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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     :   Tuple    		=       ['image_processor', 'tokenizer']
      _snake_case     :   Union[str, Any]    		=       'BlipImageProcessor'
      _snake_case     :   Optional[Any]    		=       'AutoTokenizer'



      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Tuple)				->str:


          """simple docstring"""


          super().__init__(_UpperCamelCase    ,  _UpperCamelCase)

          # add QFormer tokenizer
          _lowerCamelCase     :		Dict 						=						qformer_tokenizer



      def __call__(					self     :				Union[str, 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     :				Dict    ,  )				->BatchFeature:


          """simple docstring"""


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

          _lowerCamelCase     :		Tuple 						=						BatchFeature()

          if text is not None:
              _lowerCamelCase     :		Union[str, Any] 						=						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)
              _lowerCamelCase     :		List[str] 						=						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    ,  )
              _lowerCamelCase     :		str 						=						qformer_text_encoding.pop("""input_ids""")
              _lowerCamelCase     :		Any 						=						qformer_text_encoding.pop("""attention_mask""")

          if images is not None:
              _lowerCamelCase     :		str 						=						self.image_processor(_UpperCamelCase    ,  return_tensors=_UpperCamelCase)
              encoding.update(_UpperCamelCase)

          return encoding



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


          """simple docstring"""


          return self.tokenizer.batch_decode(*_UpperCamelCase    ,  **_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.tokenizer.model_input_names
          _lowerCamelCase     :		Union[str, Any] 						=						self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  **_UpperCamelCase     :				Union[str, Any])				->str:


          """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)
          _lowerCamelCase     :		str 						=						os.path.join(_UpperCamelCase    ,  """qformer_tokenizer""")
          self.qformer_tokenizer.save_pretrained(_UpperCamelCase)
          return super().save_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)




      @classmethod
      def 	_SCREAMING_SNAKE_CASE    (					cls     :				int    ,  _UpperCamelCase     :				str    ,  **_UpperCamelCase     :				int)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						AutoTokenizer.from_pretrained(_UpperCamelCase    ,  subfolder="""qformer_tokenizer""")
          _lowerCamelCase     :		str 						=						cls._get_arguments_from_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)
          args.append(_UpperCamelCase)
          return cls(*_UpperCamelCase)





def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    if n == 0:
        return 0
    _lowerCamelCase     :		Tuple 						=						float("""-inf"""	)
    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max(
            __A   , prices[i - 1] + naive_cut_rod_recursive(n - i   , __A	)	)

    return max_revue







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)
    _lowerCamelCase     :		Optional[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    return _top_down_cut_rod_recursive(__A   , __A   , __A	)







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        _lowerCamelCase     :		int 						=						float("""-inf"""	)
        for i in range(1   , n + 1	):
            _lowerCamelCase     :		Optional[Any] 						=						max(
                __A   , prices[i - 1] + _top_down_cut_rod_recursive(n - i   , __A   , __A	)   , )

        _lowerCamelCase     :		Optional[Any] 						=						max_revenue

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _enforce_args(__A   , __A	)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    _lowerCamelCase     :		List[Any] 						=						[float("""-inf"""	) for _ in range(n + 1	)]
    _lowerCamelCase     :		Any 						=						0

    for i in range(1   , n + 1	):
        _lowerCamelCase     :		Any 						=						max_rev[i]
        for j in range(1   , i + 1	):
            _lowerCamelCase     :		List[Any] 						=						max(__A   , prices[j - 1] + max_rev[i - j]	)

        _lowerCamelCase     :		int 						=						max_revenue_i

    return max_rev[n]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if n < 0:
        _lowerCamelCase     :		Any 						=						F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(__A	)

    if n > len(__A	):
        _lowerCamelCase     :		List[Any] 						=						(
            """Each integral piece of rod must have a corresponding price. """
            F"""Got n = {n} but length of prices = {len(__A	)}"""
        )
        raise ValueError(__A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						[6, 10, 12, 15, 20, 23]
    _lowerCamelCase     :		List[str] 						=						len(__A	)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    _lowerCamelCase     :		Tuple 						=						36

    _lowerCamelCase     :		Any 						=						top_down_cut_rod(__A   , __A	)
    _lowerCamelCase     :		Dict 						=						bottom_up_cut_rod(__A   , __A	)
    _lowerCamelCase     :		List[str] 						=						naive_cut_rod_recursive(__A   , __A	)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()




