
"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			['''image_processor''', '''tokenizer''']
       UpperCamelCase_		:					List[Any]   =			'''BlipImageProcessor'''
       UpperCamelCase_		:					int   =			'''AutoTokenizer'''






       def __init__(  self :							Optional[int]       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							str			)->  Optional[Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_       ,      UpperCAmelCase_			)

              # add QFormer tokenizer
              UpperCamelCase					 =		qformer_tokenizer






       def __call__(  self :							Optional[Any]       ,      UpperCAmelCase_ :							ImageInput = None       ,      UpperCAmelCase_ :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Union[bool, str, PaddingStrategy] = False       ,      UpperCAmelCase_ :							Union[bool, str, TruncationStrategy] = None       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							int = 0       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							Optional[bool] = None       ,      UpperCAmelCase_ :							bool = False       ,      UpperCAmelCase_ :							bool = False       ,      UpperCAmelCase_ :							bool = False       ,      UpperCAmelCase_ :							bool = False       ,      UpperCAmelCase_ :							bool = False       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Optional[Union[str, TensorType]] = None       ,      **UpperCAmelCase_ :							int       ,      )->  BatchFeature:







              """simple docstring"""
              if images is None and text is None:
                     raise ValueError("You have to specify at least images or text."			)

              UpperCamelCase					 =		BatchFeature()

              if text is not None:
                     UpperCamelCase					 =		self.tokenizer(
                         text=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      stride=UpperCAmelCase_       ,      pad_to_multiple_of=UpperCAmelCase_       ,      return_attention_mask=UpperCAmelCase_       ,      return_overflowing_tokens=UpperCAmelCase_       ,      return_special_tokens_mask=UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      return_token_type_ids=UpperCAmelCase_       ,      return_length=UpperCAmelCase_       ,      verbose=UpperCAmelCase_       ,      return_tensors=UpperCAmelCase_       ,      **UpperCAmelCase_       ,      )
                     encoding.update(UpperCAmelCase_			)
                     UpperCamelCase					 =		self.qformer_tokenizer(
                         text=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      stride=UpperCAmelCase_       ,      pad_to_multiple_of=UpperCAmelCase_       ,      return_attention_mask=UpperCAmelCase_       ,      return_overflowing_tokens=UpperCAmelCase_       ,      return_special_tokens_mask=UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      return_token_type_ids=UpperCAmelCase_       ,      return_length=UpperCAmelCase_       ,      verbose=UpperCAmelCase_       ,      return_tensors=UpperCAmelCase_       ,      **UpperCAmelCase_       ,      )
                     UpperCamelCase					 =		qformer_text_encoding.pop("input_ids"			)
                     UpperCamelCase					 =		qformer_text_encoding.pop("attention_mask"			)

              if images is not None:
                     UpperCamelCase					 =		self.image_processor(UpperCAmelCase_       ,      return_tensors=UpperCAmelCase_			)
                     encoding.update(UpperCAmelCase_			)

              return encoding






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      *UpperCAmelCase_ :							List[str]       ,      **UpperCAmelCase_ :							Dict			)->  Optional[int]:







              """simple docstring"""
              return self.tokenizer.batch_decode(*UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      *UpperCAmelCase_ :							str       ,      **UpperCAmelCase_ :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              return self.tokenizer.decode(*UpperCAmelCase_       ,      **UpperCAmelCase_			)






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer.model_input_names
              UpperCamelCase					 =		self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Any       ,      **UpperCAmelCase_ :							Dict			)->  int:







              """simple docstring"""
              if os.path.isfile(UpperCAmelCase_			):
                     raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"			)
              os.makedirs(UpperCAmelCase_       ,      exist_ok=UpperCAmelCase_			)
              UpperCamelCase					 =		os.path.join(UpperCAmelCase_       ,      "qformer_tokenizer"			)
              self.qformer_tokenizer.save_pretrained(UpperCAmelCase_			)
              return super().save_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							Union[str, Any]       ,      UpperCAmelCase_ :							Tuple       ,      **UpperCAmelCase_ :							int			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		AutoTokenizer.from_pretrained(UpperCAmelCase_       ,      subfolder="qformer_tokenizer"			)
              UpperCamelCase					 =		cls._get_arguments_from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
              args.append(UpperCAmelCase_			)
              return cls(*UpperCAmelCase_			)







"""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


SCREAMING_SNAKE_CASE      =     4
SCREAMING_SNAKE_CASE      =     3



class   __a  (      _lowerCAmelCase	):
       pass





def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







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



def 	lowerCamelCase__			(							)->							Union[str, Any]:







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

       UpperCamelCase					 =		ArgumentParser()
       parser.add_argument("--streaming"		,      type=UpperCAmelCase_				)
       parser.add_argument("--local_rank"		,      type=UpperCAmelCase_				)
       parser.add_argument("--num_workers"		,      type=UpperCAmelCase_		,      default=0				)
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		args.streaming
       UpperCamelCase					 =		args.num_workers

       UpperCamelCase					 =		{"shards": [F"shard_{shard_idx}" for shard_idx in range(UpperCAmelCase_				)]}
       UpperCamelCase					 =		IterableDataset.from_generator(UpperCAmelCase_		,      gen_kwargs=UpperCAmelCase_				)
       if not streaming:
              UpperCamelCase					 =		Dataset.from_list(list(UpperCAmelCase_				)				)

       UpperCamelCase					 =		split_dataset_by_node(UpperCAmelCase_		,      rank=UpperCAmelCase_		,      world_size=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.utils.data.DataLoader(UpperCAmelCase_		,      num_workers=UpperCAmelCase_				)

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

       UpperCamelCase					 =		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()





