





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






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

							A						=    3
							A						=    IterableDataset.from_generator(lowercase__			)
							A						=    len(list(lowercase__			)			)
							A						=    [
							    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    A__							(     UpperCamelCase			):






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

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






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

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

							A						=    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()

							A						=    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    A__							(     UpperCamelCase			):
							A						=    2
							A						=    get_torch_dist_unique_port()
							A						=    Path(lowercase__			).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
							A						=    F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
							A						=    F"\n        --streaming={streaming}\n    ".split()
							A						=    [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    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    True
							A						=    get_torch_dist_unique_port()
							A						=    Path(lowercase__			).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
							A						=    F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
							A						=    F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
							A						=    [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__)

