
'''simple docstring'''

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class 			UpperCAmelCase				(					unittest.TestCase ):





		"""simple docstring"""





		def _lowercase  (    self     )				->    int:
				_UpperCamelCase       : str    =      {
				    '''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 _lowercase  (    self     )				->    Tuple:
				_UpperCamelCase       : List[Any]    =      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 _lowercase  (    self     )				->    List[str]:
				_UpperCamelCase       : Dict    =      ModelCard.from_dict(self.inputs_dict     )
				_UpperCamelCase       : Dict    =      json.loads(modelcard.to_json_string()     )
				for key, value in self.inputs_dict.items():
						self.assertEqual(obj[key]  ,					_snake_case     )
		def _lowercase  (    self     )				->    Any:
				_UpperCamelCase       : Optional[int]    =      ModelCard.from_dict(self.inputs_dict     )

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

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




		def _lowercase  (    self     )				->    List[Any]:
				_UpperCamelCase       : Dict    =      ModelCard.from_dict(self.inputs_dict     )

				with tempfile.TemporaryDirectory() as tmpdirname:
						model_card_first.save_pretrained(_snake_case     )
						_UpperCamelCase       : List[str]    =      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_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase   :      int 						=  {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase   :      int 						=  [
	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTSanJapaneseForConditionalGeneration""",
	    """GPTSanJapaneseModel""",
	    """GPTSanJapanesePreTrainedModel""",
	]
	_UpperCAmelCase   :      str 						=  [
	    """GPTSanJapaneseTokenizer""",
	]


if TYPE_CHECKING:
	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptsan_japanese import (
		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTSanJapaneseForConditionalGeneration,
		    GPTSanJapaneseModel,
		    GPTSanJapanesePreTrainedModel,
		)
		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
	import sys

	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





