code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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_... | 21 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
'''simple docstring'''
_snake_case : int = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
fro... | 22 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
snake_case__ : Union[str, Any] = """__DUMMY_TRANSFORMERS_USER__"""
snake_case__ : Optional[int] =... | 23 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 24 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number >... | 25 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
d... | 26 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> list:
"""simple docstring"""
_A = False
while is_sorted is False: # Until all the indices are traversed keep looping
_A = True
for i in range(0 , len(_SCREAMING... | 27 |
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
class... | 14 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets... | 28 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
... | 29 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a = TypeVar('_T')
class __a( Generic[_T] ):
"""simple docstring"""
def __init__( self ,_SCREAMING_SNAKE_CASE = None ) -> None:
UpperCAmelCase_ : list[_T] = list(iter... | 30 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : in... | 31 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggingfac... | 32 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
lowerCamelCase__ : List[Any] = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusio... | 33 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_avai... | 34 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
def a ( A__ ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n ... | 35 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
from math import pi
def lowercase ( __A : int , __A : int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 36 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
from __future__ import annotations
from collections import deque
class A__ :
"""simple docstring"""
def __init__( self : Any , lowerCamelCase__ : list[str] ):
a__ : list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state"... | 37 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..ta... | 38 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
lowerCAmelCase_ = range(2, 20 + 1)
lowerCAmelCase_ = [10**k for k in range(ks[-1] + 1)]
lowerCAmelCase_ = {}
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
... | 39 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
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 = {
'''bert-base-uncased''': '''https://huggingface.co/b... | 40 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCAmelCase__ = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-... | 41 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCAmelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAK... | 42 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TY... | 43 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):... | 44 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class lowerCAmelCase_ ( lowercase ):
... | 45 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_lowerCAmelCase : List[str] = parse(importlib.metadata.version('''torch'''))
def lowerCamelCase_( _lowerCamelCas... | 46 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
import sys
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... | 47 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
'''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__ : str = logging.get_logger(__name__)
UpperCAmelCase__ :... | 48 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int = 4_000_000 ):
__UpperCAmelCase = [0, 1]
__UpperCAmelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
__UpperCAmelCase = 0
for j in rang... | 49 |
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
class... | 14 | 0 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def A__ ( __lowerCAmelCase : bool = True , *__lowerCAmelCase : int , **__lowerCAmelCase : Union[str, A... | 50 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 51 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :str) -> list[int]:
return [ord(a_) - 96 for elem in plain]
def __A ( a_ :list[int]) -> str:
return "".join(chr(elem + 96) for elem in encoded)
def __A ( ) ->... | 52 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
def a_ ( lowerCAmelCase_ : float, lowerCAmelCase_ : int ):
if digit_amount > 0:
return round(number - int(lowerCAmelCase_ ), lowerCAmelCase_ )
return number - int(lowerCAmelCase_ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
... | 53 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
def a__ ( lowercase__ = 1 , lowercase__ = 1_0_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =1
UpperCAmelCase_ =0
for divide_by_number in range(lowercase__ , digit + 1 ):
UpperCAmelCase_ =[]
UpperCAmel... | 54 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple do... | 55 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_a : Any = logging.get_logger(__name__)
class _lowercase ( __lowercase ):
def __init__( self : List[Any] , *SCREAMING_SNAKE_CASE_... | 56 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
from math import factorial
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: List[Any] = real
if isinstance(_lowerCamelCase ... | 57 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
"""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
__lowerCAmelCase : Union[str, Any] ... | 58 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 59 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : A... | 60 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __lowerCamelCase ( UpperCamelCase__ ):
... | 61 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 62 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@r... | 63 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : str = {
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_PRETRAINED... | 64 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __lowercase ( __lowerCamelCase ):
snake_case_ = """SpeechT5FeatureExtractor"""
snake_case_ = """SpeechT5Tokenizer"""
def __init__( self : Optional[i... | 65 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers... | 66 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
... | 67 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
import torch
from transformers import AutoModel
class _A ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : str="sayef/fsner-bert-base-uncased" ) -> Optional[int]:
super(__SCREAMIN... | 68 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __Upper... | 69 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprec... | 70 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ : Any = st... | 71 |
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
class... | 14 | 0 |
'''simple docstring'''
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... | 72 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Tup... | 73 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
lowercase_ = models.Sequential()
# Step 1 - Convolution
# Here ... | 74 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrat... | 75 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
"""simple docstring"""
a_ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
... | 76 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...t... | 77 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
SCREAMING_SNAKE_CASE_: Union[str, Any] ='__DUMMY_TRANSFORMERS_USER__'
SCREAMING_SNAKE_CASE_: Optional[Any] ='Dummy User'
SCREAM... | 78 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
SCREAMING_SNAKE_CASE__ : Dict = 6_55_21
def _lowerCamelCase ( __lowerCamelCase ) -> int:
'''simple docstring'''
UpperCAmelCase__ : str = 1
UpperCAmelCase__ : Tuple = 0
for plain_chr in plain_text:
... | 79 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
__lowerc... | 80 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase = 1_0 ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or n < 0:
raise ValueError("Invalid input" )
__snake_case : List[str] = 1_0**n
__snake_case : Optional[int] = ... | 81 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase__... | 82 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : List[str] = prime_factors(A_ )
if is_square_free(A_... | 83 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_a... | 84 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_S... | 85 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ = len(__UpperCamelCase )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from ... | 86 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
import re
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
if len(re.findall('''[ATCG]''' , lowercase_ ) ) != len(lowercase_ ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.maketrans('''ATCG''' , ... | 87 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( __snake_case : Tuple , __snake_case : ... | 88 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
from __future__ import annotations
from math import pi
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' ... | 89 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCAmelCase__ = 1
for i in range(1 , n + 1 ):
# to compute current row from previou... | 90 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
"""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
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from trans... | 92 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
f... | 93 |
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
class... | 14 | 0 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
Character... | 94 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {name: getattr(transformers, nam... | 95 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : List[Any] ) -> str:
__magic_name__: Optional[int] = [0] * len(__UpperCAmelCase )
__magic_name__: str = []
__magic_name__: Any = []
__magic_name__: Union[... | 96 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .... | 97 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def a__ ( lowercase : Sequence[float], lowercase : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(lowercase ) )
def a__ ( lowercase : Seq... | 98 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 99 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER,... | 100 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 14 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase__ : List[str] =3_00 # TEMPERATURE (unit = K)
def a__ ( A__, A__, A__, ):
if donor_conc <= 0:
raise ValueError('Donor concentration should be positi... | 101 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
"""simple docstring"""
import math
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return math.pow(SCREAMING_SNAKE_CASE , 2 ) - a
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return 2 * x
def UpperCamelC... | 102 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, l... | 103 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 14 | 0 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils im... | 104 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ : Union[str, Any] = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if ... | 105 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 14 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__snake_case :int ={
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export', 'validate... | 106 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cla... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attentio... | 107 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 | 0 |
import re
from filelock import FileLock
try:
import nltk
__a: Any = True
except (ImportError, ModuleNotFoundError):
__a: Optional[int] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _SCREAMING_SNAK... | 108 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,... | 109 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAm... | 14 | 0 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = 1
UpperCAmelCase__ : Tuple = True
for v in tree[start]:
if v not in visited:
ret += dfs(_snake_case )
if ret ... | 110 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
a__ = '''Usage of script: script_name <size_of_canvas:int>'''
a__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def __UpperCAmelCase ... | 14 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase ( a_ , a_ ) -> str:
"""simple docstrin... | 55 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProces... | 27 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''google/mobi... | 14 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 24 |
a__ = '''Input must be a string of 8 numbers plus letter'''
a__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __UpperCAmelCase ( __a : str ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : List[s... | 14 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://hu... | 290 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCAmelCase ( __a : Optional[Any] ,__a : int ,__a : Any ) -> int:
"""simple docstring"""
_a : int = 0
if start < end:
_a ... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = list[list[int]]
# assigning initial values to the grid
_lowerCamelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, ... | 71 |
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
class... | 14 | 0 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import P... | 602 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 14 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__SCREAMING_SNAKE_CASE : List[str] = logging.get_l... | 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common im... | 157 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 0 |
from __future__ import annotations
def a__ ( _UpperCamelCase : list[int] ,_UpperCamelCase : int ):
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = 0
__lowerCamelCase = sum(__a )
create_state_space_tree(__a ... | 175 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( __a : ... | 14 | 0 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCAmelCase__ = '''__DUMMY_TRANSFORMERS_USER__'''
UpperCAmelCase__ = '''Dummy User'''
UpperCAmelCase__ = '... | 186 |
from scipy.stats import spearmanr
import datasets
a__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 14 | 0 |
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