code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffuser... | 42 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 0 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 369 |
from __future__ import annotations
from collections import deque
class lowerCAmelCase :
def __init__( self :List[Any] , _lowercase :list[str] ):
'''simple docstring'''
lowercase__ = []
self.adlist.append(
{"value": "", "next_states"... | 201 | 0 |
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 UpperCamelCase ( lowerCAmelCase__ ):
''... | 101 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require... | 101 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if i... | 165 | from math import pi
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 165 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase( __UpperCamelCase : int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(__UpperCamelCase : float ,__UpperCamelCase :... | 103 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hu... | 106 | '''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __A ( unittes... | 106 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 5 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( __snake_case = "https://www.worldometers.info/coronavirus" ) -> dict:
"""simple docstring"""
_lowercase =BeautifulSoup(requests.get(__snake_case ).text , '''html.parser''' )
_lowerc... | 5 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowe... | 152 |
import string
def _lowerCAmelCase ( A__: str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
Upper... | 152 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Option... | 274 |
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
... | 310 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class lowercase ( snake_case__):
"""simple docstring"""
a__ : str = fiel... | 277 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : ... | 277 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Fo... | 91 |
from __future__ import annotations
from typing import Generic, TypeVar
lowerCamelCase_ = TypeVar('''T''')
class __A( Generic[T] ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = data
UpperCamelCase__ = self
... | 244 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_war... | 220 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
Ber... | 220 | 1 |
import numpy as np
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ = 1e-12 , lowercase__ = 100 , ):
assert np.shape(lowercase__ )[0] == np.shape(lowercase__ )[1]
# Ensure proper dimensionality.
assert np.shape(lowercase... | 9 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 0 |
"""simple docstring"""
from math import pow, sqrt
def __UpperCAmelCase ( *UpperCAmelCase_ : Dict ) -> Optional[int]:
'''simple docstring'''
__snake_case : Dict = len(A__ ) > 0 and all(value > 0.0 for value in values )
return result
def __U... | 358 | """simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> str:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError('Un... | 95 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase_( SCREAMIN... | 37 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: list[int] , __UpperCAmelCase: int ) -> list[int]:
UpperCamelCase__ : Optional[int] = 0
UpperCamelCase__ : Tuple = len(__UpperCAmelCase ) - 1... | 201 | 0 |
'''simple docstring'''
import os
import string
import sys
__lowerCAmelCase = 1 << 8
__lowerCAmelCase = {
"""tab""": ord('\t'),
"""newline""": ord('\r'),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
... | 366 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 270 | 0 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
A_ : List[Any] = 2_048
A_ : int = 4_096
A_ : Optional[int] = 42
A_ : Optional[Any] = os.environ.pop("PROCESS_TRAIN", "false")
A_ ... | 165 |
"""simple docstring"""
def A ( snake_case__ = 10_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 1, 1
SCREAMING_SNAKE_CASE__ = 2
while True:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_... | 165 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ ( snake_case_ ):
'''si... | 356 |
from functools import lru_cache
@lru_cache
def a_ ( __lowercase : int ) -> int:
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 130 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowerCAmelCase__ : int = mf_knapsack(i - 1 , A_ , A_ , A_ )
else:
lowerCAmelCas... | 106 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acc... | 106 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCamelCase( lowercase_ ) -> ... | 365 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_to... | 152 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[Any] =... | 152 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_to... | 363 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a : str ... | 82 | 0 |
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 import ModelTesterMixin, ids_tensor
fro... | 277 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowercase_ (A : List[str] ):
snake_case_... | 277 | 1 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,... | 170 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_commo... | 170 | 1 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorStat... | 220 |
"""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 _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] ):... | 220 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning t... | 360 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :list ) -> Dict:
_enforce_args(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if n == 0:
return 0
__lowerCAmelCase : Union[str, Any] = float("""-inf""" )
for i in range(1 , n + 1 ):... | 232 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_availab... | 50 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 95 | 0 |
_lowerCamelCase : List[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : int... | 206 | 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,
to_channel_dimension_forma... | 206 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedule... | 71 |
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.huggingface ... | 270 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse... | 365 | from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase ):
'''simple docstring'''
__A = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any]) -> Opti... | 63 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : Dict ) -> List[Any]:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2,... | 47 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCAmelCase__ = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''s... | 130 | 0 |
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__":
snake_case : List[str] = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
... | 281 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise... | 281 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTrans... | 37 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImage... | 34 | 0 |
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_ = {
'''facebook/convnextv2-tiny-1k-224''': '''htt... | 355 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpToken... | 82 | 0 |
"""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() and is_transformers_version('''>=''', '''4.25.0''')):
raise Optiona... | 64 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int ) -> int:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Input value must be an 'int' type" )
__lowerCamelCase : Dict = 0
... | 64 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_lowercase : Any =logging.get_logger(__name__)
def l... | 170 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCAmelCase_ ( _lowercase : float , _lowercase : float , _lowercase : bool = False) -> list[float]:
"""sim... | 170 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> int:
if n == 1 or not isinstance(a_ , a_):
return 0
elif n == 2:
return 1
else:
__a : Optional[int] = [0, 1]
for i in range(2 , n + 1):
sequ... | 188 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 188 | 1 |
from manim import *
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
_a : Dict = Rectangle(height=0.5 , width=0.5 )
_a : Tuple = Rectangle(... | 235 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=UpperCamelCase ):
snake_case_ = ['onnx']
def __init__( self : str , *snake_case : Optional[int] , **... | 350 |
"""simple docstring"""
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , snake_case : int ):
'''simple docstring'''
A__ : List[Any] = order
# a_{0} ... a_{k}
... | 296 | 0 |
'''simple docstring'''
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__":
lowerCamelCase :Any = pd.read_csv('''sample_data.csv''', ... | 206 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class _lowerCAmelCase :
def __init__(self ):
A_ : int = {}
def _a (self , lowercase , lowercase , lowercase=1 ... | 206 | 1 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
a_ = "path-to-your-trained-model"
a_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
a_ = "A photo of sks dog in a bucket"
a_ ... | 371 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "ma... | 163 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
d... | 294 |
'''simple docstring'''
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 ... | 63 | 0 |
"""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/LICENSE... | 234 |
"""simple docstring"""
def _a ( _snake_case = 6008_5147_5143 ):
"""simple docstring"""
try:
UpperCAmelCase = int(_snake_case )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" ... | 234 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( snake_case ):
UpperCamelCase__ ... | 281 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
snake_case : Dict = re.compile(R"\b(a|an|the)\b", re.UNICODE)
snake_case : Optional[int] = None
def lowerCAmelCase_ ( ) -> Union[str, Any]:
'''simp... | 281 | 1 |
from __future__ import annotations
import numpy as np
def UpperCamelCase ( _A ):
"""simple docstring"""
return np.maximum(0, lowerCAmelCase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 361 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class snake_case__ ( tf.keras.layers.Layer ):
def __init__( self , lo... | 138 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=__lowercase ):
lowerCamelCase : Tuple = ['''flax''', '''transformers''']
def __init__( self : Optional[Any] , *UpperCAmelCase__ : ... | 4 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from ... | 34 | 0 |
from __future__ import annotations
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self , A ) -> None:
snake_case : Union[str, Any] = num_of_nodes
snake_case : list[list[int]] = []
... | 176 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[str]:
return ConvertCommand(
args.model_type ,args.tf_checkpoint ,args.pytorch_dump_output ,args.conf... | 176 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
A_ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}... | 64 |
"""simple docstring"""
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 (
HPSearchBack... | 64 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 356 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
... | 3 | 0 |
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 import DUMMY_UNKNOWN_IDENTI... | 188 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''... | 188 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import Templ... | 125 |
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''': [
'''L... | 125 | 1 |
_UpperCAmelCase : Union[str, Any] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :List[Any] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 236 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCas... | 236 | 1 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 354 | from math import isqrt
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 204 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 18 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__A =datasets.utils.logging.get_logger(__name__)
class _snake_case ( folder_based_builder.FolderBasedBuilderConf... | 163 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Dict = {
'configuration_speech_to_text': ['SPEECH_TO_TEX... | 364 |
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.TestC... | 120 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCamelCase__ = ''
lowerCamelCase__ = ''
lowerCamelCase__ = ''
lowerCamelCase__ = 1 # (0 is vertical, 1 is horizontal)
def __lowerCAmelCas... | 234 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
lowerCamelCase__ = logging.get_logger(__n... | 234 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Tuple = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json"... | 369 | '''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelC... | 274 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_call... | 8 |
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 : Tuple = collections.namedtuple('''_Datasets''', ['''train''', '... | 138 | 0 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: str ):
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" )
_Upp... | 17 | 1 |
import os
import string
import sys
__snake_case = 1 << 8
__snake_case = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FL... | 176 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
'''simple docstring'''
def update_area_of_max_square(UpperCamelCase_ , UpperCamelCase_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
SCREAMING_SN... | 176 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase = 600851475143 ):
'''simple docstring'''
try:
UpperCAmelCase = int(lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or ca... | 355 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
_... | 248 | 0 |
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class A__(__snake_case ):
"""simple docstring"""
_A : List[Any] = '''Salesforc... | 248 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_... | 3 | 0 |
"""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 _lowerCAmelCase ( lower... | 353 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase = 4 ):
'''simple docstring'''
UpperCAmelCase = abs(lowerCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase )] for y in ra... | 248 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 125 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor... | 125 | 1 |
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 (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSol... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
... | 83 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : str = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available()... | 204 | 0 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _a ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = JukeboxTokenizer
UpperCamelCase__ = {
"""artist""": """Z... | 326 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 | 1 |
from math import pi, sqrt, tan
def _UpperCAmelCase (UpperCamelCase__ : float ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def _UpperCAmelCase (UpperCamelCase__ : float ... | 11 |
'''simple docstring'''
class __snake_case :
"""simple docstring"""
def __init__( self : int , lowerCamelCase : int , lowerCamelCase : int=None , lowerCamelCase : int=None ) -> str:
lowerCAmelCase_ : str... | 120 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A__ ( lowerCamelCase ) -> int:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode... | 359 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigT... | 223 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError("Undefined for non-inte... | 95 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCAmelCase_ ( __lowerCamelCase : List[str] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" ,set() )
@pytest.... | 223 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : int = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class ... | 110 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from ... | 110 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.j... | 17 |
"""simple docstring"""
from math import sqrt
def _A ( UpperCamelCase_ : int) -> int:
'''simple docstring'''
__lowercase = 0
for i in range(1, int(sqrt(UpperCamelCase_) + 1)):
if n % i == 0 and i != sqrt(UpperCamelCase_):
total += i + n // i
eli... | 17 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Conditional... | 354 | """simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =FileLock(str(tmpdir / '''foo.lock''' ) )
__UpperCamelCase =F... | 85 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoP... | 98 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class A__(a_ ):
"""sim... | 248 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import... | 13 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from trans... | 13 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructu... | 317 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
... | 317 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class snake_case__:
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : Union[str, Any] = data
... | 370 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split("." )[n_sha... | 121 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.d... | 84 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 0 |
"""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_com... | 202 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __snake_case ( SCREAMING_SNAKE_CASE__ : list[Any] ) -> None:
'''simple docstring'''
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def __snake_... | 202 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : str =JukeboxTokenizer
UpperCAmelCase_ : Tuple ={... | 326 |
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_optuna,
defau... | 326 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_util... | 317 |
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = ... | 317 | 1 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase : Any =datasets.logging.get_logger(__name__)
_UpperCAmelCase : Any ="""\
@InProceedings{moosavi2019minimum,
author ... | 262 |
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, logging
if is_sentencepiece_... | 262 | 1 |
def _UpperCAmelCase (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_steps}"
if number_of_steps == 1:
return ... | 350 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
fr... | 159 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 110 |
from __future__ import annotations
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: str , UpperCamelCase_: str ) -> List[Any]:
"""simple docstring"""
lowercase__ , lowercase__ =... | 110 | 1 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
SCREAMING_SNAKE_CASE__:Tuple = "src/transformers"
SCREAMING_SNA... | 368 | """simple docstring"""
SCREAMING_SNAKE_CASE__:Any = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": ""... | 268 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowercase__ = logging.get_logger(__name__)
class __snake_case :
def __init__( ... | 290 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCamelCase_( snake_case : Tuple ):
'''simple docstring'''
snake_case_ = FileLock(str(tmpdir / "foo.lock" ) )
snake_cas... | 85 | 0 |
from __future__ import annotations
import math
def A_ ( _UpperCAmelCase ):
if num <= 0:
SCREAMING_SNAKE_CASE_: Union[str, Any] = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Optio... | 371 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : int
_UpperCAmelCase : int
class __lowercase :... | 127 | 0 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCAmelCase : str = l... | 13 |
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 __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transform... | 365 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
... | 322 | 0 |
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = (boundary[1] - boundary[0]) / steps
_UpperCAmelCase = boundary[0]
_UpperCAmelCase = boundary[1]
_UpperCAmel... | 39 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
UpperCAmelCase__ : Union[str, Any] = TypeVar('T')
class UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
__UpperCamelCase : deque[T] # Cache store of ke... | 121 | 0 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def __a ( UpperCAmelCase ) ->List[str]:
"""simple docstring"""
A = test_... | 337 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : List[Any] ):
A = str(id_ )
A = None
A = None
A ... | 337 | 1 |
"""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, loggin... | 132 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: complex , __UpperCAmelCase: str = "x" , __UpperCAmelCase: float = 10**-10 , __UpperCAmelCase: int = 1 , ) -> com... | 201 | 0 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock im... | 346 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCAmelCase_ : List[str] = TypeVar('T')
lowerCAmelCase_ : Dict = TypeVar('U')
class __SCREAMING_SNAKE_CASE (Generic[T, U] ):
... | 346 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_... | 317 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from dat... | 317 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration... | 355 |
# 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
#
# Unl... | 247 | 0 |
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : List[str]=False ) -> List[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and isinstance(lowerCAmelCase_ , lowerCAmelCas... | 281 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 159 | 0 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = prime_factors(__UpperCame... | 246 |
'''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_sentencepi... | 246 | 1 |
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
snake_case : Optional[Any] = logging.getLogger()
@unittest.skip('Temporarily disable... | 94 |
"""simple docstring"""
from torch import nn
def snake_case ( A__ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {ac... | 268 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposit... | 207 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils imp... | 207 | 1 |
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