




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        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    Dataset.from_dict({'''i''': range(17   )}   )
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    3
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert sum(len(lowercase_   ) 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        __SCREAMING_SNAKE_CASE (	)							->						Tuple:

    """simple docstring"""


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

    __UpperCamelCase			=    3
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert sum(len(list(lowercase_   )   ) 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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[str]:

    """simple docstring"""


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

    __UpperCamelCase			=    3
    __UpperCamelCase			=    shards_per_node * world_size
    __UpperCamelCase			=    {'''shards''': [F"shard_{shard_idx}.txt" for shard_idx in range(lowercase_   )]}
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_       ,  gen_kwargs=lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )
    assert full_ds.n_shards == world_size * shards_per_node
    __UpperCamelCase			=    [
        split_dataset_by_node(lowercase_       ,  rank=lowercase_       ,  world_size=lowercase_   ) for rank in range(lowercase_   )
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(lowercase_   )   ) 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        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


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

    __UpperCamelCase			=    2
    __UpperCamelCase			=    IterableDataset.from_generator(lowercase_   )
    __UpperCamelCase			=    len(list(lowercase_   )   )

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

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




@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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    2
    __UpperCamelCase			=    get_torch_dist_unique_port()
    __UpperCamelCase			=    Path(lowercase_   ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    __UpperCamelCase			=    F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
    __UpperCamelCase			=    F"\n        --streaming={streaming}\n    ".split()
    __UpperCamelCase			=    [sys.executable] + distributed_args + args
    execute_subprocess_async(lowercase_       ,  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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Union[str, Any]:

    """simple docstring"""


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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase					:				Optional[int]   	={
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				int   	=[
		    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2Text2ForCausalLM',
		    'Speech2Text2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
		from .processing_speech_to_text_a import SpeechaTextaProcessor
		from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text_a import (
				    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextaForCausalLM,
				    SpeechaTextaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase					:				Union[str, Any]   	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

