






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       ( )->    List[str]:





						"""simple docstring"""



						_UpperCAmelCase							   =     Dataset.from_dict({'i': range(17      )}      )
						_UpperCAmelCase							   =     len(__lowerCAmelCase      )
						_UpperCAmelCase							   =     3
						_UpperCAmelCase							   =     [
						    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 __A       ( )->    List[Any]:





						"""simple docstring"""



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

						_UpperCAmelCase							   =     3
						_UpperCAmelCase							   =     IterableDataset.from_generator(__lowerCAmelCase      )
						_UpperCAmelCase							   =     len(list(__lowerCAmelCase      )      )
						_UpperCAmelCase							   =     [
						    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 __A       ( __lowerCAmelCase      )->    List[str]:





						"""simple docstring"""



						def gen(__lowerCAmelCase      ):
												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(__lowerCAmelCase      )]}
						_UpperCAmelCase							   =     IterableDataset.from_generator(__lowerCAmelCase					,						gen_kwargs=__lowerCAmelCase      )
						_UpperCAmelCase							   =     len(list(__lowerCAmelCase      )      )
						assert full_ds.n_shards == world_size * shards_per_node
						_UpperCAmelCase							   =     [
						    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 __A       ( )->    Union[str, Any]:





						"""simple docstring"""



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

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

						_UpperCAmelCase							   =     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()

						_UpperCAmelCase							   =     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 __A       ( __lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     2
						_UpperCAmelCase							   =     get_torch_dist_unique_port()
						_UpperCAmelCase							   =     Path(__lowerCAmelCase      ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
						_UpperCAmelCase							   =     F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_UpperCAmelCase							   =     F"""
        --streaming={streaming}
    """.split()
						_UpperCAmelCase							   =     [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 __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    List[str]:





						"""simple docstring"""



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

"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__    :    List[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    Dict			=      {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"mobilevit"







					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      str=256       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Union[str, Any]=[144, 192, 240]       ,lowerCamelCase__  :      Optional[int]=[16, 32, 64, 96, 128, 160, 640]       ,lowerCamelCase__  :      Dict=4       ,lowerCamelCase__  :      int=2.0       ,lowerCamelCase__  :      Optional[Any]=4.0       ,lowerCamelCase__  :      Optional[int]="silu"       ,lowerCamelCase__  :      List[str]=3       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      int=0.1       ,lowerCamelCase__  :      List[str]=0.0       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Tuple=0.0_2       ,lowerCamelCase__  :      Union[str, Any]=1e-5       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      Dict=256       ,lowerCamelCase__  :      Any=[6, 12, 18]       ,lowerCamelCase__  :      Optional[int]=0.1       ,lowerCamelCase__  :      List[str]=255       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )

									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				patch_size
									UpperCAmelCase__			 =				hidden_sizes
									UpperCAmelCase__			 =				neck_hidden_sizes
									UpperCAmelCase__			 =				num_attention_heads
									UpperCAmelCase__			 =				mlp_ratio
									UpperCAmelCase__			 =				expand_ratio
									UpperCAmelCase__			 =				hidden_act
									UpperCAmelCase__			 =				conv_kernel_size
									UpperCAmelCase__			 =				output_stride
									UpperCAmelCase__			 =				hidden_dropout_prob
									UpperCAmelCase__			 =				attention_probs_dropout_prob
									UpperCAmelCase__			 =				classifier_dropout_prob
									UpperCAmelCase__			 =				initializer_range
									UpperCAmelCase__			 =				layer_norm_eps
									UpperCAmelCase__			 =				qkv_bias

									# decode head attributes for semantic segmentation
									UpperCAmelCase__			 =				aspp_out_channels
									UpperCAmelCase__			 =				atrous_rates
									UpperCAmelCase__			 =				aspp_dropout_prob
									UpperCAmelCase__			 =				semantic_loss_ignore_index






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					version.parse("1.11"							)







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									if self.task == "image-classification":
													return OrderedDict([('logits', {0: 'batch'})]     )
									else:
													return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return 1e-4



