1:
return lst
lowerCamelCase__ : Optional[int] = 1
while i < len(__lowerCAmelCase ):
if lst[i - 1] <= ls... | 718 |
'''simple docstring'''
_A : List[str] ='''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_di... | 631 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultiple... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A : Dict ={
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-h... | 631 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : Any ={
'huggingface/time-series-transformer-tourism-monthly': (... | 720 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# ... | 631 | 0 |
'''simple docstring'''
_A : Any =0 # The first color of the flag.
_A : Union[str, Any] =1 # The second color of the flag.
_A : List[str] =2 # The third color of the flag.
_A : Optional[int] =(red, white, blue)
def SCREAMING_SN... | 721 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _lowercase ( _lowercase ):
def __init__( self: Optional[Any] , UpperCamelCase__: ... | 631 | 0 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative... | 700 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 631 | 0 |
'''simple docstring'''
import torch
def SCREAMING_SNAKE_CASE_ () -> Dict:
if torch.cuda.is_available():
lowerCamelCase__ : List[str] = torch.cuda.device_count()
else:
lowerCamelCase__ : int = 0
... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_A : Any ={
'''configuration_trocr''': ['''TRO... | 631 | 0 |
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> List[str]:
return x if y == 0 else greatest_common_divisor(lowercase__ , x % y )
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> int:
return (x * y) // gr... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[str] ={
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.c... | 631 | 0 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMas... | 703 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_A : List[str] ='''examples/'''
_A : Any ={
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
... | 631 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Op... | 704 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_A : Union[str, Any] =False
class _lowercase ( ... | 631 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[str]:
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase__ : int = 4
lower... | 705 |
'''simple docstring'''
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> list:
lowerCamelCase__ : Optional[int] = 0
# ... | 631 | 0 |
'''simple docstring'''
import operator
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase = False , UpperCamelCase = None ) -> Any:
lowerCamelCase__ : Optional[int] = operator.lt if reverse else operator.gt
lowerCamel... | 706 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 | 0 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDat... | 707 |
'''simple docstring'''
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_t... | 631 | 0 |
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