


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											=       4
lowerCamelCase											=       3




class A  (   UpperCamelCase_     ):
      pass




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]     ):
       '''simple docstring'''





       for shard in shards:
              for i in range(SCREAMING_SNAKE_CASE__     ):
                     yield {"i": i, "shard": shard}




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[int]		=int(os.environ['RANK']     )
       _lowerCamelCase       :		str		=int(os.environ['WORLD_SIZE']     )

       _lowerCamelCase       :		Union[str, Any]		=ArgumentParser()
       parser.add_argument('--streaming'		,			type=SCREAMING_SNAKE_CASE__     )
       parser.add_argument('--local_rank'		,			type=SCREAMING_SNAKE_CASE__     )
       parser.add_argument('--num_workers'		,			type=SCREAMING_SNAKE_CASE__		,			default=0     )
       _lowerCamelCase       :		Any		=parser.parse_args()
       _lowerCamelCase       :		Union[str, Any]		=args.streaming
       _lowerCamelCase       :		List[str]		=args.num_workers

       _lowerCamelCase       :		List[Any]		={'shards': [F'''shard_{shard_idx}''' for shard_idx in range(SCREAMING_SNAKE_CASE__     )]}
       _lowerCamelCase       :		Union[str, Any]		=IterableDataset.from_generator(SCREAMING_SNAKE_CASE__		,			gen_kwargs=SCREAMING_SNAKE_CASE__     )
       if not streaming:
              _lowerCamelCase       :		List[Any]		=Dataset.from_list(list(SCREAMING_SNAKE_CASE__     )     )

       _lowerCamelCase       :		Optional[int]		=split_dataset_by_node(SCREAMING_SNAKE_CASE__		,			rank=SCREAMING_SNAKE_CASE__		,			world_size=SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Union[str, Any]		=torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE__		,			num_workers=SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		int		=NUM_SHARDS * NUM_ITEMS_PER_SHARD
       _lowerCamelCase       :		Any		=full_size // world_size
       expected_local_size += int(rank < (full_size % world_size)     )

       _lowerCamelCase       :		Union[str, Any]		=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()






import os
import sys
import unittest


lowerCamelCase											=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase											=       os.path.join(git_repo_path, 'src', 'transformers')

lowerCamelCase											=       '\n{0} = None\n'

lowerCamelCase											=       '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


lowerCamelCase											=       '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'




class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
             self.assertIsNone(lowercase_ )

             _lowerCamelCase       :		List[str]		=find_backend('    if not is_tokenizers_available():' )
             self.assertEqual(lowercase_			,   'tokenizers' )

             _lowerCamelCase       :		List[Any]		=find_backend('    if not is_tensorflow_text_available():' )
             self.assertEqual(lowercase_			,   'tensorflow_text' )

             _lowerCamelCase       :		int		=find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers' )

             _lowerCamelCase       :		Dict		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tensorflow_text' )

             _lowerCamelCase       :		List[Any]		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers_and_vision' )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=read_init()
             # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
             self.assertIn('torch'			,   lowercase_ )
             self.assertIn('tensorflow_text'			,   lowercase_ )
             self.assertIn('sentencepiece_and_tokenizers'			,   lowercase_ )

             # Likewise, we can't assert on the exact content of a key
             self.assertIn('BertModel'			,   objects['torch'] )
             self.assertIn('TFBertModel'			,   objects['tf'] )
             self.assertIn('FlaxBertModel'			,   objects['flax'] )
             self.assertIn('BertModel'			,   objects['torch'] )
             self.assertIn('TFBertTokenizer'			,   objects['tensorflow_text'] )
             self.assertIn('convert_slow_tokenizer'			,   objects['sentencepiece_and_tokenizers'] )






      def        lowerCamelCase       (					self						:     int )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=create_dummy_object('CONSTANT'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   '\nCONSTANT = None\n' )

             _lowerCamelCase       :		Dict		=create_dummy_object('function'			,   '\'torch\'' )
             self.assertEqual(
                 lowercase_			,   '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

             _lowerCamelCase       :		Union[str, Any]		='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
             _lowerCamelCase       :		Tuple		=create_dummy_object('FakeClass'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Dict		='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
             _lowerCamelCase       :		Optional[int]		=create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
             self.assertEqual(dummy_files['torch']			,   lowercase_ )


