


"""simple docstring"""






import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						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        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      ModelCard.from_dict(self.inputs_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"model_details"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"intended_use"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"factors"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"metrics"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"evaluation_data"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"training_data"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"quantitative_analyses"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"ethical_considerations"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"caveats_and_recommendations"))




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      ModelCard.from_dict(self.inputs_dict)
								snake_case__  :						Any		=      json.loads(modelcard.to_json_string())
								for key, value in self.inputs_dict.items():
									self.assertEqual(obj[key]	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[str]		=      ModelCard.from_dict(self.inputs_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									snake_case__  :						Optional[Any]		=      os.path.join(lowerCamelCase__	,					"modelcard.json")
									model_card_first.to_json_file(lowerCamelCase__)
									snake_case__  :						Dict		=      ModelCard.from_json_file(lowerCamelCase__)

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







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







								'''simple docstring'''




								snake_case__  :						str		=      ModelCard.from_dict(self.inputs_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									model_card_first.save_pretrained(lowerCamelCase__)
									snake_case__  :						Union[str, Any]		=      ModelCard.from_pretrained(lowerCamelCase__)

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









"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return self.tokenizer.batch_decode(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.decode(*lowerCamelCase__	,					**lowerCamelCase__)







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





