






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_			:Dict	    =		{
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Optional[int]	    =		['''LayoutLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Any	    =		[
							    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LayoutLMForMaskedLM''',
							    '''LayoutLMForSequenceClassification''',
							    '''LayoutLMForTokenClassification''',
							    '''LayoutLMForQuestionAnswering''',
							    '''LayoutLMModel''',
							    '''LayoutLMPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Optional[Any]	    =		[
							    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFLayoutLMForMaskedLM''',
							    '''TFLayoutLMForSequenceClassification''',
							    '''TFLayoutLMForTokenClassification''',
							    '''TFLayoutLMForQuestionAnswering''',
							    '''TFLayoutLMMainLayer''',
							    '''TFLayoutLMModel''',
							    '''TFLayoutLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							A_			:Union[str, Any]	    =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class snake_case   (	unittest.TestCase):







		@patch("diffusers.utils.hub_utils.get_full_repo_name"		)
		def 			a_			(       self   :							Any     , a__   :							Mock		)							->    None:





						'''simple docstring'''







						_A       	=      "full_repo_name"
						with TemporaryDirectory() as tmpdir:
										# Dummy args values
										_A       	=      Mock()
										_A       	=      tmpdir
										_A       	=      0
										_A       	=      "hub_token"
										_A       	=      "dataset_name"
										_A       	=      0.0_1
										_A       	=      10_00_00
										_A       	=      1_00_00
										_A       	=      0.0_1
										_A       	=      0.0_2
										_A       	=      0.0_3
										_A       	=      0.0_0_0_5
										_A       	=      0.0_0_0_0_0_1
										_A       	=      1
										_A       	=      10
										_A       	=      0.0_0_1
										_A       	=      0.1
										_A       	=      0.2
										_A       	=      True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(a__     , model_name="model_name"		)
										self.assertTrue((Path(a__		) / "README.md").is_file()		)