

'''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     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[int]           =    ['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    'BlipImageProcessor'
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    'AutoTokenizer'






  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  Tuple      ,lowercase__     :  Union[str, Any]    ):
   super().__init__(lowercase__      ,lowercase__    )

   # add QFormer tokenizer
   __lowercase													=				qformer_tokenizer






  def __call__(   self     :  Tuple      ,lowercase__     :  ImageInput = None      ,lowercase__     :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,lowercase__     :  bool = True      ,lowercase__     :  Union[bool, str, PaddingStrategy] = False      ,lowercase__     :  Union[bool, str, TruncationStrategy] = None      ,lowercase__     :  Optional[int] = None      ,lowercase__     :  int = 0      ,lowercase__     :  Optional[int] = None      ,lowercase__     :  Optional[bool] = None      ,lowercase__     :  bool = False      ,lowercase__     :  bool = False      ,lowercase__     :  bool = False      ,lowercase__     :  bool = False      ,lowercase__     :  bool = False      ,lowercase__     :  bool = True      ,lowercase__     :  Optional[Union[str, TensorType]] = None      ,**lowercase__     :  Optional[int]      ,):
   if images is None and text is None:
    raise ValueError('''You have to specify at least images or text.'''    )

   __lowercase													=				BatchFeature()

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

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

   return encoding






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,*lowercase__     :  Any      ,**lowercase__     :  int    ):
   return self.tokenizer.batch_decode(*lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,*lowercase__     :  Optional[Any]      ,**lowercase__     :  List[Any]    ):
   return self.tokenizer.decode(*lowercase__      ,**lowercase__    )






  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.tokenizer.model_input_names
   __lowercase													=				self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,**lowercase__     :  List[str]    ):
   if os.path.isfile(lowercase__    ):
    raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"    )
   os.makedirs(lowercase__      ,exist_ok=lowercase__    )
   __lowercase													=				os.path.join(lowercase__      ,'''qformer_tokenizer'''    )
   self.qformer_tokenizer.save_pretrained(lowercase__    )
   return super().save_pretrained(lowercase__      ,**lowercase__    )






  @classmethod
  def       SCREAMING_SNAKE_CASE   (   cls     :  Union[str, Any]      ,lowercase__     :  Dict      ,**lowercase__     :  Any    ):
   __lowercase													=				AutoTokenizer.from_pretrained(lowercase__      ,subfolder='''qformer_tokenizer'''    )
   __lowercase													=				cls._get_arguments_from_pretrained(lowercase__      ,**lowercase__    )
   args.append(lowercase__    )
   return cls(*lowercase__    )




'''simple docstring'''






from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def 	_A  (					A__   ):






 """simple docstring"""



 return DownloadCommand(args.model  ,       args.cache_dir  ,       args.force  ,       args.trust_remote_code   )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @staticmethod
  def       SCREAMING_SNAKE_CASE   (   lowercase__     :  ArgumentParser    ):
   __lowercase													=				parser.add_parser('''download'''    )
   download_parser.add_argument(
       '''--cache-dir'''      ,type=lowercase__      ,default=lowercase__      ,help='''Path to location to store the models'''    )
   download_parser.add_argument(
       '''--force'''      ,action='''store_true'''      ,help='''Force the model to be download even if already in cache-dir'''    )
   download_parser.add_argument(
       '''--trust-remote-code'''      ,action='''store_true'''      ,help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''      ,)
   download_parser.add_argument('''model'''      ,type=lowercase__      ,help='''Name of the model to download'''    )
   download_parser.set_defaults(func=lowercase__    )






  def __init__(   self     :  str      ,lowercase__     :  str      ,lowercase__     :  str      ,lowercase__     :  bool      ,lowercase__     :  bool    ):
   __lowercase													=				model
   __lowercase													=				cache
   __lowercase													=				force
   __lowercase													=				trust_remote_code






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   from ..models.auto import AutoModel, AutoTokenizer

   AutoModel.from_pretrained(
       self._model      ,cache_dir=self._cache      ,force_download=self._force      ,trust_remote_code=self._trust_remote_code    )
   AutoTokenizer.from_pretrained(
       self._model      ,cache_dir=self._cache      ,force_download=self._force      ,trust_remote_code=self._trust_remote_code    )

