






'''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   :							int         =   4
__UpperCamelCase   :							Any         =   3
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      pass







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str]     )	->      int:

   """simple docstring"""
   for shard in shards:
      for i in range(SCREAMING_SNAKE_CASE__     ):
         yield {"i": i, "shard": shard}



def  __UpperCAmelCase						(					)	->      Optional[int]:

   """simple docstring"""
   __a					  =     int(os.environ['RANK']     )
   __a					  =     int(os.environ['WORLD_SIZE']     )

   __a					  =     ArgumentParser()
   parser.add_argument('--streaming',       type=SCREAMING_SNAKE_CASE__     )
   parser.add_argument('--local_rank',       type=SCREAMING_SNAKE_CASE__     )
   parser.add_argument('--num_workers',       type=SCREAMING_SNAKE_CASE__,       default=0     )
   __a					  =     parser.parse_args()
   __a					  =     args.streaming
   __a					  =     args.num_workers

   __a					  =     {'shards': [f"""shard_{shard_idx}""" for shard_idx in range(SCREAMING_SNAKE_CASE__     )]}
   __a					  =     IterableDataset.from_generator(SCREAMING_SNAKE_CASE__,       gen_kwargs=SCREAMING_SNAKE_CASE__     )
   if not streaming:
      __a					  =     Dataset.from_list(list(SCREAMING_SNAKE_CASE__     )     )

   __a					  =     split_dataset_by_node(SCREAMING_SNAKE_CASE__,       rank=SCREAMING_SNAKE_CASE__,       world_size=SCREAMING_SNAKE_CASE__     )
   __a					  =     torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE__,       num_workers=SCREAMING_SNAKE_CASE__     )

   __a					  =     NUM_SHARDS * NUM_ITEMS_PER_SHARD
   __a					  =     full_size // world_size
   expected_local_size += int(rank < (full_size % world_size)     )

   __a					  =     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()







'''simple docstring'''



import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							int         =   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_text_model"




      def __init__(	self			, lowerCamelCase=3_0522			, lowerCamelCase=768			, lowerCamelCase=12			, lowerCamelCase=12			, lowerCamelCase=3072			, lowerCamelCase="gelu"			, lowerCamelCase=0.1			, lowerCamelCase=0.1			, lowerCamelCase=512			, lowerCamelCase=2			, lowerCamelCase=0.02			, lowerCamelCase=1e-12			, lowerCamelCase=0			, lowerCamelCase="absolute"			, lowerCamelCase=True			, **lowerCamelCase			, )				->List[Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     vocab_size
         __a					  =     hidden_size
         __a					  =     num_hidden_layers
         __a					  =     num_attention_heads
         __a					  =     hidden_act
         __a					  =     intermediate_size
         __a					  =     hidden_dropout_prob
         __a					  =     attention_probs_dropout_prob
         __a					  =     max_position_embeddings
         __a					  =     type_vocab_size
         __a					  =     initializer_range
         __a					  =     layer_norm_eps
         __a					  =     position_embedding_type
         __a					  =     use_cache
         __a					  =     pad_token_id






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the text config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['text_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_vision_model"




      def __init__(	self			, lowerCamelCase = 3			, lowerCamelCase = 600			, lowerCamelCase = 2.0			, lowerCamelCase = 3.1			, lowerCamelCase = 8			, lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]			, lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]			, lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]			, lowerCamelCase = []			, lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]			, lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]			, lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]			, lowerCamelCase = 0.25			, lowerCamelCase = "swish"			, lowerCamelCase = 2560			, lowerCamelCase = "mean"			, lowerCamelCase = 0.02			, lowerCamelCase = 0.001			, lowerCamelCase = 0.99			, lowerCamelCase = 0.2			, **lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     num_channels
         __a					  =     image_size
         __a					  =     width_coefficient
         __a					  =     depth_coefficient
         __a					  =     depth_divisor
         __a					  =     kernel_sizes
         __a					  =     in_channels
         __a					  =     out_channels
         __a					  =     depthwise_padding
         __a					  =     strides
         __a					  =     num_block_repeats
         __a					  =     expand_ratios
         __a					  =     squeeze_expansion_ratio
         __a					  =     hidden_act
         __a					  =     hidden_dim
         __a					  =     pooling_type
         __a					  =     initializer_range
         __a					  =     batch_norm_eps
         __a					  =     batch_norm_momentum
         __a					  =     drop_connect_rate
         __a					  =     sum(lowerCamelCase   ) * 4






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the vision config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align"
      __a	       =True




      def __init__(	self			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase=640			, lowerCamelCase=1.0			, lowerCamelCase=0.02			, **lowerCamelCase			, )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         if text_config is None:
            __a					  =     {}
            logger.info('text_config is None. Initializing the AlignTextConfig with default values.'   )

         if vision_config is None:
            __a					  =     {}
            logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'   )

         __a					  =     AlignTextConfig(**lowerCamelCase   )
         __a					  =     AlignVisionConfig(**lowerCamelCase   )

         __a					  =     projection_dim
         __a					  =     temperature_init_value
         __a					  =     initializer_range




      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, lowerCamelCase			, **lowerCamelCase   )				->int:



         '''simple docstring'''
         return cls(text_config=text_config.to_dict()			, vision_config=vision_config.to_dict()			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     copy.deepcopy(self.__dict__   )
         __a					  =     self.text_config.to_dict()
         __a					  =     self.vision_config.to_dict()
         __a					  =     self.__class__.model_type
         return output