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 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 t... | 50 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE_ = """examples/"""
SCREAMING_SNAKE_CASE_ = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 237 | 0 |
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
... | 719 |
import unittest
import numpy as np
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case = None , ) -> np.ndarray:
_UpperCAmelCase = np.shape(snake_case )
_UpperCAmelCase = np.shape(snake_case ... | 175 | 0 |
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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
""... | 651 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distribut... | 651 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : str = logging.get_logger(__name__)
a__ : str = {
'facebook/data2vec-te... | 713 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase ( *__A , __A = None , __A=True , __A=2 ) -> int:
'''simple docstring'''
from .. import __version__
Upp... | 223 | 0 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_ava... | 384 |
'''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 _UpperCamelCase ( __UpperCamelCase ) -> str: # pic... | 384 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipe... | 56 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _lowercase ( __lo... | 56 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_de... | 663 | """simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __A ( SCREAMING_... | 663 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAM... | 147 |
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 diffusers.schedulers.scheduling_ddpm ... | 147 | 1 |
def lowerCAmelCase_ ( lowercase: list[int] , lowercase: list[int] ) -> None:
'''simple docstring'''
_UpperCamelCase: List[str] = len(lowercase )
print('''The following activities are selected:''' )
# The first activity is always selected
_UpperCamelCase: L... | 264 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 264 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]... | 156 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoic... | 156 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( lowercase_ : list , lowercase_ : int | None = None , lowercase_ : int | None = None ) -> None:
'''simple docstring'''
if start is None:
lowercase =0
if end is None:
... | 145 |
'''simple docstring'''
import argparse
import json
import subprocess
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowercase =[]
lowercase =(
f'curl -H "Accept: application/vnd.github+... | 145 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _a ( *UpperCAmelCase__ , UpperCAmelCase__ = None , UpperCAmelCase__=True , UpperCAmelCase__=2 ) -> List[str]:
from .. import __version... | 482 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCas... | 482 | 1 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
Train... | 708 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffuse... | 120 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 175 |
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 __UpperCAmelCase ( __a : ... | 14 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a ( UpperCAmelCase ):
def __init__( self , A_ , A_ = None ... | 707 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> Optional[int]:
_UpperCAmelCase : Optional[Any] = os.path.join(args.tf_model_dir , "pa... | 467 | 0 |
import math
import unittest
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < ... | 241 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__UpperCAmelCase : Optional[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConf... | 241 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterM... | 513 |
'''simple docstring'''
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,... | 513 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCAmelCase_( __snake_case ):
'''simple docstring'''
__lowercase : str = '''Speech2TextFeatureExtractor'''
__lowercase : Optional[int] ... | 565 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
f... | 581 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _SCREAMING_SNAKE_CASE ( __a ):
@staticmethod
@abstractmethod
def A_ ( lowerCamelCase ):
raise NotImplementedError()
@abstractmethod
def A_ ( self ):
raise NotImpleme... | 706 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__magic_name__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the reference c... | 530 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
... | 240 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 714 |
'''simple docstring'''
def A (__lowerCamelCase :int = 100 ):
_lowerCAmelCase = n * (n + 1) * (2 * n + 1) / 6
_lowerCAmelCase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 162 | 0 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
cla... | 3 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
#... | 23 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybrid... | 716 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 0 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a = logging.getLogger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring... | 7 |
"""simple docstring"""
def lowercase (_snake_case ) -> str:
'''simple docstring'''
if isinstance(_snake_case ,_snake_case ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_snake_case ,_snake_case ):
raise TypeError("'str' object can... | 505 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Pa... | 715 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available(... | 499 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def UpperCamelCase( __Upper... | 171 |
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_d... | 23 | 0 |
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
... | 167 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuratio... | 167 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.... | 432 |
'''simple docstring'''
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... | 432 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__Upper... | 34 | import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils i... | 34 | 1 |
from __future__ import annotations
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : list[str] | None = None ):
"""simple docstring"""
snake_case__ : Optional[int] =word_bank or []
# create a table
snake_case__ : ... | 381 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase__ = '''src/diffusers'''
# Matches is_xxx_available()
lowerCamelCase__ = re.compile(r''... | 381 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
... | 721 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 402 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 23 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSe... | 16 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
fr... | 708 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokeniz... | 431 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 553 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( UpperCamelCase ):
def __init__( self : str , UpperCAmelCase ... | 553 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN,... | 591 | from string import ascii_uppercase
lowercase = {str(ord(c) - 5_5): c for c in ascii_uppercase}
def lowerCamelCase_ ( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
if isinstance(UpperCamelCase__, UpperCa... | 591 | 1 |
'''simple docstring'''
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
a__ : Dict = logging.get_logger(__name__)
a__ : Tu... | 51 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 416 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import fl... | 703 | 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
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : ... | 164 | 0 |
def _a ( lowercase__ : str , lowercase__ : bool = False ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = f'''Expected string as input, found {type(lowercase__ )}'''
raise ValueError(l... | 85 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase , __lowercase ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
raise Exception("Rate of interes... | 330 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_ba... | 706 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
... | 240 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_s... | 20 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments... | 20 | 1 |
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_callba... | 409 |
def a ( ):
'''simple docstring'''
lowercase_ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowercase_ = 6
lowercase_ = 1
lowercase_ = 1_901
lowercase_ = 0
while year < 2_001:
day += 7
... | 409 | 1 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__a: Union[str, Any] = 2_99_79_24_58
# Symbols
__a , __a , __a , __a: List[Any] = symbols("""ct x y z""")
def __UpperCamelCase ( UpperCAmelCase ):
... | 152 | '''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 import floats_... | 152 | 1 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
... | 709 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : List[Any] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
... | 288 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( ... | 143 |
'''simple docstring'''
def A_ ( snake_case = 600851475143 ):
try:
SCREAMING_SNAKE_CASE:str = int(snake_case )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater tha... | 143 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __lowerCamelCase ( _UpperCamelCase : Optional[Any] ):
'''simple docstring'''
create_state_space_tree(_UpperCamelCase , [] , 0 )
def __lowerCamelCase ( _UpperCam... | 717 | '''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __lowerCamelCase ( _UpperCamelCase : np.ndarray , _UpperCamelCase : tuple[int, int] , _UpperCamelCase : tuple[int, int] , _UpperCamelCase : bool , ):
'''simple docstring'... | 43 | 0 |
'''simple docstring'''
import numpy as np
import datasets
A : Optional[Any] = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance... | 128 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase__ ( lowerCamelCase : list ):
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase ) / len(lowerCamelCase )
if __name__ == "__main__":
import doctest
doc... | 128 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 720 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ : int = {"configuration_vit": ["VIT_P... | 532 | 0 |
def __lowerCAmelCase ( _UpperCamelCase ) -> bool:
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
lowerCamelCase__: Optional[int] = sorted(st... | 306 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( A__ ):
@staticmethod
@abstractmethod
def lowerCamelCase_ ( __a : ArgumentParser ):
'''simple docstring'''
r... | 306 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Any =... | 453 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_envir... | 453 | 1 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : str ) -> Tuple:
return " ".join(
"""""".join(word[::-1] ) if len(A__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey woll... | 287 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 254 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transfo... | 701 |
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : float , _lowerCamelCase : float ):
return round(float(moles / volume ) * nfactor )
def __magic_name__ ( _lowerCamelCase : float , _lowerC... | 63 | 0 |
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 __lowerCamelCase ( lowerCamelCase__ : Any ):
'''simple docstring'''
lowerCamelCase = tes... | 457 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase : Dict = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_to... | 372 | 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... | 631 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowercase ( _lowercase ):
a = """"""
a = (
None ... | 631 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Optional[Any] = 'SpeechT5FeatureExtractor'
_snake_case : Tuple = 'SpeechT5Tokeniz... | 98 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase ( lowerCAmelCase : str ):
"""simple docstring"""
def wrapper(*lowerCAmelCase : ... | 561 | 0 |
'''simple docstring'''
__A =8.3_1_4_4_5_9_8
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("""Molar mass cannot be le... | 113 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _snake_case ( yaml.SafeLoader ):
def snake_case__ ( self , _lowerCamelCase):
UpperCAmelCase__ : Dict... | 113 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class UpperCAmelCase_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=1 , ... | 173 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils impo... | 173 | 1 |
"""simple docstring"""
from statistics import mean, stdev
def lowercase_ ( _lowercase : list , _lowercase : int = 3 ):
'''simple docstring'''
UpperCAmelCase : str = min(_lowercase )
UpperCAmelCase : int = max(_lowercase )
# n... | 292 |
"""simple docstring"""
import os
def lowercase_ ( ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = os.path.join(os.path.dirname(_lowercase ) , "num.txt" )
with open(_lowercase ) as file_hand:
return str(sum(int(_lowercase ) for line i... | 292 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNot... | 314 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resol... | 632 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : List[str] = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class ... | 159 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Tuple = {
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerCon... | 159 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE ( lowercase_ : Callable[[int | float], int | float] , lowercase_ : int | float , lowercase_ : int | float , lowercase_ : int = 100 ,... | 588 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 588 | 1 |
# 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 requi... | 714 |
from math import sqrt
def a ( _UpperCAmelCase : int ):
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
__UpperCAmelCase : Optional[int] ... | 241 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common... | 590 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 272 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
snake_case = (UnCLIPScheduler,)
def __UpperCAmelCase ( self : str , **SCR... | 258 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"""xlm-mlm-en-204... | 258 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _UpperCAmelCase :
def __init__( self : Optional[int] , lowercase_ : Optional[int]=2 , lowercase_ : List[Any]=3 , lo... | 123 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataColla... | 150 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
clas... | 720 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ = "cpu" ,lowerCAmelCase__ = None ):
A__ = torch.load(lowerCAmelCase__ ,map_locat... | 554 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
c... | 55 | """simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> list:
'''simple docstring'''
if len(__lowerCAmelCase ) != 2 or len(a[0] ) != 2 or len(__lowerCAmelCase ) != 2 or ... | 530 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_ba... | 155 |
import json
import sys
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Optional[int]:
with open(SCREAMING_SNAKE_CASE__ , encoding="""utf-8""") as f:
__snake_case: Tuple = json.load(SCREAMING_SNAKE_CASE__)
__snake_case: Union[str, ... | 155 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[Any] = logging.get_logger(__name... | 278 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
snake_case__ : int = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ (a__ ):
'''simple docstrin... | 278 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class lowerCamelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self , *... | 708 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowercase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
SCREAMING_SNAKE_CASE__ = list(lowerCamelCase_ )
SCREAMING_SNAKE_C... | 112 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-large"... | 337 |
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__UpperCAmelCase =logging.getLogger(__name__)
if is_torch_tpu_avai... | 337 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 1_00 ):
'''simple docstring'''
lowerCamelCase_ = set()
lowerCamelCase_ = 0
lowerCamelCase_ = n + 1 # maximum limit
for a in range(2 , a_ ):
for b in range(2... | 705 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common impo... | 651 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _A( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_to_config
def __init... | 239 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/spiece.model''')
@require_sentenc... | 463 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[int]:
"""simple docstring"""
return [ord(SCREAMING_SNAKE_CASE_ ) - 96 for elem in plain]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"... | 700 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6... | 0 | 0 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concate... | 156 |
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dis... | 156 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 324 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
... | 324 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condi... | 46 |
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, fou... | 42 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowerCamelCase_ : str = '''\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\... | 705 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class _SC... | 265 | 0 |
"""simple docstring"""
from typing import List
import numpy as np
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : int = {key: len(__UpperCamelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCamelCase , __UpperCamelCase )}
... | 76 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowercase__( )-> Optional[int]:
"""simple docstring"""
raise RuntimeError("CUDA ou... | 138 | 0 |
"""simple docstring"""
from math import pow, sqrt
def _lowerCamelCase ( *UpperCAmelCase_ : float ) -> bool:
"""simple docstring"""
A__ = len(UpperCAmelCase_ ) > 0 and all(value > 0.0 for value in values )
return resul... | 562 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""BAAI/AltCLIP""": """https:/... | 562 | 1 |
"""simple docstring"""
from collections import namedtuple
A = namedtuple("""from_to""", """from_ to""")
A = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": f... | 77 |
import logging
from transformers import PretrainedConfig
lowerCamelCase__ = logging.getLogger(__name__)
lowerCamelCase__ = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}
class S... | 225 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kand... | 702 |
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def snake_case_ ( self , lowerCAmelCase__):
raise NotImplementedEr... | 248 | 0 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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_modelin... | 251 | '''simple docstring'''
def __lowerCAmelCase ( a_ = 1 , a_ = 1000 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Optional[int] = 0
for div... | 251 | 1 |
'''simple docstring'''
class snake_case_ :
"""simple docstring"""
def __init__( self , UpperCamelCase):
lowerCamelCase__ = n
lowerCamelCase__ = [None] * self.n
lowerCamelCase__ = 0 # index of the first element
... | 426 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class snake_case_ ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ... | 426 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowercase : List[Any] =Lock()
def a__ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , ... | 54 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_... | 321 | 0 |
'''simple docstring'''
_lowerCAmelCase : str = {str(digit): digit**5 for digit in range(1_0)}
def _A ( snake_case__ : Optional[int] ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCamelCase ) )
def _A ( ):
return sum(
number
... | 716 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCAmelCase : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
pars... | 694 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from tra... | 401 |
'''simple docstring'''
from __future__ import annotations
def __lowercase (_SCREAMING_SNAKE_CASE :list[int] ):
if not nums:
return 0
SCREAMING_SNAKE_CASE : Tuple = nums[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for num in nums[1:]:
SC... | 507 | 0 |
"""simple docstring"""
import math
import sys
def _A ( _a : str ):
"""simple docstring"""
A = """"""
try:
with open(_a , """rb""" ) as binary_file:
A = binary_file.read()
... | 717 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCamelCase__ ( SCREAMIN... | 255 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
'processing_mgp_s... | 466 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a_ ( snake_case_ ):
'''si... | 314 | 0 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 req... | 336 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
'''configuration_clip''': [
... | 336 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationT... | 163 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __magic_name__ ( datasets.BeamBasedBuilder ):
def lowerCAmelCase ... | 163 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : Optional[Any] ):
stooge(_snake_case , 0 , len(_snake_case ) - 1 )
return arr
def _snake_case ( _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[Any] ... | 637 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 1 |
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_warmup, set_seed
from accelerate import Accelerator, Distr... | 547 | def lowerCAmelCase__ ( a__ ) ->int:
'''simple docstring'''
if not isinstance(a__ , a__ ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCamelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
... | 547 | 1 |
from __future__ import annotations
def __lowercase ( snake_case ):
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(snake_case ) / len(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 180 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = """▁"""
snake_case = {"""vocab_fil... | 424 |
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,
OPENAI... | 233 | 0 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoCo... | 523 |
"""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 _lowerCamelCase ( self ) -> List[str]:
... | 523 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-t... | 34 | """simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCAmelCase_ (_SCREAMING_SNAKE_CASE :Optional[Any] ) -> List[Any]:
# This defines a "chinese character" as anything in... | 473 | 0 |
"""simple docstring"""
from __future__ import annotations
def A__ ( UpperCamelCase ):
if len(UpperCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i in nums ):
raise ValueError("All va... | 524 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
A = sorted(string.lower() )
return len(UpperCamelCase ) == len(set(UpperCamelCase ) )
if __nam... | 524 | 1 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class _snak... | 413 |
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.dataset_dict import IterableDatasetDict
from ... | 413 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case__( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ["""flax"""]
def __init__( self , *__lowercase , **__lowercase ) ... | 619 |
from __future__ import annotations
from math import pi
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
... | 619 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.