


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 		snake_case_   (				):






				'''simple docstring'''






				_lowerCAmelCase  						=Dataset.from_dict({"""i""": range(17     )}     )
				_lowerCAmelCase  						=len(lowercase__     )
				_lowerCAmelCase  						=3
				_lowerCAmelCase  						=[
				    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 		snake_case_   (				):






				'''simple docstring'''






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

				_lowerCAmelCase  						=3
				_lowerCAmelCase  						=IterableDataset.from_generator(lowercase__     )
				_lowerCAmelCase  						=len(list(lowercase__     )     )
				_lowerCAmelCase  						=[
				    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 		snake_case_   (				lowercase__     :    Dict     ):






				'''simple docstring'''






				def gen(lowercase__     :    Optional[Any]     ):
								for shard in shards:
												yield from ({"i": i, "shard": shard} for i in range(17     ))

				_lowerCAmelCase  						=3
				_lowerCAmelCase  						=shards_per_node * world_size
				_lowerCAmelCase  						={"""shards""": [f"shard_{shard_idx}.txt" for shard_idx in range(lowercase__     )]}
				_lowerCAmelCase  						=IterableDataset.from_generator(lowercase__       ,     gen_kwargs=lowercase__     )
				_lowerCAmelCase  						=len(list(lowercase__     )     )
				assert full_ds.n_shards == world_size * shards_per_node
				_lowerCAmelCase  						=[
				    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 		snake_case_   (				):






				'''simple docstring'''






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

				_lowerCAmelCase  						=2
				_lowerCAmelCase  						=IterableDataset.from_generator(lowercase__     )
				_lowerCAmelCase  						=len(list(lowercase__     )     )

				_lowerCAmelCase  						=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()

				_lowerCAmelCase  						=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 		snake_case_   (				lowercase__     :    int     ):






				'''simple docstring'''






				_lowerCAmelCase  						=2
				_lowerCAmelCase  						=get_torch_dist_unique_port()
				_lowerCAmelCase  						=Path(lowercase__     ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
				_lowerCAmelCase  						=f"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
				_lowerCAmelCase  						=f"\n        --streaming={streaming}\n    ".split()
				_lowerCAmelCase  						=[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 		snake_case_   (				lowercase__     :    Any       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

