






import unittest

from transformers import DonutProcessor


lowercase    				=   "naver-clova-ix/donut-base"




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				DonutProcessor.from_pretrained(a   )






	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				{
								    'name': 'John Doe',
								    'age': '99',
								    'city': 'Atlanta',
								    'state': 'GA',
								    'zip': '30301',
								    'phone': '123-4567',
								    'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
								}

								snake_case_           =				(
								    '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
								    '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
								    '<s_nicknames><s_nickname>Johnny</s_nickname>'
								    '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
								)
								snake_case_           =				self.processor.tokenajson(a   )

								self.assertDictEqual(a			,     a   )













import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





