"""simple docstring"""




import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard
class      UpperCamelCase__(      unittest.TestCase			):





						def 					snake_case__						(							self  )       ->					List[str]:
													A__						  =				{
													    'model_details': {
													        'Organization': 'testing',
													        'Model date': 'today',
													        'Model version': 'v2.1, Developed by Test Corp in 2019.',
													        'Architecture': 'Convolutional Neural Network.',
													    },
													    'metrics': 'BLEU and ROUGE-1',
													    'evaluation_data': {
													        'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
													        'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
													    },
													    'training_data': {
													        'Dataset': 'English Wikipedia dump dated 2018-12-01',
													        'Preprocessing': (
													            'Using SentencePiece vocabulary of size 52k tokens. See details on'
													            ' https://arxiv.org/pdf/1810.03993.pdf'
													        ),
													    },
													    'quantitative_analyses': {'BLEU': 5_5.1, 'ROUGE-1': 76},
													}





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				ModelCard.from_dict(self.inputs_dict  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'model_details'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'intended_use'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'factors'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'metrics'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'evaluation_data'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'training_data'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'quantitative_analyses'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'ethical_considerations'  )  )
													self.assertTrue(hasattr(__UpperCAmelCase		,'caveats_and_recommendations'  )  )





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				ModelCard.from_dict(self.inputs_dict  )
													A__						  =				json.loads(modelcard.to_json_string()  )
													for key, value in self.inputs_dict.items():
																				self.assertEqual(obj[key]		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				ModelCard.from_dict(self.inputs_dict  )

													with tempfile.TemporaryDirectory() as tmpdirname:
																				A__						  =				os.path.join(__UpperCAmelCase		,'modelcard.json'  )
																				model_card_first.to_json_file(__UpperCAmelCase  )
																				A__						  =				ModelCard.from_json_file(__UpperCAmelCase  )

													self.assertEqual(model_card_second.to_dict()		,model_card_first.to_dict()  )





						def 					snake_case__						(							self  )       ->					Dict:
													A__						  =				ModelCard.from_dict(self.inputs_dict  )

													with tempfile.TemporaryDirectory() as tmpdirname:
																				model_card_first.save_pretrained(__UpperCAmelCase  )
																				A__						  =				ModelCard.from_pretrained(__UpperCAmelCase  )

													self.assertEqual(model_card_second.to_dict()		,model_card_first.to_dict()  )






"""simple docstring"""




from typing import TYPE_CHECKING

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


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

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

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "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

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





