




"""simple docstring"""




import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		BlenderbotSmallTokenizer
				__UpperCamelCase    						=		False





				def      _lowerCAmelCase       ( self				):





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

					lowerCamelCase       =      ["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
					lowerCamelCase       =      dict(zip(_a	,				range(len(_a				)				)				)				)

					lowerCamelCase       =      ["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
					lowerCamelCase       =      {"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

					lowerCamelCase       =      os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["""vocab_file"""]				)
					lowerCamelCase       =      os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["""merges_file"""]				)
					with open(self.vocab_file	,				"""w"""	,				encoding="""utf-8"""				) as fp:
						fp.write(json.dumps(_a				) + """\n"""				)
					with open(self.merges_file	,				"""w"""	,				encoding="""utf-8"""				) as fp:
						fp.write("""\n""".join(_a				)				)





				def      _lowerCAmelCase       ( self	,				**_a				):





					"""simple docstring"""
					kwargs.update(self.special_tokens_map				)
					return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





					"""simple docstring"""
					lowerCamelCase       =      """adapt act apte"""
					lowerCamelCase       =      """adapt act apte"""
					return input_text, output_text





				def      _lowerCAmelCase       ( self				):





					"""simple docstring"""
					lowerCamelCase       =      BlenderbotSmallTokenizer(self.vocab_file	,				self.merges_file	,				**self.special_tokens_map				)
					lowerCamelCase       =      """adapt act apte"""
					lowerCamelCase       =      ["""adapt""", """act""", """ap@@""", """te"""]
					lowerCamelCase       =      tokenizer.tokenize(_a				)
					self.assertListEqual(_a	,				_a				)

					lowerCamelCase       =      [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

					lowerCamelCase       =      [0, 1, 2, 3, 4, 5]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)	,				_a				)





				def      _lowerCAmelCase       ( self				):





					"""simple docstring"""
					lowerCamelCase       =      BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""				)
					assert tok("""sam"""				).input_ids == [1_384]
					lowerCamelCase       =      """I am a small frog."""
					lowerCamelCase       =      tok([src_text]	,				padding=_a	,				truncation=_a				)["""input_ids"""]
					lowerCamelCase       =      tok.batch_decode(_a	,				skip_special_tokens=_a	,				clean_up_tokenization_spaces=_a				)[0]
					assert src_text != decoded  # I wish it did!
					assert decoded == "i am a small frog ."





				def      _lowerCAmelCase       ( self				):





					"""simple docstring"""
					lowerCamelCase       =      BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""				)
					lowerCamelCase       =      """I am a small frog ."""
					lowerCamelCase       =      """."""
					lowerCamelCase       =      tok(_a				)["""input_ids"""]
					lowerCamelCase       =      tok(_a				)["""input_ids"""]

					assert encoded[-1] == encoded_dot[0]









"""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()



