





import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard



class 				_UpperCamelCase       (						unittest.TestCase  ):

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




              """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": 76},
              }

       def lowercase			(		self:			Any		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	ModelCard.from_dict(self.inputs_dict		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "model_details"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "intended_use"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "factors"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "metrics"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "evaluation_data"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "training_data"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "quantitative_analyses"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "ethical_considerations"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "caveats_and_recommendations"		)		)

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




              """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]       , _SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			int		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	ModelCard.from_dict(self.inputs_dict		)

              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase_ 							=	os.path.join(_SCREAMING_SNAKE_CASE       , "modelcard.json"		)
                     model_card_first.to_json_file(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	ModelCard.from_json_file(_SCREAMING_SNAKE_CASE		)

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



       def lowercase			(		self:			Optional[int]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	ModelCard.from_dict(self.inputs_dict		)

              with tempfile.TemporaryDirectory() as tmpdirname:
                     model_card_first.save_pretrained(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	ModelCard.from_pretrained(_SCREAMING_SNAKE_CASE		)

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''qdqbert'''

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any=30522       , _SCREAMING_SNAKE_CASE:			List[str]=768       , _SCREAMING_SNAKE_CASE:			int=12       , _SCREAMING_SNAKE_CASE:			List[Any]=12       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=3072       , _SCREAMING_SNAKE_CASE:			List[str]="gelu"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Dict=512       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=2       , _SCREAMING_SNAKE_CASE:			int=0.02       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1e-12       , _SCREAMING_SNAKE_CASE:			str=True       , _SCREAMING_SNAKE_CASE:			Any=1       , _SCREAMING_SNAKE_CASE:			int=0       , _SCREAMING_SNAKE_CASE:			Tuple=2       , **_SCREAMING_SNAKE_CASE:			List[str]       , )							->			Tuple:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	max_position_embeddings
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	num_attention_heads
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	hidden_act
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	layer_norm_eps
              UpperCamelCase_ 							=	use_cache

