





from typing import TYPE_CHECKING

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


__A    :   Any          =       {
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Dict          =       [
				    '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
				    '''MODEL_FOR_BACKBONE_MAPPING''',
				    '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
				    '''MODEL_FOR_CAUSAL_LM_MAPPING''',
				    '''MODEL_FOR_CTC_MAPPING''',
				    '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
				    '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
				    '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
				    '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
				    '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
				    '''MODEL_FOR_MASKED_LM_MAPPING''',
				    '''MODEL_FOR_MASK_GENERATION_MAPPING''',
				    '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
				    '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
				    '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
				    '''MODEL_FOR_PRETRAINING_MAPPING''',
				    '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
				    '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
				    '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
				    '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
				    '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
				    '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
				    '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
				    '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
				    '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
				    '''MODEL_MAPPING''',
				    '''MODEL_WITH_LM_HEAD_MAPPING''',
				    '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
				    '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
				    '''AutoModel''',
				    '''AutoBackbone''',
				    '''AutoModelForAudioClassification''',
				    '''AutoModelForAudioFrameClassification''',
				    '''AutoModelForAudioXVector''',
				    '''AutoModelForCausalLM''',
				    '''AutoModelForCTC''',
				    '''AutoModelForDepthEstimation''',
				    '''AutoModelForImageClassification''',
				    '''AutoModelForImageSegmentation''',
				    '''AutoModelForInstanceSegmentation''',
				    '''AutoModelForMaskGeneration''',
				    '''AutoModelForTextEncoding''',
				    '''AutoModelForMaskedImageModeling''',
				    '''AutoModelForMaskedLM''',
				    '''AutoModelForMultipleChoice''',
				    '''AutoModelForNextSentencePrediction''',
				    '''AutoModelForObjectDetection''',
				    '''AutoModelForPreTraining''',
				    '''AutoModelForQuestionAnswering''',
				    '''AutoModelForSemanticSegmentation''',
				    '''AutoModelForSeq2SeqLM''',
				    '''AutoModelForSequenceClassification''',
				    '''AutoModelForSpeechSeq2Seq''',
				    '''AutoModelForTableQuestionAnswering''',
				    '''AutoModelForTokenClassification''',
				    '''AutoModelForUniversalSegmentation''',
				    '''AutoModelForVideoClassification''',
				    '''AutoModelForVision2Seq''',
				    '''AutoModelForVisualQuestionAnswering''',
				    '''AutoModelForDocumentQuestionAnswering''',
				    '''AutoModelWithLMHead''',
				    '''AutoModelForZeroShotImageClassification''',
				    '''AutoModelForZeroShotObjectDetection''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Union[str, Any]          =       [
				    '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
				    '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
				    '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
				    '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
				    '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
				    '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
				    '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
				    '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
				    '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
				    '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
				    '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
				    '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
				    '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
				    '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
				    '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
				    '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
				    '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
				    '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
				    '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
				    '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
				    '''TF_MODEL_MAPPING''',
				    '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
				    '''TFAutoModel''',
				    '''TFAutoModelForAudioClassification''',
				    '''TFAutoModelForCausalLM''',
				    '''TFAutoModelForImageClassification''',
				    '''TFAutoModelForMaskedImageModeling''',
				    '''TFAutoModelForMaskedLM''',
				    '''TFAutoModelForMaskGeneration''',
				    '''TFAutoModelForMultipleChoice''',
				    '''TFAutoModelForNextSentencePrediction''',
				    '''TFAutoModelForPreTraining''',
				    '''TFAutoModelForDocumentQuestionAnswering''',
				    '''TFAutoModelForQuestionAnswering''',
				    '''TFAutoModelForSemanticSegmentation''',
				    '''TFAutoModelForSeq2SeqLM''',
				    '''TFAutoModelForSequenceClassification''',
				    '''TFAutoModelForSpeechSeq2Seq''',
				    '''TFAutoModelForTableQuestionAnswering''',
				    '''TFAutoModelForTextEncoding''',
				    '''TFAutoModelForTokenClassification''',
				    '''TFAutoModelForVision2Seq''',
				    '''TFAutoModelForZeroShotImageClassification''',
				    '''TFAutoModelWithLMHead''',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Optional[int]          =       [
				    '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
				    '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
				    '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
				    '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
				    '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
				    '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
				    '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
				    '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
				    '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
				    '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
				    '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
				    '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
				    '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
				    '''FLAX_MODEL_MAPPING''',
				    '''FlaxAutoModel''',
				    '''FlaxAutoModelForCausalLM''',
				    '''FlaxAutoModelForImageClassification''',
				    '''FlaxAutoModelForMaskedLM''',
				    '''FlaxAutoModelForMultipleChoice''',
				    '''FlaxAutoModelForNextSentencePrediction''',
				    '''FlaxAutoModelForPreTraining''',
				    '''FlaxAutoModelForQuestionAnswering''',
				    '''FlaxAutoModelForSeq2SeqLM''',
				    '''FlaxAutoModelForSequenceClassification''',
				    '''FlaxAutoModelForSpeechSeq2Seq''',
				    '''FlaxAutoModelForTokenClassification''',
				    '''FlaxAutoModelForVision2Seq''',
				]


if TYPE_CHECKING:
				from .auto_factory import get_values
				from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
				from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
				from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
				from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
				from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_auto import (
								    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
								    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
								    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
								    MODEL_FOR_BACKBONE_MAPPING,
								    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
								    MODEL_FOR_CAUSAL_LM_MAPPING,
								    MODEL_FOR_CTC_MAPPING,
								    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
								    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
								    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
								    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
								    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
								    MODEL_FOR_MASK_GENERATION_MAPPING,
								    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
								    MODEL_FOR_MASKED_LM_MAPPING,
								    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
								    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
								    MODEL_FOR_OBJECT_DETECTION_MAPPING,
								    MODEL_FOR_PRETRAINING_MAPPING,
								    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
								    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
								    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
								    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
								    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
								    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
								    MODEL_FOR_TEXT_ENCODING_MAPPING,
								    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
								    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
								    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
								    MODEL_FOR_VISION_2_SEQ_MAPPING,
								    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
								    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
								    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
								    MODEL_MAPPING,
								    MODEL_WITH_LM_HEAD_MAPPING,
								    AutoBackbone,
								    AutoModel,
								    AutoModelForAudioClassification,
								    AutoModelForAudioFrameClassification,
								    AutoModelForAudioXVector,
								    AutoModelForCausalLM,
								    AutoModelForCTC,
								    AutoModelForDepthEstimation,
								    AutoModelForDocumentQuestionAnswering,
								    AutoModelForImageClassification,
								    AutoModelForImageSegmentation,
								    AutoModelForInstanceSegmentation,
								    AutoModelForMaskedImageModeling,
								    AutoModelForMaskedLM,
								    AutoModelForMaskGeneration,
								    AutoModelForMultipleChoice,
								    AutoModelForNextSentencePrediction,
								    AutoModelForObjectDetection,
								    AutoModelForPreTraining,
								    AutoModelForQuestionAnswering,
								    AutoModelForSemanticSegmentation,
								    AutoModelForSeqaSeqLM,
								    AutoModelForSequenceClassification,
								    AutoModelForSpeechSeqaSeq,
								    AutoModelForTableQuestionAnswering,
								    AutoModelForTextEncoding,
								    AutoModelForTokenClassification,
								    AutoModelForUniversalSegmentation,
								    AutoModelForVideoClassification,
								    AutoModelForVisionaSeq,
								    AutoModelForVisualQuestionAnswering,
								    AutoModelForZeroShotImageClassification,
								    AutoModelForZeroShotObjectDetection,
								    AutoModelWithLMHead,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_auto import (
								    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
								    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
								    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
								    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
								    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
								    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
								    TF_MODEL_FOR_MASKED_LM_MAPPING,
								    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
								    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
								    TF_MODEL_FOR_PRETRAINING_MAPPING,
								    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
								    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
								    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
								    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
								    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
								    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
								    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
								    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
								    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
								    TF_MODEL_MAPPING,
								    TF_MODEL_WITH_LM_HEAD_MAPPING,
								    TFAutoModel,
								    TFAutoModelForAudioClassification,
								    TFAutoModelForCausalLM,
								    TFAutoModelForDocumentQuestionAnswering,
								    TFAutoModelForImageClassification,
								    TFAutoModelForMaskedImageModeling,
								    TFAutoModelForMaskedLM,
								    TFAutoModelForMaskGeneration,
								    TFAutoModelForMultipleChoice,
								    TFAutoModelForNextSentencePrediction,
								    TFAutoModelForPreTraining,
								    TFAutoModelForQuestionAnswering,
								    TFAutoModelForSemanticSegmentation,
								    TFAutoModelForSeqaSeqLM,
								    TFAutoModelForSequenceClassification,
								    TFAutoModelForSpeechSeqaSeq,
								    TFAutoModelForTableQuestionAnswering,
								    TFAutoModelForTextEncoding,
								    TFAutoModelForTokenClassification,
								    TFAutoModelForVisionaSeq,
								    TFAutoModelForZeroShotImageClassification,
								    TFAutoModelWithLMHead,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_auto import (
								    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
								    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
								    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
								    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
								    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
								    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
								    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
								    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
								    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
								    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
								    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
								    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
								    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
								    FLAX_MODEL_MAPPING,
								    FlaxAutoModel,
								    FlaxAutoModelForCausalLM,
								    FlaxAutoModelForImageClassification,
								    FlaxAutoModelForMaskedLM,
								    FlaxAutoModelForMultipleChoice,
								    FlaxAutoModelForNextSentencePrediction,
								    FlaxAutoModelForPreTraining,
								    FlaxAutoModelForQuestionAnswering,
								    FlaxAutoModelForSeqaSeqLM,
								    FlaxAutoModelForSequenceClassification,
								    FlaxAutoModelForSpeechSeqaSeq,
								    FlaxAutoModelForTokenClassification,
								    FlaxAutoModelForVisionaSeq,
								)

else:
				import sys

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










import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''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''': 55.1, '''ROUGE-1''': 7_6},
				}
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				self.assertTrue(hasattr(_A							, '''model_details'''		)		)
				self.assertTrue(hasattr(_A							, '''intended_use'''		)		)
				self.assertTrue(hasattr(_A							, '''factors'''		)		)
				self.assertTrue(hasattr(_A							, '''metrics'''		)		)
				self.assertTrue(hasattr(_A							, '''evaluation_data'''		)		)
				self.assertTrue(hasattr(_A							, '''training_data'''		)		)
				self.assertTrue(hasattr(_A							, '''quantitative_analyses'''		)		)
				self.assertTrue(hasattr(_A							, '''ethical_considerations'''		)		)
				self.assertTrue(hasattr(_A							, '''caveats_and_recommendations'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				UpperCAmelCase       		=    json.loads(modelcard.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.assertEqual(obj[key]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

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

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







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model_card_first.save_pretrained(_A		)
							UpperCAmelCase       		=    ModelCard.from_pretrained(_A		)

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



