code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
'configuration_distilbert': [
... | 288 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ = '... | 288 | 1 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int = 2 , __lowerCamelCase : int = 1 , __lowerCamelCase : int = 3 , ) -> int | None:
# A valu... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 1 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
def _UpperCAmelCase ... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformer... | 288 | 1 |
"""simple docstring"""
import os
import numpy
import onnx
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : Optional[int] ) -> int:
_snake_case = a.name
_snake_case = b.name
_snake_case = ''''''
... | 288 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 288 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _UpperCAmelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any]=None , **__lowerCamelCase : Dict ) -> str:
_snake_... | 288 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( A_ ):
def __lt__( self : Any , _lowerCamelCase : int ... | 288 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusi... | 288 |
"""simple docstring"""
UpperCAmelCase__ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0... | 288 | 1 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch... | 288 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 10_00 ) -> int:
return sum(e for e in range(3 , __lowerCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 288 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# ... | 288 | 1 |
"""simple docstring"""
from maths.prime_factors import prime_factors
def _UpperCAmelCase ( __lowerCamelCase : int ) -> int:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
_snake_case = f'''Input value of [number={number}] must be an integer'''
... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __lowerCamelCase ):
for n in range(__lowerCamelCase , __lowerC... | 288 | 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
UpperCAmelCase__ = logging.get_logger(__name__)
U... | 288 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _UpperCAmelCase ( __lowerCamelCase : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowerCamelCase , __l... | 288 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _UpperCAmelCase ( ) -> Union[str, Any]:
_snake_case = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
_snake_case ... | 288 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokeniz... | 288 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase__ ( nn.Module ):
__a = 42
__a = jnp.floataa
def lowercase ( self : Union[str, Any] ):
_snake_case = ... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Any:
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[str] , __low... | 288 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'post_... | 288 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _UpperCAmelCase ( __lowerCamelCase : str ) -> List[Any]:
ret... | 288 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 288 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 | 1 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base i... | 288 |
"""simple docstring"""
from math import pow
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , ) -> tuple[int, int]:
if current_sum =... | 288 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTr... | 288 |
"""simple docstring"""
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 requ... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : str ) -> bool:
_snake_case = len(__lowerCamelCase )
_snake_case = len(__lowerCamelCase )
_snake_case = [[False for _ in range(m... | 288 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 1 |
"""simple docstring"""
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,
)
UpperCAmelCase__ = {
'con... | 288 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 | 1 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
UpperCAmelCase__ = 'path-to-your-trained-model'
UpperCAmelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCAmelCase__ = 'A photo of sks ... | 288 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from dataset... | 288 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
UpperCAmelCase__ = tuple[int, int]
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] ):
_snake_case = []
_snake_case = set()
... | 288 |
"""simple docstring"""
from math import sqrt
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for... | 288 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase__ :
def __init__( self : Any ):
_snake_case = {}
def lowercase ( ... | 288 |
"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.ut... | 288 | 1 |
"""simple docstring"""
import math
def _UpperCAmelCase ( __lowerCamelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are no... | 288 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ = '... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : str ) -> list[int]:
_snake_case = [0 for i in range(len(__lowerCamelCase ) )]
# initialize interval's left pointer and right pointer
_snake_case , _snake_case = 0, 0
... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'... | 288 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Convers... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformer... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] , _lowerCamelCase : int ):
_snake_case = num_of_nodes
_snake_case = ... | 288 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 288 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PR... | 288 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( A_ ):
def __lt__( self : Any , _lowerCamelCase : int ... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCAmelCase__ ( A_ ):
pass
class lowerCAmelCase__ :
def __init__( self : List[str] , _lowerCamelCase : Any ):
_snake_case... | 288 |
"""simple docstring"""
UpperCAmelCase__ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0... | 288 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
Stabl... | 288 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 288 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_... | 288 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# ... | 288 | 1 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProces... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __lowerCamelCase ):
for n in range(__lowerCamelCase , __lowerC... | 288 | 1 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset... | 288 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _UpperCAmelCase ( __lowerCamelCase : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowerCamelCase , __l... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import ... | 288 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokeniz... | 288 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Any:
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[str] , __low... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCAmelCase ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('... | 288 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _UpperCAmelCase ( __lowerCamelCase : str ) -> List[Any]:
ret... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict ) -> str:
_snake_case = [0 for i in range(r + 1 )]
# nc0 = 1
_snake_case = 1
for i in range(1 , n + 1 ):
# to compu... | 288 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'robert... | 288 |
"""simple docstring"""
from math import pow
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , ) -> tuple[int, int]:
if current_sum =... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Any:
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[str] , __low... | 288 |
"""simple docstring"""
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 requ... | 288 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 288 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) -> Union[str, Any]:
_snake_case = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _UpperCAmelCase ( ... | 288 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 | 1 |
"""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, _co... | 288 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from dataset... | 288 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
... | 288 |
"""simple docstring"""
from math import sqrt
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for... | 288 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCAmelCase__ = False
class lowerCA... | 288 |
"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.ut... | 288 | 1 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowerCAmelCase__... | 288 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ = '... | 288 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'... | 288 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
f... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : float ) -> float:
if edge <= 0 or not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformer... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('''Coefficie... | 288 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 288 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( A_ ):
__a = (PNDMScheduler,)
__a = (("""num_inference_steps""", 50),)
def... | 288 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( A_ ):
def __lt__( self : Any , _lowerCamelCase : int ... | 288 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _UpperCAmelCase ... | 288 |
"""simple docstring"""
UpperCAmelCase__ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0... | 288 | 1 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 288 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 288 | 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 PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = '▁'
UpperCAmelCase__ = {'vocab_fi... | 288 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# ... | 288 | 1 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the confi... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __lowerCamelCase ):
for n in range(__lowerCamelCase , __lowerC... | 288 | 1 |
"""simple docstring"""
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__ = logging.get_logger(__name__)
Up... | 288 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _UpperCAmelCase ( __lowerCamelCase : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowerCamelCase , __l... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _UpperCAmelCase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ... | 288 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokeniz... | 288 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'huggingface/time-series-transformer-tourism-monthly': (
... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Any:
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[str] , __low... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _UpperCAmelCase ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : bool = False ) ... | 288 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _UpperCAmelCase ( __lowerCamelCase : str ) -> List[Any]:
ret... | 288 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokeniz... | 288 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _UpperCAmelCase ( ) -> Optional[Any]:
_snake_case = ArgumentParser(
descriptio... | 288 |
"""simple docstring"""
from math import pow
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , ) -> tuple[int, int]:
if current_sum =... | 288 | 1 |
"""simple docstring"""
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 requ... | 288 |
"""simple docstring"""
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 requ... | 288 | 1 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from tran... | 288 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 1 |
"""simple docstring"""
# 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/LICENS... | 288 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 | 1 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase__ = 299792458
# Symbols
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = symbols('ct x y z')
de... | 288 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from dataset... | 288 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDe... | 288 |
"""simple docstring"""
from math import sqrt
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for... | 288 | 1 |
"""simple docstring"""
import os
def _UpperCAmelCase ( ) -> str:
with open(os.path.dirname(__lowerCamelCase ) + '''/grid.txt''' ) as f:
_snake_case = [] # noqa: E741
for _ in range(20 ):
l.append([int(__lowerCamelCase ) for x in f.readline().split()] ... | 288 |
"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.ut... | 288 | 1 |
"""simple docstring"""
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 requ... | 288 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ = '... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ) -> int... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'... | 288 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtr... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_c... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformer... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int ) -> int:
_snake_case = [[0 for _ in range(__lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_snake_case = 1
for n in range(m + 1 ):
for k in r... | 288 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 288 | 1 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( A_ ):
def __lt__( self : Any , _lowerCamelCase : int ... | 288 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_... | 288 |
"""simple docstring"""
UpperCAmelCase__ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0... | 288 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common impo... | 288 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 288 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCAmelCase ( *__lowerCamelCase : Tuple , __lowerCamelCase : Optional[Union[Dict, Any]] = None , __lowerCamelCase : Any=True ... | 288 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# ... | 288 | 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
UpperCAmelCase__ = logging.get_l... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __lowerCamelCase ):
for n in range(__lowerCamelCase , __lowerC... | 288 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase__ ( tf.keras.layers.Layer ):
... | 288 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _UpperCAmelCase ( __lowerCamelCase : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowerCamelCase , __l... | 288 | 1 |
"""simple docstring"""
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 OnnxCo... | 288 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokeniz... | 288 | 1 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCAmelCase__ = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight',... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Any:
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[str] , __low... | 288 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 288 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _UpperCAmelCase ( __lowerCamelCase : str ) -> List[Any]:
ret... | 288 | 1 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'microsoft/xprophetnet-large-wiki100-cased': (
'h... | 288 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 288 |
"""simple docstring"""
from math import pow
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , ) -> tuple[int, int]:
if current_sum =... | 288 | 1 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download i... | 288 |
"""simple docstring"""
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 requ... | 288 | 1 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _UpperCAmelCase ( __lowerCamelCase : Optional[int] ) -> str:
_snake_case = os.path.join(args.tf_mode... | 288 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _UpperCAmelCase ( __lowerCamelCase : List[str] ) -> Optional[int]:
if not is_accelerate_available():
return me... | 288 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _UpperCAmelCase ( __lowerCamelCase : Tuple , __lowerC... | 288 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from dataset... | 288 | 1 |
"""simple docstring"""
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 ..... | 288 |
"""simple docstring"""
from math import sqrt
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for... | 288 | 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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAme... | 288 |
"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.ut... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def _UpperCAmelCase ( __lowerCamelCase : Callable[[int | float], int | float] , __lowerCamelCase : int | float , __lowerCamelCase : int | float , __lowerCamelCase :... | 288 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ = '... | 288 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixi... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'... | 288 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Dict ) -> Union[str, Any]:
_snake_case = len(__lowerCamelCase )
for i in range(length - 1 ):
_snake_case = i
for k in range(i + 1 , __lowerCamelCase ):
if colle... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
UpperCAmelCase__ = '\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 reli... | 288 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformer... | 288 | 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, ... | 288 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowerCAmelCase__ :
def __init__( self : Tuple , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : float = 0 ):
_snake... | 288 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCAmelCase__ ( A_ ):
def __lt__( self : Any , _lowerCamelCase : int ... | 288 | 1 |
"""simple docstring"""
import random
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : List[str] ) -> tuple:
_snake_case , _snake_case , _snake_case = [], [], []
for element in data:
if element < pivot:
le... | 288 |
"""simple docstring"""
UpperCAmelCase__ = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0... | 288 | 1 |
"""simple docstring"""
import math
def _UpperCAmelCase ( __lowerCamelCase : int ) -> bool:
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
r... | 288 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 288 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['Ta... | 288 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# ... | 288 | 1 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 1_00_00_00 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __lowerCamelCase ):
for n in range(__lowerCamelCase , __lowerC... | 288 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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 imp... | 288 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _UpperCAmelCase ( __lowerCamelCase : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowerCamelCase , __l... | 288 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
... | 288 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokeniz... | 288 | 1 |
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