

'''simple docstring'''




import copy
import os
from typing import Union

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


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Dict      	=					"""blip_text_model"""



 def __init__(   self		:   Any			,				a__		:   Dict=3_0524			,				a__		:   Tuple=768			,				a__		:   Any=768			,				a__		:   List[Any]=3072			,				a__		:   List[str]=768			,				a__		:   Dict=12			,				a__		:   Dict=8			,				a__		:   Optional[Any]=512			,				a__		:   List[str]="gelu"			,				a__		:   Tuple=1E-12			,				a__		:   Dict=0.0			,				a__		:   int=0.0			,				a__		:   Any=0.02			,				a__		:   Union[str, Any]=3_0522			,				a__		:   str=2			,				a__		:   Dict=0			,				a__		:   int=102			,				a__		:   Any=True			,				a__		:   Optional[Any]=True			,				**a__		:   List[str]			,				):
      super().__init__(
          pad_token_id=a__			,				bos_token_id=a__			,				eos_token_id=a__			,				sep_token_id=a__			,				**a__			,				)

      __magic_name__						       =    vocab_size
      __magic_name__						       =    hidden_size
      __magic_name__						       =    encoder_hidden_size
      __magic_name__						       =    intermediate_size
      __magic_name__						       =    projection_dim
      __magic_name__						       =    hidden_dropout_prob
      __magic_name__						       =    num_hidden_layers
      __magic_name__						       =    num_attention_heads
      __magic_name__						       =    max_position_embeddings
      __magic_name__						       =    layer_norm_eps
      __magic_name__						       =    hidden_act
      __magic_name__						       =    initializer_range
      __magic_name__						       =    attention_probs_dropout_prob
      __magic_name__						       =    is_decoder
      __magic_name__						       =    use_cache



 @classmethod
 def 			snake_case__  (   cls		:   str			,				a__		:   Union[str, os.PathLike]			,				**a__		:   List[str]      ):
      cls._set_token_in_kwargs(a__      )

      __magic_name__       ,  __magic_name__						       =    cls.get_config_dict(a__			,				**a__      )

      # get the text config dict if we are loading from BlipConfig
      if config_dict.get('''model_type'''      ) == "blip":
           __magic_name__						       =    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(a__			,				**a__      )






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""blip_vision_model"""



 def __init__(   self		:   Tuple			,				a__		:   List[str]=768			,				a__		:   Optional[int]=3072			,				a__		:   Optional[int]=512			,				a__		:   Tuple=12			,				a__		:   Any=12			,				a__		:   Optional[Any]=384			,				a__		:   str=16			,				a__		:   Any="gelu"			,				a__		:   Tuple=1E-5			,				a__		:   Tuple=0.0			,				a__		:   Any=1E-10			,				**a__		:   Optional[int]			,				):
      super().__init__(**a__      )

      __magic_name__						       =    hidden_size
      __magic_name__						       =    intermediate_size
      __magic_name__						       =    projection_dim
      __magic_name__						       =    num_hidden_layers
      __magic_name__						       =    num_attention_heads
      __magic_name__						       =    patch_size
      __magic_name__						       =    image_size
      __magic_name__						       =    initializer_range
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    layer_norm_eps
      __magic_name__						       =    hidden_act



 @classmethod
 def 			snake_case__  (   cls		:   Any			,				a__		:   Union[str, os.PathLike]			,				**a__		:   Any      ):
      cls._set_token_in_kwargs(a__      )

      __magic_name__       ,  __magic_name__						       =    cls.get_config_dict(a__			,				**a__      )

      # get the vision config dict if we are loading from BlipConfig
      if config_dict.get('''model_type'''      ) == "blip":
           __magic_name__						       =    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(a__			,				**a__      )






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""blip"""
 __SCREAMING_SNAKE_CASE					:Dict      	=					True



 def __init__(   self		:   Optional[int]			,				a__		:   str=None			,				a__		:   List[str]=None			,				a__		:   List[Any]=512			,				a__		:   Tuple=2.6_592			,				a__		:   Any=256			,				**a__		:   Any			,				):
      super().__init__(**a__      )

      if text_config is None:
           __magic_name__						       =    {}
           logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''      )

      if vision_config is None:
           __magic_name__						       =    {}
           logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''      )

      __magic_name__						       =    BlipTextConfig(**a__      )
      __magic_name__						       =    BlipVisionConfig(**a__      )

      __magic_name__						       =    self.vision_config.hidden_size

      __magic_name__						       =    projection_dim
      __magic_name__						       =    logit_scale_init_value
      __magic_name__						       =    1.0
      __magic_name__						       =    0.02
      __magic_name__						       =    image_text_hidden_size



 @classmethod
 def 			snake_case__  (   cls		:   Optional[int]			,				a__		:   BlipTextConfig			,				a__		:   BlipVisionConfig			,				**a__		:   List[Any]      ):
      return cls(text_config=text_config.to_dict()			,				vision_config=vision_config.to_dict()			,				**a__      )



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    copy.deepcopy(self.__dict__      )
      __magic_name__						       =    self.text_config.to_dict()
      __magic_name__						       =    self.vision_config.to_dict()
      __magic_name__						       =    self.__class__.model_type
      return output









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






