



"""simple docstring"""




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   lowercase__     (			)     ->			Optional[int]:






						'''simple docstring'''


						lowercase  :      List[str]					=   Dataset.from_dict({'i': range(17				)}				)
						lowercase  :      Optional[Any]					=   len(_UpperCAmelCase				)
						lowercase  :      Dict					=   3
						lowercase  :      Dict					=   [
						    split_dataset_by_node(_UpperCAmelCase	,      rank=_UpperCAmelCase	,      world_size=_UpperCAmelCase				) for rank in range(_UpperCAmelCase				)
						]
						assert sum(len(_UpperCAmelCase				) 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   lowercase__     (			)     ->			List[Any]:






						'''simple docstring'''


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

						lowercase  :      List[Any]					=   3
						lowercase  :      Dict					=   IterableDataset.from_generator(_UpperCAmelCase				)
						lowercase  :      Tuple					=   len(list(_UpperCAmelCase				)				)
						lowercase  :      str					=   [
						    split_dataset_by_node(_UpperCAmelCase	,      rank=_UpperCAmelCase	,      world_size=_UpperCAmelCase				) for rank in range(_UpperCAmelCase				)
						]
						assert sum(len(list(_UpperCAmelCase				)				) 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   lowercase__     (			_UpperCAmelCase				)     ->			Optional[int]:






						'''simple docstring'''


						def gen(_UpperCAmelCase				):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17				))

						lowercase  :      Any					=   3
						lowercase  :      str					=   shards_per_node * world_size
						lowercase  :      Optional[int]					=   {'shards': [f'''shard_{shard_idx}.txt''' for shard_idx in range(_UpperCAmelCase				)]}
						lowercase  :      Optional[int]					=   IterableDataset.from_generator(_UpperCAmelCase	,      gen_kwargs=_UpperCAmelCase				)
						lowercase  :      str					=   len(list(_UpperCAmelCase				)				)
						assert full_ds.n_shards == world_size * shards_per_node
						lowercase  :      Any					=   [
						    split_dataset_by_node(_UpperCAmelCase	,      rank=_UpperCAmelCase	,      world_size=_UpperCAmelCase				) for rank in range(_UpperCAmelCase				)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(_UpperCAmelCase				)				) 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   lowercase__     (			)     ->			Tuple:






						'''simple docstring'''


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

						lowercase  :      List[str]					=   2
						lowercase  :      List[Any]					=   IterableDataset.from_generator(_UpperCAmelCase				)
						lowercase  :      Optional[int]					=   len(list(_UpperCAmelCase				)				)

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

						lowercase  :      List[Any]					=   split_dataset_by_node(full_ds.shuffle(seed=42				)	,      rank=0	,      world_size=_UpperCAmelCase				)
						assert len(list(_UpperCAmelCase				)				) == 1 + full_size // world_size
						with pytest.raises(_UpperCAmelCase				):
												split_dataset_by_node(full_ds.shuffle()	,      rank=0	,      world_size=_UpperCAmelCase				)


@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   lowercase__     (			_UpperCAmelCase				)     ->			Dict:






						'''simple docstring'''


						lowercase  :      int					=   2
						lowercase  :      Union[str, Any]					=   get_torch_dist_unique_port()
						lowercase  :      str					=   Path(_UpperCAmelCase				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
						lowercase  :      str					=   f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						lowercase  :      Any					=   f'''
        --streaming={streaming}
    '''.split()
						lowercase  :      Union[str, Any]					=   [sys.executable] + distributed_args + args
						execute_subprocess_async(_UpperCAmelCase	,      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   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			int:






						'''simple docstring'''


						lowercase  :      str					=   True
						lowercase  :      Union[str, Any]					=   get_torch_dist_unique_port()
						lowercase  :      Tuple					=   Path(_UpperCAmelCase				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
						lowercase  :      Dict					=   f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
						lowercase  :      Any					=   f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
						lowercase  :      Optional[int]					=   [sys.executable] + distributed_args + args
						execute_subprocess_async(_UpperCAmelCase	,      env=os.environ.copy()				)





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   args[1:]

         if audio is None and text is None:
               raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

         if audio is not None:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
