code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8... | 429 |
from collections import deque
from .hash_table import HashTable
class __snake_case (_a ):
def __init__( self : int , *_UpperCAmelCase : str , **_UpperCAmelCase : Union[str, Any] ) -> Tuple:
'''simple docstring'''
super().__init__(*_UpperCAmelCas... | 429 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self :int ):
_... | 703 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_UpperCamelCase = logging.get_logger(__name__... | 363 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCamelCase__ : Dict = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear... | 31 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDep... | 391 | 0 |
'''simple docstring'''
import operator
def __UpperCAmelCase ( a_: list, a_: bool = False, a_: list | None = None ):
_UpperCAmelCase : Tuple = operator.lt if reverse else operator.gt
_UpperCAmelCase : Any = solution or []
if not arr:
... | 714 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
... | 257 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(F... | 257 | 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 ConfigTester
from ...test_modeling_commo... | 192 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDepe... | 705 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[Any]:
super().__init__(*UpperCamelCase_ , **UpperC... | 523 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int ) -> str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
lowercase : Dict =len(bin(__magic_name__ )[3:] )
lowercase : List[Any] ... | 92 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : int = (UnCLIPScheduler,)
def lowerCAmelCase ( self : Union[str, Any] ,**SCREAMI... | 652 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ :Any = [
'good first issue',
'feature request',
'wip',
]
def lowerCamelCase_ ( ) ->str:
"""simple docstring"""
... | 704 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase_... | 374 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_opt... | 2 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __a ( A ) -> Union[str, Any]:
'''s... | 337 | 0 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 570 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase =... | 570 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ... | 87 |
# Copyright 2023 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 appli... | 40 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
snake_case = False
class UpperCA... | 710 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ = 1000 ) -> int:
return sum(e for e in range(3 , lowerCAmelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 404 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : str = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
... | 375 |
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->list:
"""simple docstring"""
__magic_name__ : Optional[Any] = word.split()
def justify(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ) -> str:
__mag... | 154 | 0 |
import os
import sys
import unittest
_snake_case : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy... | 720 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class ... | 421 | 0 |
"""simple docstring"""
import os
import sys
import unittest
_lowerCAmelCase : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E... | 46 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class Upp... | 473 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
snake_case_ : List[Any] = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
snake_case_ : List[Any] = [ord(letter)... | 708 |
'''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 ... | 350 | 0 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def __A ( a_ : int ):
lowerCAmelCase : Any = year % 1_9
lowerCAmelCase : str = year % 4
lowerCAmelCase : Union[str, Any] = year % 7
lowerCAmelCase : ... | 525 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resi... | 348 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( snake_case = "https://www.worldometers.info/coronavirus"):
__snake_case = BeautifulSoup(requests.get(_lowerCamelCase).text, '''html.parser''')
__snake_case = ... | 700 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( snake_case):
__snake_case = len(snake_case)
for i in range(length - 1):
__snake_case = i
for k in range(i + 1, snake_case):
if collection[k] < collection[least]:
... | 93 | 0 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] =logging.get_logger(__name__)
__lowercase : Optional[Any] ={
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""h... | 54 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""microsoft/focalnet-tiny""": """... | 678 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={"vocab_file": "vo... | 705 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
lowerCamelCase =logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_SNAKE_CASE_ ( U... | 462 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self ):
__a = {}
def snake_case_ ( self , __A ):
__a = {}
... | 99 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common im... | 319 | 0 |
def lowerCamelCase__ ( _A ):
if not all(char in '01' for char in bin_string ):
raise ValueError('Non-binary value was passed to the function' )
if not bin_string:
raise ValueError('Empty string was passed to the function' )
a : Any = ''
while len(_A ) %... | 707 |
'''simple docstring'''
class a__:
def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : Any , __snake_case : Tuple ):
a : List[str] = name
a : Dict = value
a : List[str] ... | 195 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return number | (1 << position)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return number & ~(1 << position)
def UpperCAmelCase_ ( __SCREAMING_SNAK... | 84 | # This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
Au... | 64 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:... | 347 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/def... | 347 | 1 |
"""simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import ve... | 482 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__UpperCamelCase : Any = TypeVar("KT")
__UpperCamelCase : List[Any] = TypeVar("VT")
class __lowerCAmelCase ( Generic[KT, VT] ):
def __init__( self ... | 468 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pip... | 714 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_lowerCAmelCase = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Eval... | 10 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
_UpperCAmelCase : List[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_UpperCAmelCase : ... | 715 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmel... | 108 | 0 |
import math
def __lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = 0 ) -> list:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] = end or len(_UpperCamelCase )
for i in range(_UpperCamelCase ... | 306 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import tor... | 306 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__snake_case : int = TypeVar("T")
__snake_case : List[Any] = TypeVar("U")
class A ( Generic[T, U] ):
def __init__( self , ... | 705 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unorde... | 691 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler... | 444 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase : Optional[int] = datasets.logging.get_logger(__name__)
lowerCAmelCase : List[str] = """\
@inprocee... | 444 | 1 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowerCamelCase : Optional[int] = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- ... | 290 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Ac... | 290 | 1 |
import colorsys
from PIL import Image # type: ignore
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> List[Any]:
snake_case : Optional[int] = x
snake_case : Tuple = y
for step in range(lowerCAmelCase__ ): # noqa: B007
snake_... | 587 |
import argparse
import os
import re
_lowercase : List[str] ="""src/diffusers"""
# Pattern that looks at the indentation in a line.
_lowercase : str =re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : Dict =re.comp... | 364 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 720 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list:
for i in range(len(lowercase ) - 1 ,0 ,-1 ):
snake_case : Any = False
for j in range(lowercase ,0 ,-1 ):
if unsorted[j] < unsorted[j - 1]:
snake_case , snake_case : Option... | 684 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __a:
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=64 ,_SCREAMING_SNAKE_CASE=None ... | 30 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , _lowerCAmelCase : Optional[int]=2 , _lowerCAme... | 31 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info... | 715 |
import collections
import os
import re
from pathlib import Path
lowercase : int = """src/transformers"""
# Matches is_xxx_available()
lowercase : List[str] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowercase : str = re.compil... | 392 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> tuple[float, float]:
if not all(isi... | 42 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import Robe... | 212 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging... | 212 | 1 |
"""simple docstring"""
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__A = collections.namedtuple('''_Datasets''', ['''train... | 646 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCAmelCase__(__snake_case ) -> Dict:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
lowerCamelCase__ = na... | 481 | 0 |
def A_ ( lowercase_ ) ->bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
__UpperCAmelCase = int(input... | 259 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"configuration_layoutlmv3": [
"LAYOUTLMV3_PRETRAINED_CO... | 259 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCamelCase__ = input("""Enter image url: """).strip()
print(F"""Downloading image from {url} ...""")
lowerCamelCase__ = BeautifulSoup(requests.get(url).content, """html.parser... | 225 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class SCREAMING_SNAKE_... | 225 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class A_ ( __UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__snake_case = DownB... | 713 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class... | 230 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
i... | 109 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 36 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A : int = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if... | 330 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : List[str] = logging.get_logger(__name__)
_A : Any = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json'... | 330 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
lowerCamelCase = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logg... | 474 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
... | 474 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
UpperCAmelCase = TypeVar("""T""")
class UpperCAmelCase_ ( Generic[T]):
def __init__( self : int , __UpperCamelCase : list[T] , __Upp... | 342 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils impor... | 342 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_a : Union[str, Any] = logging.get_logger(__name__)
class _lowercase ( __lowercase ):
def __init__( self : Dict , *SCREAMING_SNAKE_... | 56 | def __lowerCAmelCase ( A_ : int , A_ : int ) -> int:
return x if y == 0 else greatest_common_divisor(A_ , x % y )
def __lowerCAmelCase ( A_ : int , A_ : int ) -> int:
return (x * y) // greatest_common_divisor(A_ , A_ )
d... | 221 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _UpperCAmelCase ( __lowerCamelCase : Dict ) -> int:
_snake_case = [
'''encoder.version''',
'''decoder.version''',... | 709 |
"""simple docstring"""
# Copyright 2023 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/LICENS... | 430 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ :Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ :Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
c... | 355 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ :str = logging.get_logger(__name__)
UpperCamelCase__ :Optional[int] = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/c... | 355 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__magic_name__ ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 469 | 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 _A ( __UpperCamelCase , __UpperCamelCase ):
@register_to_config
def __init__(self ... | 469 | 1 |
# Copyright 2023 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 ap... | 66 |
"""simple docstring"""
def UpperCAmelCase_ ( __a : list ):
'''simple docstring'''
if len(__a ) <= 1:
return lst
_lowerCamelCase : str = 1
while i < len(__a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_lowerC... | 437 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus imp... | 330 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Union[str, Any] = logging.get_logger(__name__)
_A : Union[str, Any] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/... | 330 | 1 |
"""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(_a ) ,... | 46 |
from torch import nn
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , _snake_case : List[Any] , _snake_case : Tuple ):
super().__init__()
__lower... | 509 | 0 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Tuple = (DDPMScheduler,)
def _snake_case ( self , **_SCREAMING_SNAKE_CASE )-> Tuple:
... | 75 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__A : int = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))
print('Googling.....')
__A... | 75 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( A__ ):
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(A__ ) ):
matrix[i][0] += matrix[i - 1][0]
... | 41 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] ... | 654 | 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__ : Dict = logging.get_logger(__name__)
a__ : Union[str, Any] = {
"... | 309 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a__ : List[str] = """docs/source/en/_toctree.yml"""
def A__ ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = defaultdict(__lowerCamelCase )
for do... | 309 | 1 |
def UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
__lowercase , __lowercase = len(UpperCamelCase__ ), len(grid[0] )
if (
min(UpperCamelCase__ , UpperCamelCase__ ) < 0
or... | 534 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Tuple = 'M-CLIP'
def __init__( self : Any , lowerCamelCase__ : List[Any]=1_... | 332 | 0 |
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_device
from diffusers.utils.testing_utils import en... | 713 |
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 SCREAMING_SNAKE_CASE_ (a__ ):
... | 171 | 0 |
def a_ ( __magic_name__ ) -> None:
"""simple docstring"""
snake_case : Any = generate_pascal_triangle(__magic_name__ )
for row_idx in range(__magic_name__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ... | 598 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class a_ ( a ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON ser... | 598 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def _lowerCamelCase ( lowerCamelCase_ : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
UpperCAmelCa... | 715 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_senten... | 389 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Confi... | 696 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 4 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _snake_case ( __a ):
"""simple docstring"""
lowerCame... | 317 | import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModel... | 271 | 0 |
'''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
snake_case_ = logging.get_logger(__name__)
snake_case_ = {'vocab_file'... | 721 |
'''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.uti... | 68 | 0 |
'''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
a : int = ge... | 69 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A :
_SCREAMING_SNAKE_CASE = field(
default="""codeparrot/codeparrot""" ,metadata={"""help""": """Model name or path of model to be trained."""} )
_SCREAMING_SNAKE_CASE = ... | 326 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A_ : List[str] =True
except (ImportError, ModuleNotFoundError):
A_ : str =False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet... | 222 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float )-> float:
if edge <= 0 or not isinstance(snake_case , snake_case ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def SCREA... | 222 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : complex , SCREAMING_SNAKE_CASE__ : str = "x" , SCREAMING_SNAKE_CASE__ : float = 10**-10 , SCREAMIN... | 663 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 0 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from ... | 37 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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_available():
... | 37 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCAmelCase__ : Optional[Any] = 1
UpperCAmelCase__ : Any = 1
while repunit:
UpperCAmelCase__ : Unio... | 182 |
"""simple docstring"""
from __future__ import annotations
import math
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = size
# approximate the overall s... | 182 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> str:
# ===== initialization =====
_lowercase : Tuple = Mock()
_low... | 720 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 | 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 import Conv... | 644 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Union[str, Any] = (720, 1280) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
... | 683 | 0 |
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 ( UpperCAmelCase_ ):
__lowerCAmelCase... | 708 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__ : int = Lock()
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE ... | 620 | 0 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
a : List[Any] = ... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
"""simple docstring"""
import functools
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n... | 95 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def low... | 95 | 1 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : List[str] = R'''
Args:... | 286 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, l... | 286 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
f... | 704 | '''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_A : Union[str, Any] = '''\
@misc{chen2021evalua... | 330 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE (__A ):
"""simple docstring"""
_a : Tuple = (DDPMParallelScheduler,)
def _a ( self , ... | 536 |
'''simple docstring'''
def __UpperCamelCase ( lowercase_ : list[int] , lowercase_ : list[int] , lowercase_ : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, ne... | 536 | 1 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase_ ( a_ , a_ ):
@register_to_config
def __init__( self , *,
snake_case__ = 4 , snake_case... | 378 |
"""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,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 378 | 1 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _UpperCamelCase (_lowerCamelCase : Union[dict, list, ... | 24 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__snake_case = 1
__snake_case = 1
while repunit:
__snake_case ... | 24 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIV... | 707 |
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase_ : np.array )-> np.array:
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase_ : np.array )-> np.array:
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doc... | 526 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffus... | 90 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class __SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
__a =42
__a =42
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: str ) -> list[... | 448 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectro... | 701 |
def a_ ( _A , _A ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_A ) , _A )
return number - int(_A )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decimal_isola... | 372 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCAmelCase_ ( *lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
__magic_name__ : Union[str, Any] =list(low... | 21 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self , snake_case ) -> Dict:
"""simple docstr... | 112 | 0 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transf... | 705 |
def a_ ( __magic_name__ ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
snake_case : int = 4
snake_case : Optio... | 84 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Union[s... | 549 |
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = 8.314_4598
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float ) -> float:
if temperature < 0:
raise Exception('Temperature canno... | 549 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureEx... | 458 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fro... | 458 | 1 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_s... | 580 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 580 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See al... | 717 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggi... | 48 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils impo... | 604 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 1 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase_ :
def _lowercase( self , A ) -> Optional[int]:
raise NotImplementedError()
def _l... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_a... | 37 |
"""simple docstring"""
from __future__ import annotations
class __magic_name__ :
def __init__( self , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = order
# a_{0} ... a_{k}
_lowerCAmelCase = [1.0] + [0.0] * orde... | 589 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def SCREAMING_SNAKE_CASE ( snake_case_ : ... | 707 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ... | 25 | 0 |
"""simple docstring"""
import math
def __A ( a_ :int) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not pr... | 52 |
"""simple docstring"""
def __A ( a_ :Tuple , a_ :Union[str, Any] , a_ :int=False) -> List[str]:
if isinstance(a_ , a_) and isinstance(a_ , a_):
__a : List[str] = len(set_a.intersection(a_))
if alternative... | 52 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> np.ndarray:... | 715 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
snake_case_ = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
auth... | 262 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : Tuple = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( ... | 63 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _lowerCamelCase :
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase ):
"""simple docstring"""
if dst_width < 0 or dst_height < 0... | 365 | 0 |
from ...processing_utils import ProcessorMixin
class lowercase_ ( A ):
__lowerCamelCase = ["image_processor", "feature_extractor"]
__lowerCamelCase = "TvltImageProcessor"
__lowerCamelCase = "TvltFeatureExtractor"
def __init__( self... | 431 |
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[int]=None , **UpperCAmelCase_ : Any ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ :... | 431 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',... | 603 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_tr... | 603 | 1 |
import logging
import os
from .state import PartialState
class SCREAMING_SNAKE_CASE__ ( logging.LoggerAdapter ):
@staticmethod
def SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ : Any ) -> int:
a_ : Optional[Any] = PartialState()
... | 443 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
... | 443 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ = None ) ->int:
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
_lowerCamelCase : str = nums[0]
for i in range... | 434 | """simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import ... | 434 | 1 |
'''simple docstring'''
__snake_case: Optional[int] = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,... | 460 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case: List[str] = logging.get_logger(__name_... | 460 | 1 |
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