




"""simple docstring"""




import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		FunnelTokenizer
				__UpperCamelCase    						=		FunnelTokenizerFast
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		True





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							super().setUp()

							lowerCamelCase       =      [
							    """<unk>""",
							    """<cls>""",
							    """<sep>""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCamelCase       =      os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["""vocab_file"""]				)
							with open(self.vocab_file	,				"""w"""	,				encoding="""utf-8"""				) as vocab_writer:
										vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							return FunnelTokenizer.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							return FunnelTokenizerFast.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      """UNwant\u00E9d,running"""
							lowerCamelCase       =      """unwanted, running"""
							return input_text, output_text





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.tokenizer_class(self.vocab_file				)

							lowerCamelCase       =      tokenizer.tokenize("""UNwant\u00E9d,running"""				)
							self.assertListEqual(_a	,				["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]				)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)	,				[7, 4, 5, 10, 8, 9]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_tokenizers(do_lower_case=_a				)
							for tokenizer in tokenizers:
										lowerCamelCase       =      tokenizer("""UNwant\u00E9d,running"""				)
										lowerCamelCase       =      len(inputs["""input_ids"""]				) - 1
										self.assertListEqual(inputs["""token_type_ids"""]	,				[2] + [0] * sentence_len				)

										lowerCamelCase       =      tokenizer("""UNwant\u00E9d,running"""	,				"""UNwant\u00E9d,running"""				)
										self.assertListEqual(inputs["""token_type_ids"""]	,				[2] + [0] * sentence_len + [1] * sentence_len				)









"""simple docstring"""




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


lowerCAmelCase							:    List[str]  							=							4
lowerCAmelCase							:    List[str]  							=							3






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				pass




def 					a__				(       snake_case__       )		->      Dict:
			for shard in shards:
						for i in range(snake_case__       ):
									yield {"i": i, "shard": shard}



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      int(os.environ["""RANK"""]       )
			lowerCamelCase       =      int(os.environ["""WORLD_SIZE"""]       )

			lowerCamelCase       =      ArgumentParser()
			parser.add_argument("""--streaming""" ,				type=snake_case__       )
			parser.add_argument("""--local_rank""" ,				type=snake_case__       )
			parser.add_argument("""--num_workers""" ,				type=snake_case__ ,				default=0       )
			lowerCamelCase       =      parser.parse_args()
			lowerCamelCase       =      args.streaming
			lowerCamelCase       =      args.num_workers

			lowerCamelCase       =      {"""shards""": [F'shard_{shard_idx}' for shard_idx in range(snake_case__       )]}
			lowerCamelCase       =      IterableDataset.from_generator(snake_case__ ,				gen_kwargs=snake_case__       )
			if not streaming:
						lowerCamelCase       =      Dataset.from_list(list(snake_case__       )       )

			lowerCamelCase       =      split_dataset_by_node(snake_case__ ,				rank=snake_case__ ,				world_size=snake_case__       )
			lowerCamelCase       =      torch.utils.data.DataLoader(snake_case__ ,				num_workers=snake_case__       )

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

			lowerCamelCase       =      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()



