






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_					=				logging.get_logger(__name__)

UpperCamelCase_					=				{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}




class 					__UpperCAmelCase (		_lowerCamelCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Tuple				       =					'vilt'




      def __init__(					self  ,		_UpperCAmelCase=3_0522  ,		_UpperCAmelCase=2  ,		_UpperCAmelCase=2  ,		_UpperCAmelCase=40  ,		_UpperCAmelCase=768  ,		_UpperCAmelCase=12  ,		_UpperCAmelCase=12  ,		_UpperCAmelCase=3072  ,		_UpperCAmelCase="gelu"  ,		_UpperCAmelCase=0.0  ,		_UpperCAmelCase=0.0  ,		_UpperCAmelCase=0.02  ,		_UpperCAmelCase=1E-12  ,		_UpperCAmelCase=384  ,		_UpperCAmelCase=32  ,		_UpperCAmelCase=3  ,		_UpperCAmelCase=True  ,		_UpperCAmelCase=-1  ,		_UpperCAmelCase=False  ,		_UpperCAmelCase=-1  ,		**_UpperCAmelCase  ,		):
             super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE  ,		**_SCREAMING_SNAKE_CASE						)

             UpperCAmelCase__	:   List[str]												=	vocab_size
             UpperCAmelCase__	:   Optional[Any]												=	type_vocab_size
             UpperCAmelCase__	:   Optional[Any]												=	modality_type_vocab_size
             UpperCAmelCase__	:   Union[str, Any]												=	max_position_embeddings

             UpperCAmelCase__	:   Union[str, Any]												=	hidden_size
             UpperCAmelCase__	:   Dict												=	num_hidden_layers
             UpperCAmelCase__	:   Any												=	num_attention_heads
             UpperCAmelCase__	:   Any												=	intermediate_size
             UpperCAmelCase__	:   Union[str, Any]												=	hidden_act
             UpperCAmelCase__	:   Optional[int]												=	hidden_dropout_prob
             UpperCAmelCase__	:   Dict												=	attention_probs_dropout_prob
             UpperCAmelCase__	:   Optional[int]												=	initializer_range
             UpperCAmelCase__	:   Dict												=	layer_norm_eps

             UpperCAmelCase__	:   List[Any]												=	image_size
             UpperCAmelCase__	:   Optional[int]												=	patch_size
             UpperCAmelCase__	:   Tuple												=	num_channels
             UpperCAmelCase__	:   List[Any]												=	qkv_bias
             UpperCAmelCase__	:   Optional[int]												=	max_image_length
             UpperCAmelCase__	:   Optional[int]												=	num_images

"""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        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) 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        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) 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        __lowerCAmelCase							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) 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        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

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

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@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        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)