code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import math
def A__ ( __A , __A , __A , __A , __A ):
'''simple docstring'''
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if len(__A ) == 0:
raise ValueError("""Scores cannot b... | 15 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import wr... | 15 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Union[str, Any] =logging.get_logger(__name__)
lowerCAmelCase : int ={
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/ma... | 15 | 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_features_output_indices
... | 15 | 1 |
from math import ceil
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : List[Any] = list(range(0 , __A ) )
_lowerCamelCase : List[str] = [item for sublist in list(device_map.values() ) for item in sublist]
... | 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 1 |
lowerCAmelCase : Optional[Any] ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/t... | 15 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
l... | 15 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 1 |
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_available():
from PIL import Image
from ..image... | 15 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
lowerCAmelCase : int ={
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
... | 15 | 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_available():
from PIL import Image
from ..image... | 15 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Datase... | 15 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 15 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test... | 15 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert... | 15 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __snake_case :
'''simple docstring'''
def __init__( self : Optional[Any] , _UpperCamelCase : List[str]=2 , _... | 15 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 1 |
import math
import random
def A__ ( __A , __A = False ):
'''simple docstring'''
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase : Optional[int] =0.02
def A__ ... | 15 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 1 |
def A__ ( __A , __A , __A , __A ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_lowerCamelCase : List[str] = mf_knapsack(i - 1 , __A , __A , __A )
else:
... | 15 | 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
lowerCAmelCase : int ... | 15 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] ={
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available... | 15 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 1 |
from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 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 (
IMAGENET_STANDARD_MEAN,
... | 15 | 1 |
from __future__ import annotations
def A__ ( __A ): # This function is recursive
'''simple docstring'''
_lowerCamelCase : Tuple = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if... | 15 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 15 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
i... | 15 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 1 |
import doctest
from collections import deque
import numpy as np
class __snake_case :
'''simple docstring'''
def __init__( self : Any) ->None:
"""simple docstring"""
_lowerCamelCase : int = [2, 1, 2, -1]
... | 15 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 1 |
from scipy.stats import spearmanr
import datasets
lowerCAmelCase : Dict ="\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correl... | 15 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 1 |
import argparse
import struct
import unittest
class __snake_case :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCamelCase : bytes) ->None:
"""simple docstring"""
_lowerCamelCase : str = ... | 15 | 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_features_output_indices
... | 15 | 1 |
def A__ ( __A , __A , __A ):
'''simple docstring'''
if len(__A ) != len(__A ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
raise ValueError("""max_weight must greater than zero.""" )
if any(p < 0 for p in p... | 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __snake_case ( __low... | 15 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
lowerCAmelCase : Optional[int] =[
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel"... | 15 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 1 |
import warnings
from .generation import TFGenerationMixin
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be remo... | 15 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice... | 15 | 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_available():
from PIL import Image
from ..image... | 15 | 1 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=__lowerCAmelCase ):
'''simple docstring'''
_snake_case = ['onnx']
def __init__( self : Optional[Any] , *_UpperCamelCase : Tuple , **_UpperCam... | 15 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCAmelCase : Optional[Any] =["small", "medium", "large"]
lowerCAmelCase : Dict ="lm_head.decoder.weight"
lowerCAmelCase : str ="lm_head.weight"
def A__ ( _... | 15 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.imp... | 15 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( __A , __A , __A ):
'''simple docstring'''
# Init... | 15 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 1 |
from collections.abc import Callable
import numpy as np
def A__ ( __A , __A , __A , __A , __A ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = int(np.ceil((x_end - xa) / step_size ) )
_lowerCamelCase : int = n... | 15 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 1 |
def A__ ( __A , __A ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_lowerCamelCase : str = str(bin(__A ) )[2:] # remove the leading "0b"
_lowerCamelCase : Dict = st... | 15 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 1 |
import math
import os
import sys
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Dict = """"""
try:
with open(__A , """rb""" ) as binary_file:
_lowerCamelCase : List[Any] = binary_file.read()
... | 15 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 1 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=__lowerCAmelCase ):
'''simple docstring'''
_snake_case = ['torch', 'transformers', 'onnx']
def __init__( self : List[str] , *_UpperCamelCase : Li... | 15 | 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
lowerCAmelCase : int ... | 15 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 15 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 1 |
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
_lowerCamelCase : Optional[int] = F"""The input value of [n={number... | 15 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 15 | 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 (
IMAGENET_STANDARD_MEAN,
... | 15 | 1 |
import math
lowerCAmelCase : List[str] =10
lowerCAmelCase : List[Any] =7
lowerCAmelCase : Dict =BALLS_PER_COLOUR * NUM_COLOURS
def A__ ( __A = 20 ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = math.co... | 15 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def A__ ( __A ):
'''simple docstring'''
_lowerCame... | 15 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
import torch
lowerCAmelCa... | 15 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 1 |
import numpy as np
import datasets
lowerCAmelCase : Optional[int] ="\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was intr... | 15 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 1 |
import argparse
import os
import re
lowerCAmelCase : Optional[Any] ="src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase : List[Any] =re.compile(r"[A-Z_]+_MAP... | 15 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 1 |
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,
... | 15 | 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_features_output_indices
... | 15 | 1 |
import datasets
from .evaluate import evaluate
lowerCAmelCase : int ="\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={20... | 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCAmelCase : List[str] =logging.getLogger(__name__)
class __snake_case ( __lowerCAmelCase ):
... | 15 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 15 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 15 | 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_available():
from PIL import Image
from ..image... | 15 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, val... | 15 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 1 |
def A__ ( __A , __A , __A , __A , __A ):
'''simple docstring'''
if index == number_of_items:
return 0
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = 0
_lowerCamelCase : List[Any] = knapsack(__... | 15 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common im... | 15 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 1 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
... | 15 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 1 |
def A__ ( __A = 10**9 ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = 1
_lowerCamelCase : List[str] = 2
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : List[str] = 0
_lowerCamel... | 15 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ... | 15 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 1 |
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, l... | 15 | 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
lowerCAmelCase : int ... | 15 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 15 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
lowerCAmelCase : List[Any] ={
"kssteven/i... | 15 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 1 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Dict = []
_lowerCamelCase : List... | 15 | 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 (
IMAGENET_STANDARD_MEAN,
... | 15 | 1 |
class __snake_case :
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCamelCase : str = "" , _UpperCamelCase : bool = False) ->None:
"""simple docstring"""
_lowerCamelCase : dict[str, RadixNode] ... | 15 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_det... | 15 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 15 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 1 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientStat... | 15 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 1 |
from __future__ import annotations
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : Optional[int] = len(__A ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 15 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 1 |
import copy
import re
class __snake_case :
'''simple docstring'''
_snake_case = 'hp'
_snake_case = {}
_snake_case = None
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : List[Any] , _UpperC... | 15 | 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_features_output_indices
... | 15 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.ut... | 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 15 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowerCAmelCase : str =logging.get_logger(__name__)
low... | 15 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 1 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_... | 15 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 1 |
def A__ ( __A ):
'''simple docstring'''
def merge(__A , __A ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return l... | 15 | 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_available():
from PIL import Image
from ..image... | 15 | 1 |
def A__ ( __A ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
_lowerCamelCase : str = sorted(string.lower() )
return len(__A ) == len(set(__A ) )
if _... | 15 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] ={
"pos... | 15 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : str =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-... | 15 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
... | 15 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 1 |
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = """"""
for i in table:
res += inp[i - 1]
return res
def A__ ( __A ):
'''simple docstring'''
return data[1:] + data[0]
... | 15 | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
... | 15 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
... | 15 | lowerCAmelCase : Tuple =0 # The first color of the flag.
lowerCAmelCase : Union[str, Any] =1 # The second color of the flag.
lowerCAmelCase : Any =2 # The third color of the flag.
lowerCAmelCase : List[str] =(red, white, blue)
def A__ ( __A... | 15 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 15 | from __future__ import annotations
lowerCAmelCase : int =[]
def A__ ( __A , __A , __A ):
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i]... | 15 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case ( __lowerCAmelCase ):
... | 15 | 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
lowerCAmelCase : int ... | 15 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsear... | 15 | def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(__A )
_lowerCamelCase : List[str] = pow(2 , __A )
# If we already turned o... | 15 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 15 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@req... | 15 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 15 | 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 (
IMAGENET_STANDARD_MEAN,
... | 15 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tq... | 15 | from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ... | 15 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Li... | 15 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase : Dict =logging.getLogger()
... | 15 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 15 | import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A__ ( __A ):
'''simple docstring'''
_lowerCamelCase : Tuple = {}
... | 15 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def A__ ( __A = 8 ):
'''simple docstring'''
_lowerCamelCase : str = ascii_letters + digits + punctuation
return "".jo... | 15 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 15 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase : Optional[int] =TypeVar("T")
class __snake_case ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ... | 15 | 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_features_output_indices
... | 15 | 1 |
from __future__ import annotations
from math import pi
def A__ ( __A , __A , __A ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
... | 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def A__ ( __A , __A=7 ):
'''simple docstring'''
_lowerCamelCase : List[Any] = None
if token is not None:
_lowerCamelC... | 15 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : Dict ={"vocab_file": "vocab.json"}
lowerCAmelCase : List[str] ... | 15 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = ['image_processor', 'tokenizer']
_snake_case = 'AutoImageProcesso... | 15 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __snake_case :
'''simple docstring'''
_snake_case = 42
_snake_case =... | 15 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase : Tuple =logging.get... | 15 | 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_available():
from PIL import Image
from ..image... | 15 | 1 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def A__ ( __A , __A , __A = False ):
'''simple docstring'''
if radian_mode:
return [magnitude * cos(__A ), magnitude * sin(__A... | 15 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformer... | 15 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCAmelCase : Union[str, Any] =logging.getLogger(__name__)
if is_torch_tpu_... | 15 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = ArgumentParser(
... | 15 | 1 |
class __snake_case :
'''simple docstring'''
def __init__( self : int) ->Tuple:
"""simple docstring"""
_lowerCamelCase : Tuple = {}
def _SCREAMING_SNAKE_CASE ( self : str) ->None:
... | 15 | def A__ ( __A , __A ):
'''simple docstring'''
_enforce_args(__A , __A )
if n == 0:
return 0
_lowerCamelCase : Tuple = float("""-inf""" )
for i in range(1 , n + 1 ):
_lowerCamelCase : Any = max(
__A ,... | 15 | 1 |
import numpy as np
import qiskit
def A__ ( __A = 8 , __A = None ):
'''simple docstring'''
_lowerCamelCase : Any = np.random.default_rng(seed=__A )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
... | 15 | from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ke... | 15 | 1 |
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