


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class 						snake_case			(       unittest.TestCase	):
			'''simple docstring'''

			def _lowercase (	self     :     str					)					->					str:




								"""simple docstring"""






								SCREAMING_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 _lowercase (	self     :     Tuple					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       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[int]					)					->					Any:




								"""simple docstring"""






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

			def _lowercase (	self     :     Tuple					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ModelCard.from_dict(self.inputs_dict					)

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

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




			def _lowercase (	self     :     int					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ModelCard.from_dict(self.inputs_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
													model_card_first.save_pretrained(__SCREAMING_SNAKE_CASE					)
													SCREAMING_SNAKE_CASE_	      =       ModelCard.from_pretrained(__SCREAMING_SNAKE_CASE					)

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






from __future__ import annotations

class 						snake_case			:
			'''simple docstring'''

			def __init__(	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       value
								SCREAMING_SNAKE_CASE_	      =       None
								SCREAMING_SNAKE_CASE_	      =       None

class 						snake_case			:
			'''simple docstring'''

			UpperCAmelCase       :       int

			def __init__(	self     :     Union[str, Any]					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       0

			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int					)					->					None:




								"""simple docstring"""






								if node is None:
													return

								if path_sum == self.target:
													self.paths += 1

								if node.left:
													self.depth_first_search(node.left							,							path_sum + node.left.value					)
								if node.right:
													self.depth_first_search(node.right							,							path_sum + node.right.value					)




			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int | None = None					)					->					int:




								"""simple docstring"""






								if node is None:
													return 0
								if target is not None:
													SCREAMING_SNAKE_CASE_	      =       target

								self.depth_first_search(lowerCAmelCase_							,							node.value					)
								self.path_sum(node.left					)
								self.path_sum(node.right					)

								return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()


