code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a = 'src/diffusers'
# Matches is_xxx_available()
_a = re.compile(R'is\_([a-z_]*)_avai... | 61 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 243 | 0 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
if n_term == "":
return []
__UpperCamelCase =[]
for temp in range(int(SCREAMING_SNAKE_CASE__ ) ):
series.append(F'1/{temp + 1}' if series else '1' )
return series
if __name__ == "__m... | 62 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
def __init__( self , __UpperCAmelCase) ->Any:
a_ = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same numb... | 243 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCam... | 243 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input... | 243 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQV... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not i... | 243 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODE... | 66 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMod... | 243 | 0 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import dis... | 67 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 243 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a_... | 68 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
def is_in_circle(UpperCAmelCase , UpperCAmelCase ) -> bool:
a_... | 243 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()... | 69 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case ... | 243 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def UpperCamelCase__ ( lowerCAmelCase = 1_50_00_00 ):
"""simple docstring"""
_lowerCAmelCase = defaultdict(lowerCAmelCase )
_lowerCAmelCase ... | 70 |
"""simple docstring"""
import baseaa
def UpperCamelCase ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return baseaa.baadecode(UpperCAmelCase ).dec... | 243 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
A_ ... | 71 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 243 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def snake_case_ ( A_ : Dict, A_ : str, A_ : str, A_ : Path, A_ : str = None, ... | 72 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
__lowerCamelCase : List[Any] = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
__lowerCamelCase : List[Any] = hex_num[0] == '-'
if is_negative:
__lowe... | 73 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from ... | 74 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
a_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelC... | 243 | 0 |
'''simple docstring'''
import math
def a_ ( __snake_case : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 75 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case ( unittest.TestCase ):
a_ : Any = JukeboxTokenizer
a_ : Any = {
"""artist""": """Zac Brown Band""",
""... | 243 | 0 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE : Any = ""
SCREAMING_SNAKE_CASE : Any = ""
# append each character + "|" in new_string for range(0, length... | 76 |
"""simple docstring"""
UpperCamelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase_ = ['a', 'b', 'c', 'd', 'e']
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
... | 243 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available... | 77 |
"""simple docstring"""
import math
import os
import sys
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = ""
try:
with open(UpperCAmelCase , "rb" ) as binary_file:
a_ = binary_file.read()
for dat in data:
a_ ... | 243 | 0 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
... | 78 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(UpperCAmelCa... | 243 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational imp... | 79 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snak... | 243 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowercase_ ( a__ ):
def __init__( self , a , a ):
super().__init__()
se... | 80 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 243 | 0 |
"""simple docstring"""
def _A ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
a =0
while number:
# This way we a... | 81 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
pr... | 243 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 82 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 243 | 0 |
'''simple docstring'''
import math
import sys
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = ''
try:
with open(UpperCAmelCase_ , 'rb' ) as binary_file:
_UpperCamelCase : int = binary_file.read()
... | 83 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 243 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : dict ) -> set:
'''simple docstring'''
lowerCAmelCase_ :Any = set()
# edges = list of graph's edges
lowerCAmelCase_ :List[Any] = get_edges(lowercase__ )
# While there are st... | 84 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
def __init__( self , __UpperCAmelCase) ->Any:
a_ = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same numb... | 243 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _snake_case ( lowercase_ ):
def lowerCAmelCase__ ( self , a__ ) -> Dict:
'''simple docstring'''
with ... | 85 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCam... | 243 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def __lowerCAmelCase ():
from torch.utils.cpp_extension import load
__lowerCAmelCase : Dict = Path(_UpperCamelCase ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
__lowerCAmelCase : int = ... | 86 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input... | 243 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''... | 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not i... | 243 | 0 |
def a__ ( A_, A_ ):
'''simple docstring'''
__magic_name__ = int(A_ )
# Initialize Result
__magic_name__ = []
# Traverse through all denomination
for denomination in reversed(A_ ):
# Find denominations
while int(... | 88 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMod... | 243 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
f... | 89 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 243 | 0 |
__A = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggin... | 90 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
def is_in_circle(UpperCAmelCase , UpperCAmelCase ) -> bool:
a_... | 243 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : Optional[int] = {"""configuration_vit""": ["""VIT_PR... | 91 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case ... | 243 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : int = 1_00 ):
__lowerCAmelCase = (n * (n + 1) // 2) ** 2
__lowerCAmelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 |
"""simple docstring"""
import baseaa
def UpperCamelCase ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return baseaa.baadecode(UpperCAmelCase ).dec... | 243 | 0 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
... | 93 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 243 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Tuple ... | 94 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 0 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _A ( SCREAMING_SNAKE_CASE : str = "laptop" ):
"""simple docstring"""
a__ : Optional[int] =f'''https://www.amazon.in/laptop/s?k={pro... | 95 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 0 |
"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import ... | 96 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
a_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelC... | 243 | 0 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
... | 97 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case ( unittest.TestCase ):
a_ : Any = JukeboxTokenizer
a_ : Any = {
"""artist""": """Zac Brown Band""",
""... | 243 | 0 |
"""simple docstring"""
def a_ ( lowerCamelCase ):
if len(lowerCamelCase ) <= 1:
return [tuple(lowerCamelCase )]
UpperCAmelCase__ = []
def generate(lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = [0] *... | 98 |
"""simple docstring"""
UpperCamelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase_ = ['a', 'b', 'c', 'd', 'e']
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
... | 243 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase : Any = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if not is_to... | 99 |
"""simple docstring"""
import math
import os
import sys
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = ""
try:
with open(UpperCAmelCase , "rb" ) as binary_file:
a_ = binary_file.read()
for dat in data:
a_ ... | 243 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 100 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(UpperCAmelCa... | 243 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowercase__ :Any = pd.read_csv("sample_data.csv", header=None)
lowercase__ :Optional[Any]... | 101 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snak... | 243 | 0 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requ... | 102 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 243 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
... | 103 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
pr... | 243 | 0 |
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = len(A__ )
print('''The following activities are selected:''' )
# The first activity is always selected
__lowercase = 0
print(A__ , end=''',''' )
# Consid... | 104 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 243 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class __UpperCamelCase :
def __init__( self ) -> None:
a : list[Any] = []
a : int = 0
a : int ... | 105 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 243 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils im... | 106 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
def __init__( self , __UpperCAmelCase) ->Any:
a_ = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same numb... | 243 | 0 |
class snake_case__ :
"""simple docstring"""
def __init__( self : Optional[Any] , __lowerCamelCase : int ) -> List[str]:
# we need a list not a string, so do something to change the type
a = arr.split("," )
def _... | 107 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCam... | 243 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a__ ( SCREAMING_SNAKE_CASE : Dataset , SCREAMING_SNAKE_CASE : Dict[st... | 108 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input... | 243 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( UpperCamelCase : Optional[Any] ... | 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not i... | 243 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class A ( unittest.TestCase ):
__snake_case = JukeboxTokenizer
__snake_case = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyri... | 278 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMod... | 243 | 0 |
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
lowercase__ : Optional[Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 87 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 243 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(SCREAMING_SN... | 315 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
def is_in_circle(UpperCAmelCase , UpperCAmelCase ) -> bool:
a_... | 243 | 0 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
lowercase_ : Tuple = len(__SCREAMING_SNAKE_CASE )
print('''The... | 93 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case ... | 243 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE_ ):
"""simple ... | 63 |
"""simple docstring"""
import baseaa
def UpperCamelCase ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return baseaa.baadecode(UpperCAmelCase ).dec... | 243 | 0 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Dict=False ):
'''simple docstri... | 260 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 243 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToken... | 97 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 0 |
import os
import sys
import transformers
__A : List[Any] = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
p... | 138 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.... | 115 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
a_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelC... | 243 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( ... | 264 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case ( unittest.TestCase ):
a_ : Any = JukeboxTokenizer
a_ : Any = {
"""artist""": """Zac Brown Band""",
""... | 243 | 0 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def A__ ( UpperCAmel... | 83 |
"""simple docstring"""
UpperCamelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase_ = ['a', 'b', 'c', 'd', 'e']
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
... | 243 | 0 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 278 |
"""simple docstring"""
import math
import os
import sys
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = ""
try:
with open(UpperCAmelCase , "rb" ) as binary_file:
a_ = binary_file.read()
for dat in data:
a_ ... | 243 | 0 |
def lowercase_ ( _lowerCamelCase : Optional[Any] = 200):
lowercase__ : Union[str, Any] = [1, 2, 5, 10, 20, 50, 100, 200]
lowercase__ : Tuple = [0] * (pence + 1)
lowercase__ : str = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 87 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(UpperCAmelCa... | 243 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torch_av... | 315 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snak... | 243 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase :... | 93 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 243 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowercase : Optional[Any] , lowercase : Optional[Any] ) -> tuple[int, int]:
if b == 0:
return (1, 0)
((_a) , (_a)) = extended_euclid(lowercase , ... | 63 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
pr... | 243 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
if partitions <= 0:
raise ValueError('''partitions must ... | 260 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 243 | 0 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 97 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 243 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__A : List[str] = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__A : Optional[Any] = [ord(letter) for letter in string.ascii_lowerca... | 138 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
def __init__( self , __UpperCAmelCase) ->Any:
a_ = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same numb... | 243 | 0 |
"""simple docstring"""
from __future__ import annotations
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCamelCase : Tuple ):
'''simple docstring'''
__UpperCAmelCase : str ... | 115 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCam... | 243 | 0 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 264 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input... | 243 | 0 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def A__ ( UpperCAmelCase_ ):
if not sentence:
return ""
_UpperCamelCase : int = dict(zip(UpperCAmelCase_ , UpperCAmelCase_ ) )
return lower_to_upper.get(sentence[0]... | 83 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not i... | 243 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
_A = tuple[int, int]
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = vertices
lowerCAmelCase_ = {
... | 278 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMod... | 243 | 0 |
from scipy.stats import pearsonr
import datasets
UpperCamelCase = '''\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that ea... | 87 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 243 | 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_score
from torch import nn
from u... | 315 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
def is_in_circle(UpperCAmelCase , UpperCAmelCase ) -> bool:
a_... | 243 | 0 |
'''simple docstring'''
import re
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def snake_case_ ( __SCREAMING_SNA... | 93 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case ... | 243 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversat... | 63 |
"""simple docstring"""
import baseaa
def UpperCamelCase ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return baseaa.baadecode(UpperCAmelCase ).dec... | 243 | 0 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest... | 260 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 243 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats... | 97 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 10, _UpperCAmelCase = 22 ) -> int:
'''simple docstring'''
lowerCAmelCase : List[str] = range(1, _UpperCAmelCase )
lowerCAmelCase : str = range(1, _UpperCAmelCase )
return sum(
1 for power in ... | 138 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 0 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
... | 115 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
a_ = 1
for n in range(m + 1 ):
for k in range(1 , UpperCAmelC... | 243 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : Tuple = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-... | 264 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case ( unittest.TestCase ):
a_ : Any = JukeboxTokenizer
a_ : Any = {
"""artist""": """Zac Brown Band""",
""... | 243 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import j... | 83 |
"""simple docstring"""
UpperCamelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase_ = ['a', 'b', 'c', 'd', 'e']
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
... | 243 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mo... | 278 |
"""simple docstring"""
import math
import os
import sys
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = ""
try:
with open(UpperCAmelCase , "rb" ) as binary_file:
a_ = binary_file.read()
for dat in data:
a_ ... | 243 | 0 |
import os
import platform
import sys
UpperCamelCase = '''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
p... | 87 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(UpperCAmelCa... | 243 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_ax... | 315 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snak... | 243 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_de... | 93 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 243 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase_ : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
... | 63 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
pr... | 243 | 0 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__A : Dict = 2048
__A : Optional[Any] = 4096
__A : Any = 42
__A : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false")
__A ... | 260 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 243 | 0 |
'''simple docstring'''
def a ( __a ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(__a , __a ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(__a )]
if __name__ == "__main__":
pri... | 97 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 243 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A : str = '''\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.... | 138 |
"""simple docstring"""
from __future__ import annotations
class snake_case :
def __init__( self , __UpperCAmelCase) ->Any:
a_ = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same numb... | 243 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/... | 115 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCam... | 243 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _UpperCAmelCase ( datasets.BeamBasedBuilder):
def _snake_case ( ... | 264 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ) ->bool:
"""simple docstring"""
a_ = set()
# Replace all the whitespace in our sentence
a_ = input_str.replace(" " , "" )
for alpha in input... | 243 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_... | 83 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not i... | 243 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Data... | 278 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMod... | 243 | 0 |
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipe... | 87 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 243 | 0 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import d... | 315 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
def is_in_circle(UpperCAmelCase , UpperCAmelCase ) -> bool:
a_... | 243 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import ... | 93 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case ... | 243 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE_ ):
... | 63 |
"""simple docstring"""
import baseaa
def UpperCamelCase ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return baseaa.baadecode(UpperCAmelCase ).dec... | 243 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONF... | 260 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tra... | 243 | 0 |
'''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, TrainingJobAnalytics
from sagemaker.huggingf... | 97 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 0 |
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