




'''simple docstring'''







import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard





class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			{
							    "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 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			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 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			ModelCard.from_dict(self.inputs_dict					)
							_snake_case      			=			json.loads(modelcard.to_json_string()					)
							for key, value in self.inputs_dict.items():
									self.assertEqual(obj[key],    __UpperCAmelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			ModelCard.from_dict(self.inputs_dict					)

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

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			ModelCard.from_dict(self.inputs_dict					)

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

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






"""simple docstring"""




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


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





