




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()   )







"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




