
import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 		__magic_name__   (						unittest.TestCase    ):





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					List[Any]  	=     {
							    '''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''': 5_5.1, '''ROUGE-1''': 7_6},
							}





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Dict  	=     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       _A							(							self    :	Optional[Any]				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)
							lowerCAmelCase		:					Dict  	=     json.loads(modelcard.to_json_string()				)
							for key, value in self.inputs_dict.items():
												self.assertEqual(obj[key]						,  lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCAmelCase		:					str  	=     os.path.join(lowerCamelCase__						,  '''modelcard.json'''				)
												model_card_first.to_json_file(lowerCamelCase__				)
												lowerCAmelCase		:					Dict  	=     ModelCard.from_json_file(lowerCamelCase__				)

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





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					int  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model_card_first.save_pretrained(lowerCamelCase__				)
												lowerCAmelCase		:					Any  	=     ModelCard.from_pretrained(lowerCamelCase__				)

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


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
