
"""simple docstring"""


from typing import TYPE_CHECKING

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


A_						       =				{
    """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_						       =				["""LayoutLMTokenizerFast"""]

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """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_						       =				_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    __lowerCamelCase     (   unittest.TestCase ):




				@patch('''diffusers.utils.hub_utils.get_full_repo_name'''      )
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     '''full_repo_name'''
							with TemporaryDirectory() as tmpdir:
										# Dummy args values
										lowerCamelCase_ 		=     Mock()
										lowerCamelCase_ 		=     tmpdir
										lowerCamelCase_ 		=     0
										lowerCamelCase_ 		=     '''hub_token'''
										lowerCamelCase_ 		=     '''dataset_name'''
										lowerCamelCase_ 		=     0.0_1
										lowerCamelCase_ 		=     10_0000
										lowerCamelCase_ 		=     1_0000
										lowerCamelCase_ 		=     0.0_1
										lowerCamelCase_ 		=     0.0_2
										lowerCamelCase_ 		=     0.0_3
										lowerCamelCase_ 		=     0.0_0_0_5
										lowerCamelCase_ 		=     0.0_0_0_0_0_1
										lowerCamelCase_ 		=     1
										lowerCamelCase_ 		=     10
										lowerCamelCase_ 		=     0.0_0_1
										lowerCamelCase_ 		=     0.1
										lowerCamelCase_ 		=     0.2
										lowerCamelCase_ 		=     True

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



