

"""simple docstring"""





import warnings
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


__UpperCamelCase    : Optional[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Any				 =						{
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"segformer"







					def __init__(			self     :							Optional[int]  ,lowercase_     :							Union[str, Any]=3  ,lowercase_     :							Dict=4  ,lowercase_     :							Union[str, Any]=[2, 2, 2, 2]  ,lowercase_     :							Optional[Any]=[8, 4, 2, 1]  ,lowercase_     :							Optional[int]=[3_2, 6_4, 1_6_0, 2_5_6]  ,lowercase_     :							Dict=[7, 3, 3, 3]  ,lowercase_     :							Dict=[4, 2, 2, 2]  ,lowercase_     :							int=[1, 2, 5, 8]  ,lowercase_     :							Optional[Any]=[4, 4, 4, 4]  ,lowercase_     :							List[str]="gelu"  ,lowercase_     :							Optional[Any]=0.0  ,lowercase_     :							List[str]=0.0  ,lowercase_     :							List[str]=0.1  ,lowercase_     :							List[str]=0.02  ,lowercase_     :							Any=0.1  ,lowercase_     :							List[Any]=1E-6  ,lowercase_     :							int=2_5_6  ,lowercase_     :							Any=2_5_5  ,**lowercase_     :							Tuple  ,):
						super().__init__(**lowercase_						)

						if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
							warnings.warn(
							    '''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
							    ''' removed, as the behaviour will default to that of reshape_last_stage = True.'''  ,lowercase_  ,)

						lowerCAmelCase__      :		Union[str, Any]    							=      num_channels
						lowerCAmelCase__      :		int    							=      num_encoder_blocks
						lowerCAmelCase__      :		List[str]    							=      depths
						lowerCAmelCase__      :		int    							=      sr_ratios
						lowerCAmelCase__      :		Tuple    							=      hidden_sizes
						lowerCAmelCase__      :		Any    							=      patch_sizes
						lowerCAmelCase__      :		str    							=      strides
						lowerCAmelCase__      :		List[str]    							=      mlp_ratios
						lowerCAmelCase__      :		List[str]    							=      num_attention_heads
						lowerCAmelCase__      :		Optional[int]    							=      hidden_act
						lowerCAmelCase__      :		str    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Tuple    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		List[Any]    							=      classifier_dropout_prob
						lowerCAmelCase__      :		int    							=      initializer_range
						lowerCAmelCase__      :		int    							=      drop_path_rate
						lowerCAmelCase__      :		List[str]    							=      layer_norm_eps
						lowerCAmelCase__      :		Dict    							=      decoder_hidden_size
						lowerCAmelCase__      :		int    							=      kwargs.get('''reshape_last_stage'''  ,lowercase_						)
						lowerCAmelCase__      :		List[str]    							=      semantic_loss_ignore_index







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			version.parse("1.11"			)







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







					@property
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						return 1E-4







					@property
					def 			__lowerCAmelCase					(			self     :							int						):
						return 1_2







"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




