
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__     :    int      							=					{
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     :    Any      							=					["""MarianTokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     :    Any      							=					[
         """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MarianForCausalLM""",
         """MarianModel""",
         """MarianMTModel""",
         """MarianPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     :    Dict      							=					["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     SCREAMING_SNAKE_CASE__     :    List[str]      							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





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 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) 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 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(list(__lowerCAmelCase							)							) 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 				__magic_name__				(     __lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				def gen(__lowerCAmelCase			:		Union[str, 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(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) 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 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

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

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




@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 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						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 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



