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 |
|---|---|---|---|---|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils impor... | 645 | '''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS... | 152 | 0 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_ut... | 119 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
requi... | 119 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = ... | 21 |
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 UpperCamelCase ( __lowe... | 204 | 0 |
import os
def __UpperCamelCase ( ) ->Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =os.path.dirname(os.path.realpath(a_ ) )
lowerCamelCase_ =os.path.join(a_ , """triangle.txt""" )
with open(a_ ) as f:
lowerCamelCase_... | 712 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
... | 75 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
_snake_case : Dict = ['transformers', 'torch', 'note_seq']
def __init__( self : Tuple ... | 98 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
snake_case__ : Tuple = pd.read_csv('''sample_data.csv''', header=None)
... | 392 | 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 lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREA... | 712 | from __future__ import annotations
import bisect
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = -1 ):
'''simple docstring'''
if hi < 0:
__UpperCamelCase :str = len(SCREAMING_SNAK... | 452 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggi... | 19 |
"""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,
res... | 19 | 1 |
'''simple docstring'''
import re
def lowercase_ ( lowercase__ ) ->bool:
_snake_case: Dict = re.compile(R'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(lowercase__ , lowercase__ ):
return match.string == phone
return ... | 273 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avai... | 273 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _A ( __l... | 26 |
'''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... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'Cla... | 267 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 267 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_ut... | 598 |
"""simple docstring"""
import argparse
import json
import subprocess
def UpperCamelCase ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict ... | 238 | 0 |
"""simple docstring"""
import os
import sys
import unittest
a : Tuple = 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: E402
g... | 702 |
"""simple docstring"""
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 snake_case__ ( _SCREAMING_SNAKE_CASE ) ->Dict: #... | 422 | 0 |
from statistics import mean
import numpy as np
def __magic_name__ ( __a : list , __a : list , __a : list , __a : int ):
'''simple docstring'''
UpperCamelCase__ = 0
# Number of processes finished
UpperCamelCase_... | 513 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lower... | 50 | 0 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCa... | 703 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {"""vocab_file""": """sentencepiec... | 507 | 0 |
import doctest
from collections import deque
import numpy as np
class a :
"""simple docstring"""
def __init__( self : List[str] ) -> Any:
__UpperCAmelCase : List[str] = [2, 1, 2, -1]
__UpperCAmelCase : int = [1,... | 63 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ = 100 ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "... | 155 | 0 |
from __future__ import annotations
def __UpperCAmelCase ( __A ) -> List[str]:
'''simple docstring'''
return len(set(lowerCamelCase__ ) ) == len(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 715 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __UpperCAmelCase ( __A ) -> Union[str, Any]:
'''simple docstring'''
if (
(cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
... | 277 | 0 |
import numpy as np
def __lowercase ( _UpperCamelCase ) ->np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __lowercase ( _UpperCamelCase ) ->np.ndarray:
"""simple docstring"""
return vector * sigmoid(_UpperC... | 319 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''vocab_file''': '''vocab.txt'''}
__a = {
'''vocab_file''': ... | 319 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/fa... | 239 |
"""simple docstring"""
from itertools import count
def __lowerCAmelCase ( __lowerCAmelCase : int = 50 ) -> int:
_UpperCamelCase : Any = [1] * min_block_length
for n in count(__lowerCAmelCase ):
fill_count_functions.append(1 )
for block_length in ... | 239 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resol... | 373 | from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer impo... | 424 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase (... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # no... | 94 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _UpperCAmelCase ( __lowerCAmelCase ):
raise NotImplementedError()
... | 208 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowercase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
el... | 718 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common i... | 22 | 0 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/fa... | 432 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemak... | 432 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
... | 599 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCAmelCase__ ( a_ : bytes , a_ : int ) -> np.array:
UpperCAmelCase__ : Union[str, Any] = f"""{sampling_ra... | 599 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 407 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_t... | 407 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[An... | 721 |
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 ) -> int:
... | 166 | 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 ..... | 631 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPEN... | 631 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase( lowerCAmelCase_... | 233 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class __lowerCAmelCase( lowerCAmelCase__ ):
__snake_case : Optional[Any] = 'bert-generation'
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Any=50_358 ... | 233 | 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
if is_torch_available():
import torch
if is_vision_av... | 622 |
'''simple docstring'''
def lowerCamelCase__ ( A : int = 50 ):
'''simple docstring'''
UpperCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile... | 210 | 0 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> list:
"""simple docstring"""
__snake_case = len(SCREAMING_SNAKE_CASE )
for i in range(1 , SCREAMING_SNAKE_CASE ):
__snake_case = collection[i]
... | 614 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> list:
"""simple docstring"""
__snake_case = len(SCREAMING_SNAKE_CASE )
for i in range(1 , SCREAMING_SNAKE_CASE ):
__snake_case = collection[i]
... | 614 | 1 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
_A = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n ... | 505 |
"""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 OptionalDependencyNotAvailabl... | 505 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''junnyu/roformer_chinese_small''': '... | 226 |
import argparse
import json
from tqdm import tqdm
def A():
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=__a , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(... | 226 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenize... | 640 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, 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_comm... | 720 | '''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCAmelCase_ : Dict = TypeVar("T")
class lowercase ( Generic[T]... | 461 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_lowerCAmelCase :Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( lowerCAmelCase_ ):
'''simple docstring'''... | 251 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :Optional[int] = logging.get_logger(__name__)
UpperCAmelCase__ :Union[str, Any] = {
"""microsoft/wavlm-base""": """https... | 150 | 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... | 706 | import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert... | 469 | 0 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase : Any = Lock()
def lowerCamelCase__ ( A : List[Any] , A : Union[str, Any] , A : Tuple , A : Optional[int] , ... | 210 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Optional[int] )-> List[str]:
"""sim... | 210 | 1 |
from __future__ import annotations
snake_case__ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
snake_case__ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCamelCase__ ( a : list[float] ) -> list[float]:
"""simple docstring"""
a__ :T... | 373 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models at https://huggingf... | 373 | 1 |
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_torc... | 613 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : int = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerConfig""",
"""Bridge... | 613 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Ll... | 310 |
"""simple docstring"""
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 , snake_case: Dict=2 , snake_case: Uni... | 310 | 1 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCAmelCase_ = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < ... | 531 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_m... | 531 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__magic_na... | 717 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase = "laptop" ):
snake_case__ = F"""https://www.amazon.in/laptop/s?k={product}"""
snake_case__ = {
"User-Agent": "... | 530 | 0 |
"""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_MODEL_IDENTIFIER... | 363 | """simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCAmelCase (unittest.TestCase , __A ):
'''simple docstring'''
def lowerCamelCase ( self ):
'''simple docstring''... | 363 | 1 |
import heapq
def __magic_name__ ( lowercase ) -> set[int]:
"""simple docstring"""
lowercase_ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq m... | 436 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/micr... | 436 | 1 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : str ) ->int:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_SCREAMING_SNAKE_CASE = str(bin(__UpperCamelCase ... | 314 |
import sys
SCREAMING_SNAKE_CASE : List[Any] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6689664895044... | 141 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowerCamelCase_ ( _lowerCamelCase ):
'''simple docstring'''
if not is_accelerate_available():
return method
lowerCamelC... | 707 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A_ : Optional[int] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A_ : L... | 696 | 0 |
def __lowerCamelCase ( lowerCamelCase__ : str ):
'''simple docstring'''
if n_term == "":
return []
lowerCamelCase = []
for temp in range(int(lowerCamelCase__ ) ):
series.append(f'1/{temp + 1}' if series else """1""" )
return series
if ... | 457 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
UpperCAmelCase : Any ... | 457 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Dict = len(_lowerCamelCase )
lowerCamelCase__ : Dict = len(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = [[False for _ in range(m +... | 696 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
with open(os.path.dirname(_lowerCamelCase ) + '/p022_names.txt' ) as file:
lowerCamelCase__ : Union[str, Any] = str(file.readlines()[0] )
lowerCamelCase__ : int = names.replace('"' , '' ... | 696 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 57 |
"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import ... | 179 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 333 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ->int:
if dst_width < 0 or dst_height < 0:
... | 333 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class snake_case ( _a ):
"""simple d... | 261 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : int ):
"""simple docstring"""
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_s... | 616 | 0 |
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 snake_ca... | 303 |
def lowercase__( A = 1_0 , A = 1_0_0_0 , A = True ):
assert (
isinstance(A , A )
and isinstance(A , A )
and isinstance(A , A )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('In... | 303 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 381 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
... | 381 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked... | 721 |
import requests
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )-> None:
"""simple docstring"""
snake_case_ = {'''Content-Type''': '''application/json'''}
snake_case_ = requests.post(SCREAMING_SNAKE_CASE , json={'''text''': messag... | 531 | 0 |
def a__ ( _UpperCamelCase : int ,_UpperCamelCase : int ):
return int((input_a, input_a).count(0 ) == 0 )
def a__ ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if ... | 175 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE models at https://huggingface.co/models?filter... | 175 | 1 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( _SCREAMING_SNAKE_CASE : List[Any] ):
_SCREAMING_SNAKE_CASE = int(_SCREAMI... | 493 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterM... | 493 | 1 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : list ) -> str:
"""simple docstring"""
_enforce_args(__magic_name__ , __magic_name__ )
if n == 0:
return 0
lowercase__ = float("""-inf""" )
for i in range(1 , ... | 15 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
lowerCAmelCase__ ="examples/"
lowerCAmelCase__ ={
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__versio... | 482 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requ... | 69 |
import unittest
from transformers import DonutProcessor
lowerCamelCase__ = """naver-clova-ix/donut-base"""
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
... | 69 | 1 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 413 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = (DDPMScheduler,)
def __lowerCamelCase ( self : Optional[int] ... | 315 | 0 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename... | 708 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
"google/pix2struct-textcaps-base": (
... | 367 | 0 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any]... | 141 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __low... | 141 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python u... | 116 |
'''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
f... | 116 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : Tuple = "ClapFeatureExtractor"
_UpperCamelCase : Optional[int] = ("RobertaTokenizer", "RobertaTok... | 712 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase = typing.Union[np.floataa, int, float] # noqa: UP007
def __magic_name__ ( SC... | 677 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.h... | 371 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_co... | 371 | 1 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_lowerCamelCase = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplific... | 700 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
_lowerCamelCase = '''path-to-your-trained-model'''
_lowerCamelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
_lowerCamelCase = '''A photo of sks... | 401 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGen... | 380 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
A__: Dict = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'... | 380 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _snake_case :
def __init__( self , a , a , a , a , a , a=0.2 , a=0.2) -> Any:
SCREAMING_SNAKE_CASE = bp_numa
SCREAMING_SNAKE_CASE = bp_numa
SCR... | 718 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ : List[str] = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outp... | 444 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER,... | 100 |
from manim import *
class _UpperCAmelCase ( A__ ):
def snake_case_ ( self):
A__ = Rectangle(height=0.5 , width=0.5)
A__ = Rectangle(height=0.2_5 , width=0.2_5)
A__ = Rectangle(height=0.4_6 , width=0.4_6).set_... | 632 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 716 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class snake_case_ ( pl.LightningModule ):
def __init__( self : Union[str, Any] , _snake_case : List[str] )... | 240 | 0 |
from __future__ import annotations
from typing import Any
def _SCREAMING_SNAKE_CASE ( a ) -> None:
create_state_space_tree(a , [] , 0 )
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> None:
if index == len(a ):
print(a )
r... | 239 |
def _SCREAMING_SNAKE_CASE ( a ) -> list:
if len(a ) <= 1:
return lst
__A : Any = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A : str = lst[i]... | 239 | 1 |
"""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 ... | 553 |
"""simple docstring"""
import functools
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not all(isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) for day in days )... | 553 | 1 |
_UpperCAmelCase : Union[str, Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCAmelCase : Tuple = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""... | 362 |
"""simple docstring"""
from __future__ import annotations
def lowercase__(A , A ) ->list[int]:
"""simple docstring"""
lowercase__ : Any= 0
lowercase__ : List[str]= len(A ) - 1
while i < j:
i... | 218 | 0 |
"""simple docstring"""
def __lowercase ( a : int ) -> int:
__snake_case : Tuple =abs(a )
__snake_case : Optional[Any] =0
while n > 0:
res += n % 10
n //= 10
return res
def __lowercase ( a : int ) ... | 497 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffus... | 497 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( A_ ):
__UpperCAmelCase = (DDPMScheduler,)
def UpperCamelCase_ ( self , **SCREAMING_SNAKE_CASE) ... | 88 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Optional[Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAIN... | 142 | 0 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> list:
UpperCAmelCase__ : int = len(lowerCAmelCase__ )
UpperCAmelCase__ : Dict = []
for i in range(len(lowerCAmelCase__ ) - pat_len + 1 ):
UpperCAmelCa... | 312 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> bool:
UpperCAmelCase__ : List[Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 312 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase_ ( UpperCAmelCas... | 167 | import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERA... | 167 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__lowerCAmelCase = 2_99_79_24_58
# Symbols
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase = symbols('''ct x y z''')
def snake_case_ ( snake... | 335 |
def snake_case_ ( snake_case ) -> int:
if not isinstance(snake_case , snake_case ):
raise TypeError('only integers accepted as input' )
else:
lowercase__: str = str(abs(snake_case ) )
lowercase__: ... | 335 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, ... | 32 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 654 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class A :
def __init__( self : str , __UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
Upp... | 716 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a... | 559 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> list[int]:
if length <= 0 or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range... | 159 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConf... | 159 | 1 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.txt',
'merges_file': 'bpe.codes',
}
_... | 711 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDepen... | 627 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : List[str] = len(__lowerCamelCase )
__snake_case : Union[str, Any] = len(__lowerCamelCase )
__snake_case : Optional[Any] = [[False for ... | 81 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl... | 237 | 0 |
UpperCAmelCase__ : List[str] = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
UpperCAmelCase__ ... | 676 |
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__ : Any = logging.getLogger()
@unittest.skip... | 676 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [[0 for _ in range(__SCREAMING_SNAKE_CASE )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowercase = 1
for n in range(m + 1 ):
for k in range(1 , __SCREAMING_SNAKE_CASE ):
memo[n][k] += m... | 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class snake_case :
def __init__( self : List[str] ) -> Tuple:
'''simple docstring'''
_A ... | 700 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = ""
lowerCamelCase_ = (
None... | 6 |
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 import R... | 6 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class _lowercase ( UpperCamelCase_ ):
'''simple docstring'''
_A = 42
_A = 42
def a__ ( lowerCAmelCase : int ):
'''simple do... | 712 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase : List[Any] , lowerCAmelCase : An... | 660 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__UpperCamelCase = TypeVar('T')
__UpperCamelCase = TypeVar('U')
class lowerCamelCase__ ( Generic[T, U] ):
"""simple docstring"""
def __init__... | 551 |
"""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_ = {
"bert-base-uncased": "https://huggingfac... | 391 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature... | 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : str = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if... | 238 | 0 |
'''simple docstring'''
from math import factorial, pi
def UpperCamelCase__ ( __magic_name__ : float , __magic_name__ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(__magic_name__ , (int, float) ):
raise ValueError("""maclaurin_sin... | 38 |
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_video_inputs
if is_torch_available():
import torch
i... | 287 | 0 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class UpperCAmel... | 450 |
from __future__ import annotations
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
create_state_space_tree(SCREAMING_SNAKE_CASE , [] , 0 , [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMIN... | 450 | 1 |
import numpy as np
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 623 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
re... | 623 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
... | 408 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_devic... | 408 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__A : Optional[Any] = logging.get_logger(__name__)
__A : Dict = "T5Config"
class l... | 27 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 1 |
class lowercase ( SCREAMING_SNAKE_CASE__ ):
pass
class lowercase ( SCREAMING_SNAKE_CASE__ ):
pass
class lowercase :
def __init__( self):
lowercase = [
[],
[],
[],
]
... | 716 |
# Copyright 2022 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 applicab... | 633 | 0 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 600 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__... | 600 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase = [
'''word_e... | 713 |
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 ...schedulers import DDPMScheduler
from ...utils i... | 102 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_... | 638 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTes... | 638 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE... | 706 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ = head.next, head
while fast and fast.next:
UpperCAmelCase_ = ... | 14 | 0 |
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