code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( snake_case_ ):
'''simple docstring'''
def __ini... | 117 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipeli... | 184 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ : Tuple = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CO... | 375 |
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__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : str = {
"""vocab_file""": """vocab.json""",
... | 568 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 0 |
# Imports
import numpy as np
class lowerCamelCase_ :
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None ) -> Any:
"""simple docstring"""
self.set... | 147 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, 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 Confi... | 431 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",
"""XCLIPV... | 171 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Optional[Any] ) -> Dict:
if not is_accelerate_available():
return method
SCRE... | 443 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _lowerCamelCase ( __a ):
SCREAMING_SNAKE_CASE_ = args.pruning_method
SCREAMING_SNAKE_CASE_ = args.threshold
SC... | 626 |
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 ConfigTe... | 33 | 0 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transform... | 29 |
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 D... | 33 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> list:
"""simple docstring"""
_UpperCamelCase = len(__lowerCAmelCase )
_UpperCamelCase = [[0] * n for i in range(__lowerCAmelCase ... | 19 |
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.utils import... | 33 | 0 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import Gradi... | 117 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 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 transformers imp... | 184 |
import inspect
import unittest
from transformers import BitConfig
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 import BackboneTesterMixin
from ... | 33 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : List[Any] = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/... | 375 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEX... | 568 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 147 |
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, '''max_num_jobs... | 33 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
Disti... | 431 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 0 |
A__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def UpperCamelCase( __UpperCamelCase : int ):
lowerCAmelCase_ : List[Any] = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
lowerCAmelCase_ ... | 171 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowercase = logging.get_logger(__name__)
class lowercase_ ( snake_case_ ):
def __init__( self , *__A , **__A ) -> Any:
... | 443 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 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_common... | 626 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Deb... | 29 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 0 |
"""simple docstring"""
_a = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_versio... | 19 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Tuple = {
"""configuration_roberta""": ["""... | 33 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resi... | 117 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
... | 184 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_ : List[str] = logging.get_logger(__name__)
class snake_case_ ( snake_case_ ):
'''simple docstring'''
def __init__( self : Dict , ... | 375 |
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__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase_ ( enum.Enum ... | 568 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 0 |
def _lowercase ( a__ : int ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowercase ( a__ : str = 1_00 ) -> int:
"""simple docstring"""
_UpperCamelCase = 1... | 147 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 0 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _A ):
lowerCAmelCase_ , lowerCAmelCase_ = analyze_text(__lowerCAmelCase )
lowerCAmelCase_ = list(''' ''' + ascii_lowercase ... | 431 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
A__ : Tuple = """<<<<<<< This should probably be modified because it mentions: """
A__ : str = """====... | 171 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM... | 443 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 0 |
"""simple docstring"""
def _lowerCamelCase ( __a = 100 ):
SCREAMING_SNAKE_CASE_ = set()
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = n + 1 # maximum limit
for a in range(2, __lowerCAmelCase ):
for b in range(2, __lowerCAmelCase ):
SCRE... | 626 |
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 ConfigTe... | 33 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A_ = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}
t... | 29 |
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 D... | 33 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""Jukebox... | 19 |
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.utils import... | 33 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( snake_case_ ):
'''simple docstring'''
__snake_case = 'en... | 117 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 184 |
import inspect
import unittest
from transformers import BitConfig
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 import BackboneTesterMixin
from ... | 33 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 375 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase : List[Any] = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CONFIG_AR... | 568 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_v... | 147 |
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, '''max_num_jobs... | 33 | 0 |
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = len(__lowerCAmelCase ) + 1
lowerCAmelCase_ = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of len... | 431 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEX... | 33 | 0 |
A__ : Optional[int] = """Input must be a string of 8 numbers plus letter"""
A__ : List[str] = """TRWAGMYFPDXBNJZSQVHLCKE"""
def UpperCamelCase( __UpperCamelCase : int ):
if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
lowerCAmelCase_ : List[... | 171 |
import math
class __magic_name__ :
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:list[list[float]] , _a:list[int] ):
snake_case__ = 0.0
snake_case__ = 0.0
for i in range(len(_a ) ):
... | 33 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Any ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
SCREAMING_SNAK... | 443 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTok... | 626 |
lowerCamelCase__ : List[str] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
sn... | 33 | 0 |
"""simple docstring"""
import math
class __lowerCamelCase :
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase ):
lowerCamelCase_ = 0.0
lowerCamelCase_ = 0.0
for i in range(len(_a ) ):
da += math.pow((sample[... | 29 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( __snake_case, __snak... | 19 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Tuple = {
"""configuration_roberta""": ["""... | 33 | 0 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTester... | 117 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface imp... | 184 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available()... | 33 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case , snake_case , ) -> None:
__lowercase = len(__lowerCAmelCase )
# If row is equal to the size of the board it means there are a... | 375 |
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__ : List[Any] = logging.get_logger(__name__)
lowerCam... | 33 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
fr... | 568 |
import os
import sys
lowerCamelCase__ : Optional[int] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering... | 33 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase_ ( snake_... | 147 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : str = (CMStochasticIterativeScheduler,)
__lowercase :... | 33 | 0 |
import qiskit
def __UpperCamelCase ( _A = 2 ):
lowerCAmelCase_ = qubits
# Using Aer's simulator
lowerCAmelCase_ = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
lowerCAmelCase_ = qiskit.Quan... | 431 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCAmelCase )
if __name__ == "__main__":
... | 33 | 0 |
import functools
def UpperCamelCase( __UpperCamelCase : List[str] ,__UpperCamelCase : List[Any] ):
# Validation
if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase ,__lowerCAmelCase ) for day in days ):
raise ValueError('''... | 171 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = set()
snake_case__ = 0
snake_case__ = n + 1 # maximum limit
for a in range(2 , __lowerCAmelCase ):
for b in range(2 , __lowerCAmelCase ):
snake_case__ = a*... | 33 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : List[Any] ) -> int:
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_... | 443 |
from copy import deepcopy
class __magic_name__ :
'''simple docstring'''
def __init__( self:int , _a:list[int] | None = None , _a:int | None = None ):
if arr is None and size is not None:
snake_case__ = size
snake_case__ = ... | 33 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( __a, __a, __a, ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot b... | 626 |
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 ConfigTe... | 33 | 0 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ = [0] * no_of_processes
lowerCamelCase_ = [0] * no_of_processes
# Initialize remaining_time to waiting_ti... | 29 |
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 D... | 33 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class _UpperCAmelCase( snake_case_ ):
... | 19 |
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.utils import... | 33 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCa... | 117 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer... | 184 |
import inspect
import unittest
from transformers import BitConfig
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 import BackboneTesterMixin
from ... | 33 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE_ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Optional[int] = {
"""t5-small""": """https://hu... | 375 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 | 0 |
from collections.abc import Sequence
def lowerCAmelCase__ ( _a : int , _a : Optional[Any] = False ):
if not arr:
return 0
snake_case_ : Tuple = 0 if allow_empty_subarrays else float("-inf" )
snake_case_ : List[str] = 0.0... | 568 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase__ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""w... | 33 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTr... | 147 |
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, '''max_num_jobs... | 33 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {'configuration_opt': ['OPT_PRETRAINED_... | 34 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers... | 34 | 1 |
"""simple docstring"""
import operator
def __snake_case ( _lowercase ,_lowercase = False ,_lowercase = None ):
"""simple docstring"""
UpperCamelCase = operator.lt if reverse else operator.gt
UpperCamelCase = solution or []
if not arr:
... | 34 |
"""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 imp... | 34 | 1 |
"""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_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE_ = logging... | 34 |
"""simple docstring"""
import operator
def __snake_case ( _lowercase ,_lowercase = False ,_lowercase = None ):
"""simple docstring"""
UpperCamelCase = operator.lt if reverse else operator.gt
UpperCamelCase = solution or []
if not arr:
... | 34 | 1 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = [0] * len(_lowercase )
for i in range(1 ,len(_lowercase ) ):
# use last results for better performance - dynamic programming
UpperCamelCase... | 34 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value... | 34 | 1 |
"""simple docstring"""
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
... | 34 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class snake_case_ ( low... | 34 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'nielsr/canine-s': 2048,
}
# Unicode de... | 34 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
SCREA... | 34 | 1 |
"""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():
imp... | 34 |
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE_ = ... | 34 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( _lowercase ):
"""simple docstring"""
return len(set(_lowercase ) ) == len(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 |
"""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
SCREAMING_SNAKE_C... | 34 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
... | 34 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case_ ( ... | 34 | 1 |
"""simple docstring"""
from manim import *
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def UpperCAmelCase__ ( self) -> Union[str, Any]:
UpperCamelCase = Rectangle(height=0.5 , width=0.5)
UpperCamelCase ... | 34 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]... | 34 | 1 |
"""simple docstring"""
def __snake_case ( _lowercase = 6008_5147_5143 ):
"""simple docstring"""
try:
UpperCamelCase = int(_lowercase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if... | 34 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( se... | 34 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 34 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = [0 for i in range(len(_lowercase ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase , UpperCamelCase = 0, 0
... | 34 | 1 |
"""simple docstring"""
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
SCREAMING_S... | 34 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 34 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __snake_case ( _l... | 34 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _lowercase ,_lowercase ,_lowercase ):
"""simple docstring"""
UpperCamelCase = 0
if start < end:
UpperCamelCase ... | 34 | 1 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 34 |
"""simple docstring"""
import os
import sys
import unittest
SCREAMING_SNAKE_CASE_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files... | 34 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vis... | 34 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __snake_case ( _lowercase ):
"""simple docstring"""
if "cls_token" in name:
UpperCamelCas... | 34 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
tra... | 34 |
"""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 __snake_case ( ):
"""simple docstring"""
raise RuntimeError('''CUDA out o... | 34 | 1 |
"""simple docstring"""
import os
import unicodedata
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
SCREAMING_SNAKE_CASE_ = logging.get_lo... | 34 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_t... | 34 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'... | 34 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
SCREAMING_SNAKE_CASE_ = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
... | 34 | 1 |
"""simple docstring"""
import argparse
import collections
import os
import re
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_table.py
SCREAMING_SNAKE_CASE_ = ... | 34 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = analyze_text(_lowercase )... | 34 | 1 |
"""simple docstring"""
import copy
import re
class snake_case_ :
"""simple docstring"""
A_ = '''hp'''
A_ = {}
A_ = None
@classmethod
def UpperCAmelCase__ ( cls , lowerCamelCase_ , lowerCamelCase_... | 34 |
"""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():
imp... | 34 | 1 |
"""simple docstring"""
# 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, CLIPTokeni... | 34 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_avai... | 34 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 34 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers... | 34 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ ... | 34 |
"""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 imp... | 34 | 1 |
"""simple docstring"""
# 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... | 34 |
"""simple docstring"""
import operator
def __snake_case ( _lowercase ,_lowercase = False ,_lowercase = None ):
"""simple docstring"""
UpperCamelCase = operator.lt if reverse else operator.gt
UpperCamelCase = solution or []
if not arr:
... | 34 | 1 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfi... | 34 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value... | 34 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tr... | 34 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class snake_case_ ( low... | 34 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"... | 34 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
SCREA... | 34 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
SCREAMING_SNAKE... | 34 |
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE_ = ... | 34 | 1 |
"""simple docstring"""
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import ... | 34 |
"""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
SCREAMING_SNAKE_C... | 34 | 1 |
"""simple docstring"""
import numpy as np
def __snake_case ( _lowercase ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __snake_case ( _lowercase ):
"""simple docstring"""
return vector * sigmoid(1.702 * vector )
if __... | 34 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case_ ( ... | 34 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE_ = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
SCREAMING_SNAKE_CASE_ =... | 34 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]... | 34 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( se... | 34 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( se... | 34 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
... | 34 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = [0 for i in range(len(_lowercase ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase , UpperCamelCase = 0, 0
... | 34 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docs... | 34 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 34 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_fe... | 34 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _lowercase ,_lowercase ,_lowercase ):
"""simple docstring"""
UpperCamelCase = 0
if start < end:
UpperCamelCase ... | 34 | 1 |
"""simple docstring"""
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 impo... | 34 |
"""simple docstring"""
import os
import sys
import unittest
SCREAMING_SNAKE_CASE_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files... | 34 | 1 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __snake_case ( _lowercase ):
"""simple docstring"""
if not isinstance(_lowercase ,_lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif pre... | 34 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __snake_case ( _lowercase ):
"""simple docstring"""
if "cls_token" in name:
UpperCamelCas... | 34 | 1 |
"""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
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCR... | 34 |
"""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 __snake_case ( ):
"""simple docstring"""
raise RuntimeError('''CUDA out o... | 34 | 1 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE_ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any member... | 34 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_t... | 34 | 1 |
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