"""simple docstring"""



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 def        _lowerCAmelCase				(   self		:      str				)				->			str:




    '''simple docstring'''






    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''': 55.1, '''ROUGE-1''': 76},
    }


 def        _lowerCAmelCase				(   self		:      int				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		ModelCard.from_dict(self.inputs_dict				)
    self.assertTrue(hasattr(_snake_case					,   'model_details'				)				)
    self.assertTrue(hasattr(_snake_case					,   'intended_use'				)				)
    self.assertTrue(hasattr(_snake_case					,   'factors'				)				)
    self.assertTrue(hasattr(_snake_case					,   'metrics'				)				)
    self.assertTrue(hasattr(_snake_case					,   'evaluation_data'				)				)
    self.assertTrue(hasattr(_snake_case					,   'training_data'				)				)
    self.assertTrue(hasattr(_snake_case					,   'quantitative_analyses'				)				)
    self.assertTrue(hasattr(_snake_case					,   'ethical_considerations'				)				)
    self.assertTrue(hasattr(_snake_case					,   'caveats_and_recommendations'				)				)


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    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]					,   _snake_case				)


 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			Any:




    '''simple docstring'''






    a__     				=		ModelCard.from_dict(self.inputs_dict				)

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

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



 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		ModelCard.from_dict(self.inputs_dict				)

    with tempfile.TemporaryDirectory() as tmpdirname:
       model_card_first.save_pretrained(_snake_case				)
       a__     				=		ModelCard.from_pretrained(_snake_case				)

    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_torch_available


__magic_name__    				=					{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "HubertForCTC",
         "HubertForSequenceClassification",
         "HubertModel",
         "HubertPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFHubertForCTC",
         "TFHubertModel",
         "TFHubertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_hubert import (
              HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              HubertForCTC,
              HubertForSequenceClassification,
              HubertModel,
              HubertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_hubert import (
              TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFHubertForCTC,
              TFHubertModel,
              TFHubertPreTrainedModel,
          )


else:
     import sys

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


