


import os
import sys
from pathlib import Path

import pytest

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

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 							UpperCamelCase_  (				)->      int:




						'''simple docstring'''
						__magic_name__ 		=			Dataset.from_dict({'i': range(17     )}     )
						__magic_name__ 		=			len(__snake_case     )
						__magic_name__ 		=			3
						__magic_name__ 		=			[
						    split_dataset_by_node(__snake_case	,			rank=__snake_case	,			world_size=__snake_case     ) for rank in range(__snake_case     )
						]
						assert sum(len(__snake_case     ) for ds in datasets_per_rank     ) == full_size
						assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
def 							UpperCamelCase_  (				)->      Optional[int]:




						'''simple docstring'''
						def gen():
												return ({"i": i} for i in range(17     ))

						__magic_name__ 		=			3
						__magic_name__ 		=			IterableDataset.from_generator(__snake_case     )
						__magic_name__ 		=			len(list(__snake_case     )     )
						__magic_name__ 		=			[
						    split_dataset_by_node(__snake_case	,			rank=__snake_case	,			world_size=__snake_case     ) for rank in range(__snake_case     )
						]
						assert sum(len(list(__snake_case     )     ) for ds in datasets_per_rank     ) == full_size
						assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
@pytest.mark.parametrize('shards_per_node'	,			[1, 2, 3]     )
def 							UpperCamelCase_  (				__snake_case			:						Dict     )->      Dict:




						'''simple docstring'''
						def gen(__snake_case			:						Any     ):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17     ))

						__magic_name__ 		=			3
						__magic_name__ 		=			shards_per_node * world_size
						__magic_name__ 		=			{'shards': [F'''shard_{shard_idx}.txt''' for shard_idx in range(__snake_case     )]}
						__magic_name__ 		=			IterableDataset.from_generator(__snake_case	,			gen_kwargs=__snake_case     )
						__magic_name__ 		=			len(list(__snake_case     )     )
						assert full_ds.n_shards == world_size * shards_per_node
						__magic_name__ 		=			[
						    split_dataset_by_node(__snake_case	,			rank=__snake_case	,			world_size=__snake_case     ) for rank in range(__snake_case     )
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__snake_case     )     ) for ds in datasets_per_rank     ) == full_size
						assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
def 							UpperCamelCase_  (				)->      Dict:




						'''simple docstring'''
						def gen():
												return ({"i": i} for i in range(17     ))

						__magic_name__ 		=			2
						__magic_name__ 		=			IterableDataset.from_generator(__snake_case     )
						__magic_name__ 		=			len(list(__snake_case     )     )

						__magic_name__ 		=			split_dataset_by_node(__snake_case	,			rank=0	,			world_size=__snake_case     ).shuffle(seed=42     )
						assert len(list(__snake_case     )     ) == 1 + full_size // world_size
						with pytest.raises(__snake_case     ):
												split_dataset_by_node(__snake_case	,			rank=0	,			world_size=__snake_case     ).shuffle()

						__magic_name__ 		=			split_dataset_by_node(full_ds.shuffle(seed=42     )	,			rank=0	,			world_size=__snake_case     )
						assert len(list(__snake_case     )     ) == 1 + full_size // world_size
						with pytest.raises(__snake_case     ):
												split_dataset_by_node(full_ds.shuffle()	,			rank=0	,			world_size=__snake_case     )
@pytest.mark.parametrize('streaming'	,			[False, True]     )
@require_torch
@pytest.mark.skipif(os.name == 'nt'	,			reason='execute_subprocess_async doesn\'t support windows'     )
@pytest.mark.integration
def 							UpperCamelCase_  (				__snake_case			:						Dict     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			2
						__magic_name__ 		=			get_torch_dist_unique_port()
						__magic_name__ 		=			Path(__snake_case     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
						__magic_name__ 		=			F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						__magic_name__ 		=			F'''
        --streaming={streaming}
    '''.split()
						__magic_name__ 		=			[sys.executable] + distributed_args + args
						execute_subprocess_async(__snake_case	,			env=os.environ.copy()     )




@pytest.mark.parametrize(
    'nproc_per_node, num_workers'	,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]	,			)
@require_torch
@pytest.mark.skipif(os.name == 'nt'	,			reason='execute_subprocess_async doesn\'t support windows'     )
@pytest.mark.integration
def 							UpperCamelCase_  (				__snake_case			:						List[Any]	,			__snake_case			:						int     )->      Union[str, Any]:




						'''simple docstring'''
						__magic_name__ 		=			True
						__magic_name__ 		=			get_torch_dist_unique_port()
						__magic_name__ 		=			Path(__snake_case     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
						__magic_name__ 		=			F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						__magic_name__ 		=			F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
						__magic_name__ 		=			[sys.executable] + distributed_args + args
						execute_subprocess_async(__snake_case	,			env=os.environ.copy()     )



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

