code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple... | 108 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : str ... | 108 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 361 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 0 |
from timeit import timeit
def _a ( SCREAMING_SNAKE_CASE : Any ) -> List[str]:
"""simple docstring"""
if number < 0:
raise ValueError('the value of input must not be negative' )
__lowerCAmelCase: Optional[Any] = 0
while number:
number &= number - ... | 322 | """simple docstring"""
UpperCAmelCase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre"""... | 289 | 0 |
"""simple docstring"""
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
_lowercase : str = logging.get_logger(__name__)
_lo... | 368 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def snake_case__ ( __lowerCamelCase : jnp.ndarray , __lowerCamelCase : int , __lowerCamelCase : float = 1 , __lowerCamelCase : float = 1 , __lowerCamelCase :... | 272 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_snake_case = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( _lowerCamel... | 36 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.vers... | 254 | 0 |
from __future__ import annotations
from typing import Any
class lowercase_ :
def __init__( self , __UpperCamelCase = 6 ):
"""simple docstring"""
UpperCamelCase_ = None
UpperCamelCase_ = None
self.create_linked_list(__UpperCamelCa... | 261 |
from __future__ import annotations
def lowerCamelCase__ ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in... | 261 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase_( lowerCamelCase_ ) -> str:
return "".join(sorted(lowerCamelCase_ ) )
def UpperCamelCase_( lowerCamelCase_ ) -> list[str]:
return word_by_signature[signatur... | 21 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import... | 113 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 216 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ (__snake_case ):
def __init__( self , a , ... | 216 | 1 |
"""simple docstring"""
class A_ :
"""simple docstring"""
def __init__( self :List[str] , lowerCamelCase_ :list[int] ):
"""simple docstring"""
lowerCamelCase__ : Any =len(lowerCamelCase_ )
... | 126 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase = get_test... | 126 | 1 |
import qiskit
def UpperCamelCase( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
'''simple docstring'''
snake_case_ = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
snake_case_ = qiskit.... | 34 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 1 |
from __future__ import annotations
lowercase : Tuple = """Muhammad Umer Farooq"""
lowercase : List[str] = """MIT"""
lowercase : Any = """1.0.0"""
lowercase : str = """Muhammad Umer Farooq"""
lowercase : List[str] = """... | 20 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 20 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 351 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 125 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non ... | 96 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase :int = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise Option... | 263 | 0 |
from __future__ import annotations
from math import gcd
def __lowerCamelCase ( __magic_name__ : int , __magic_name__ : int = 2 , __magic_name__ : int = 1 , __magic_name__ : int = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
... | 42 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeat... | 42 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Optional[int] = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolv... | 115 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simp... | 281 | 0 |
def _UpperCamelCase ( snake_case__ ) -> bool:
__UpperCAmelCase : Any = 0
for ch in input_str:
__UpperCAmelCase : Optional[Any] = ord(snake_case__ )
__UpperCAmelCase : Union[str, Any] = p... | 366 | import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 342 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : List[str] = len(_lowerCAmelCase ) + 1
lowercase__ : Any = len(_lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i]... | 77 | import math
import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1 ):
'''simple docstring'''
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CAS... | 43 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import c... | 78 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],... | 78 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
impo... | 273 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 1 |
import os
import string
import sys
lowerCAmelCase__ :List[str] = 1 << 8
lowerCAmelCase__ :Any = {
"""tab""": ord('''\t'''),
"""newline""": ord('''\r'''),
"""esc""": 2_7,
"""up""": 6_5 + ARROW_KEY_FLAG,
"""down""": 6_6 + ARROW_KEY_FLAG,
"""right""": ... | 366 |
def lowerCAmelCase__ ( a__: int ) -> None:
'''simple docstring'''
_UpperCAmelCase = generate_pascal_triangle(a__ )
for row_idx in range(a__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
... | 185 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 261 | """simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case__ ( snake_case_, snake_case_ ):
@register_to_config
def __init__( ... | 261 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : int ):
'''simple docstring'''
_UpperCAmelCase : Tuple =[True] * limit
_UpperCAmelCase : int =False
_UpperCAmelCase ... | 242 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowercase =logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase ):
def __init__( self , *snake_case , ... | 242 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pix... | 216 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impor... | 216 | 1 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _lowerCAmelCase ( lowerca... | 359 |
'''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,
# or u belongs to V a... | 46 | 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 =logging.get_logger(__name__)
A ={
'facebook/data2vec-text-base': 'https://huggingface.co/da... | 34 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return ... | 34 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = FileLock(str(tmpdir / 'foo.lock' ) )
UpperCAmelCase = FileLock(str(tmpdir / 'foo... | 360 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
"""configuration_rember... | 181 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ) or not all(
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ... | 108 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Tuple = logging.get_logger(__name__)
snake_case_ : Optional[int] = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-robe... | 125 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers... | 329 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFo... | 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 1 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if edge <= 0 or not isinstance(__a , __a ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) *... | 350 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch... | 220 | 0 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 326 |
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 import logging
logging.s... | 326 | 1 |
"""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 AutoMode... | 358 |
import math
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1... | 315 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
snake_case_ = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( ):
UpperCAmelCase = Github(os.environ['GITHUB... | 78 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...sch... | 78 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps... | 254 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=a ):
"""simple docstring"""
__magic_name__ :Optional[Any] = ["""onnx"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ... | 254 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
a : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, ... | 56 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 185 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __A ( ) -> List[Any]:
__a : List[str] = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''')
__a : Any ... | 188 |
"""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.apach... | 188 | 1 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase_ ( __UpperCAmelCase ) -> Union[str, Any]:
lowerCAmelCase__ : List[Any] = {}
lowerCAmelCase__ : Any = job[""... | 242 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
def count_of_possible_combinations(__UpperCAmelCase ) -> int:
if target < 0:
return 0
if target == 0:
r... | 242 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _a ( SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 358 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling... | 51 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowercase__( nn.Module ):
"""simple docstring"""
a :int
a :jnp.dtype = jnp.floataa
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
lowercase_ = nn.Con... | 30 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..tab... | 46 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_snake_case = datasets.utils.logging.get_logger(__name__)
... | 300 |
class UpperCAmelCase_ :
def __init__( self):
'''simple docstring'''
_lowerCAmelCase : Dict = 0
_lowerCAmelCase : Optional[int] = 0
_lowerCAmelCase : Tuple = {}
... | 300 | 1 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase_ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def __magic_name__... | 244 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Any = min(lowerCAmelCase__ ) # min() finds the minimum value
UpperCAmelCase__ : Optional[int] = ... | 181 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
@register_to_config
def __init__( self : Any , *,
... | 237 | '''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria... | 237 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils impor... | 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :Dict = logging.get_logger(__name__)
lowerCAmelCase__ :Optional[int] = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class __a ( UpperCAmelCas... | 329 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 361 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 101 | 0 |
"""simple docstring"""
_a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( UpperCamelCase_ : List[Any], UpperCamelCase_ : Optional[Any], UpperCamelCase_ ... | 17 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCamelCase : Any = TypeVar('T')
class a ( Generic[T] ):
def __init__( self , _lowerCamelCase ):
lowercase = ... | 220 | 0 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from .... | 351 |
"""simple docstring"""
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCAmelCase__ = logging.getLogger(__name__)
class __snake_case ( _lowercase):
def ... | 175 | 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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.... | 243 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , ... | 315 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as tr... | 187 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( _SCREAMING_SNAKE_CASE : int | str ) -> bool:
"""simple docstring"""
lowerCAmelCase = str(_SCREAMING_SNAKE_CASE )
return n == n[::-1]
def _snake_c... | 187 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_UpperCamelCase = '''examples/'''
_UpperCamelCase = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (r... | 254 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCamelCase = '''
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)
'''
_UpperCamelCase = '''
Args:
... | 254 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
lowerCAmelCase__ : List[str] ='''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktit... | 370 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __lowercase ( a__ ) -> List[Any]: # picklable for multiprocessing... | 118 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_modeling_common... | 188 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCAmelCase__ ( _A : int = 3 ):
'''simple docstring'''
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a i... | 188 | 1 |
from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 284 | from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, id... | 284 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
... | 318 |
snake_case_ : Dict = {
"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",
"huggingfa... | 51 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import co... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
),
# S... | 216 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = [True] * 1_0_0_0_0_0_1
SCREAMING_SNAKE_CASE_ = 2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
SCREAMING_SNAKE_CASE_ = False
... | 296 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 278 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elast... | 361 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepE... | 228 | 0 |
'''simple docstring'''
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 237 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( _lowerCamelCase : list[int | str] ):
create_state_space_tree(_lowerCamelCase , [] , 0 , [0 for i in range(len(_lowerCamelCase ) )] )
def ... | 237 | 1 |
def __magic_name__ ( __a : int , __a : int ):
'''simple docstring'''
while a != 0:
UpperCamelCase__ , UpperCamelCase__ = b % a, a
return b
def __magic_name__ ( __a : int , __a : int ):
'''simple docstring'''
... | 178 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __magic_name__ ( __a : Any ): # picklable for multiprocessing
''... | 178 | 1 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__snake_case = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It take... | 97 |
import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=1000 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase = n - 1
... | 101 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase__ =(3, 9, -11, 0, 7, 5, 1, -1)
lowercase__ =(4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class UpperCamelCase__ :
_SCREAMING_SNAKE_CASE : int
_SCREAMING_SNAKE_... | 90 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase__ ( __lowercase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = Ph... | 90 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 20 | def __lowercase ( lowerCamelCase : str , lowerCamelCase : str ):
def get_matched_characters(lowerCamelCase : str , lowerCamelCase : str ) -> str:
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : List[Any] = min(len(_stra ) , len(_stra ) ) /... | 175 | 0 |
'''simple docstring'''
import argparse
import copy
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Dict = {}
with open(UpperCamelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 283 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _snake_case ( ... | 283 | 1 |
def lowerCamelCase__ ( _A ):
'''simple docstring'''
if len(_A ) < 2:
return collection
def circle_sort_util(_A , _A , _A ) -> bool:
snake_case_ = False
if low == high:
return swapped
snake_case_ = low
... | 187 |
import numpy
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowercase : numpy.ndarray , __lowercase : numpy.ndarray ):
"""simple docstring"""
... | 187 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
UpperCAmelCase: int = logging.get_logger(__name__)
UpperCAmelCase: List[An... | 336 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_visio... | 336 | 1 |
lowerCamelCase : 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_dispatch,
)
from .data_loader import skip_fir... | 124 | import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configura... | 118 | 0 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase =logging.getLogger()
@unittest.skip('''T... | 77 |
"""simple docstring"""
def _A ( _a : Optional[int] ):
"""simple docstring"""
A = []
A = set({"""(""", """[""", """{"""} )
A = set({""")""", """]""", """}"""} )
A ... | 77 | 1 |
def a_ ( lowerCAmelCase_ : str ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCAmelCase_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod()
| 284 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a_ ( lowerCAmelCase_ : Optional[int... | 284 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
... | 354 | import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
Up... | 143 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_A : Union[str, Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
... | 229 |
def __UpperCamelCase ( lowerCAmelCase__ : int = 5_0_0_0_0_0_0_0 ):
__a : int = set()
__a : str = int((limit - 2_4) ** (1 / 2) )
__a : int = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p... | 216 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if ... | 371 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
l... | 48 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() an... | 50 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
'''simple docstring'''
from math import factorial, radians
def _UpperCAmelCase ( _UpperCamelCase : float, _UpperCamelCase : int = 18, _UpperCamelCase : int = 10 ) -> float:
A_ = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0... | 18 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
... | 18 | 1 |
from __future__ import annotations
import math
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , a ) -> None:
snake_case_ = size
# approximate the overall size of segment tree with given value
snake_case_ = ... | 178 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowercase = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ ( snake_case_ ):
'''simple docstr... | 178 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> bool:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = [int(_lowerCamelCase) for i in ip_va_address.split(".") if i.isdigit()]
return len(_lowerCamelCase) == 4 and all(0 <= int(_lowerCa... | 366 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PI... | 151 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : list ) -> list:
"""simple docstring"""
if len(UpperCamelCase__ ) <= 1:
return [tuple(UpperCamelCase__ )]
__lowerCamelCase = []
def generate(UpperCamelCase__ : i... | 90 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common impo... | 90 | 1 |
"""simple docstring"""
import sys
from collections import defaultdict
class A_ :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = []
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docst... | 23 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class A_ (unittest.TestCase ):
'''simple docstring'''
def Upp... | 23 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : int = (UnCLIPScheduler,)
def _snake_case ( self , **__A ):
... | 283 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 , 0 , -1 ):
lowerCamelCase : Tuple = False
for j in range(SCREAMING_SNAKE_CASE_ , 0 , -1 ):
if unsorted[j] < unsorted[j... | 283 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'''vocab_file''': '''vocab.json'''... | 364 | import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class lowercase :
lowercase__ : str = None
@experi... | 206 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {"voca... | 336 |
import unittest
import numpy as np
from transformers import RobertaConfig, 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.mod... | 336 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> list:
'''simple docstring'''
if n_term == "":
return []
_lowerCamelCase : list = []
for temp in range(int(_lowerCamelCase ) ):
series.append(F"""1/{temp + 1}""" if series else "1" )... | 340 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffusio... | 77 | """simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS... | 77 | 1 |
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeli... | 351 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fro... | 221 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 303 | from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __snake_case ( _lowerCamelCase ):
@staticmethod
@abstractmethod
def __a ( __UpperCamelCase ) -> Dict:
'''simple docstring'''
raise NotImplementedEr... | 143 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCam... | 299 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MOD... | 299 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 327 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : List[Any] = {'processing_layoutxlm'... | 48 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCamelCase__ : int = 1000 ):
'''simple docstring'''
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = ... | 362 | def lowerCamelCase_ ( UpperCamelCase__ : list[int], UpperCamelCase__ : list[int], UpperCamelCase__ : int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCamel... | 35 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {... | 18 | def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = int(lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict ... | 18 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
SCREAMING_SNAKE_CASE__ = {
'n_samples': 6_4,
'horizon': 3_2,
'num_inference_steps': 2_0,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value net... | 366 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE__ = 'scheduler_config.json'
class a_ ( ... | 183 | 0 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Dict:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 189 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import Dif... | 151 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
... | 295 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWit... | 295 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
... | 23 |
'''simple docstring'''
from manim import *
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
def A ( self : Union[str, Any] ) -> List[str]:
UpperCAmelCase : Optional[Any] = Rectangle(height=0.5 , widt... | 23 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__A : int = logging.get_logger(__name__)
class __UpperCamelCase ( _A ):
def __init__(self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : ... | 57 |
"""simple docstring"""
from __future__ import annotations
class __UpperCamelCase :
def __init__(self : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str):
A , A = text, pattern
A , A = len(__SCREAMING_SNAKE_C... | 57 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__a: Dict = logging.get_logger(__name__)
__a: Dict = {
'''google/umt5-small''': '''https://huggingface.co/google... | 198 |
'''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 ( lowerCamelCase_... | 206 | 0 |
import numpy as np
class UpperCamelCase :
def __init__( self ):
A__ = (0, 0)
A__ = None
A__ = 0
A__ = 0
A__ = 0
def __eq__( self , UpperCAmelCase__ ):
return self.position == c... | 198 |
from __future__ import annotations
from random import random
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ = None ):
A__ = value
A__ = random()
A__ = None
A__ = None
def __repr__( self )... | 198 | 1 |
def _a ( UpperCamelCase_ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowerCAmelCase__ = []
for temp in range(int(UpperCamelCase_ ) ):
series.append(F"1/{temp + 1}" if series else "1" ... | 340 |
from collections import defaultdict
def _a ( UpperCamelCase_ : int ) -> int:
"""simple docstring"""
lowerCAmelCase__ = 1
lowerCAmelCase__ = True
for v in tree[start]:
if v not in visited:
ret += df... | 340 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Union[str, Any] = {
"configuration_roform... | 202 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCamelCas... | 221 | """simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 221 | 1 |
def _A ( SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edg... | 367 |
UpperCAmelCase : Dict = [0, 2, 4, 6, 8]
UpperCAmelCase : Tuple = [1, 3, 5, 7, 9]
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ):
"... | 148 | 0 |
import unittest
from transformers import BigBirdConfig, 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 jax
from transformers.models.big_bird.modeling_f... | 299 |
from cva import destroyAllWindows, imread, imshow, waitKey
def A__ ( __lowerCamelCase ):
# getting number of pixels in the image
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowerCa... | 299 | 1 |
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(__lowerCamelCase ) , 'Tato... | 357 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 317 | 0 |
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