

"""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 						__UpperCamelCase     (     __snake_case    ):
  _UpperCAmelCase        =		["image_processor", "tokenizer"]
  _UpperCAmelCase        =		"BlipImageProcessor"
  _UpperCAmelCase        =		"AutoTokenizer"

  def __init__(		self     ,_A     ,_A     ,_A   ):
      '''simple docstring'''

      super().__init__(__lowerCamelCase     ,__lowerCamelCase   )

      # add QFormer tokenizer
      _lowerCAmelCase							: Dict	=     qformer_tokenizer

  def __call__(		self     ,_A = None     ,_A = None     ,_A = True     ,_A = False     ,_A = None     ,_A = None     ,_A = 0     ,_A = None     ,_A = None     ,_A = False     ,_A = False     ,_A = False     ,_A = False     ,_A = False     ,_A = True     ,_A = None     ,**_A     ,):
      '''simple docstring'''

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

      _lowerCAmelCase							: List[Any]	=     BatchFeature()

      if text is not None:
          _lowerCAmelCase							: str	=     self.tokenizer(
              text=__lowerCamelCase     ,add_special_tokens=__lowerCamelCase     ,padding=__lowerCamelCase     ,truncation=__lowerCamelCase     ,max_length=__lowerCamelCase     ,stride=__lowerCamelCase     ,pad_to_multiple_of=__lowerCamelCase     ,return_attention_mask=__lowerCamelCase     ,return_overflowing_tokens=__lowerCamelCase     ,return_special_tokens_mask=__lowerCamelCase     ,return_offsets_mapping=__lowerCamelCase     ,return_token_type_ids=__lowerCamelCase     ,return_length=__lowerCamelCase     ,verbose=__lowerCamelCase     ,return_tensors=__lowerCamelCase     ,**__lowerCamelCase     ,)
          encoding.update(__lowerCamelCase   )
          _lowerCAmelCase							: Optional[int]	=     self.qformer_tokenizer(
              text=__lowerCamelCase     ,add_special_tokens=__lowerCamelCase     ,padding=__lowerCamelCase     ,truncation=__lowerCamelCase     ,max_length=__lowerCamelCase     ,stride=__lowerCamelCase     ,pad_to_multiple_of=__lowerCamelCase     ,return_attention_mask=__lowerCamelCase     ,return_overflowing_tokens=__lowerCamelCase     ,return_special_tokens_mask=__lowerCamelCase     ,return_offsets_mapping=__lowerCamelCase     ,return_token_type_ids=__lowerCamelCase     ,return_length=__lowerCamelCase     ,verbose=__lowerCamelCase     ,return_tensors=__lowerCamelCase     ,**__lowerCamelCase     ,)
          _lowerCAmelCase							: Optional[Any]	=     qformer_text_encoding.pop('input_ids'   )
          _lowerCAmelCase							: Union[str, Any]	=     qformer_text_encoding.pop('attention_mask'   )

      if images is not None:
          _lowerCAmelCase							: List[str]	=     self.image_processor(__lowerCamelCase     ,return_tensors=__lowerCamelCase   )
          encoding.update(__lowerCamelCase   )

      return encoding

  def __lowerCamelCase		(		self     ,*_A     ,**_A   ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self     ,*_A     ,**_A   ):
      '''simple docstring'''

      return self.tokenizer.decode(*__lowerCamelCase     ,**__lowerCamelCase   )

  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: Tuple	=     self.tokenizer.model_input_names
      _lowerCAmelCase							: List[Any]	=     self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

  def __lowerCamelCase		(		self     ,_A     ,**_A   ):
      '''simple docstring'''

      if os.path.isfile(__lowerCamelCase   ):
          raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""   )
      os.makedirs(__lowerCamelCase     ,exist_ok=__lowerCamelCase   )
      _lowerCAmelCase							: str	=     os.path.join(__lowerCamelCase     ,'qformer_tokenizer'   )
      self.qformer_tokenizer.save_pretrained(__lowerCamelCase   )
      return super().save_pretrained(__lowerCamelCase     ,**__lowerCamelCase   )







  @classmethod
  def __lowerCamelCase		(		cls     ,_A     ,**_A   ):
      '''simple docstring'''

      _lowerCAmelCase							: Dict	=     AutoTokenizer.from_pretrained(__lowerCamelCase     ,subfolder='qformer_tokenizer'   )
      _lowerCAmelCase							: int	=     cls._get_arguments_from_pretrained(__lowerCamelCase     ,**__lowerCamelCase   )
      args.append(__lowerCamelCase   )
      return cls(*__lowerCamelCase   )








from collections.abc import Callable

import numpy as np





def 	__a     (      A__			:  Callable			,  A__			:  float			,  A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   int(np.ceil((x_end - xa) / step_size       )       )
    SCREAMING_SNAKE_CASE										=   np.zeros((n + 1,)       )
    SCREAMING_SNAKE_CASE										=   ya
    SCREAMING_SNAKE_CASE										=   xa

    for k in range(A__       ):
        SCREAMING_SNAKE_CASE										=   y[k] + step_size * ode_func(A__			,  y[k]       )
        SCREAMING_SNAKE_CASE										=   y[k] + (
            (step_size / 2) * (ode_func(A__			,  y[k]       ) + ode_func(x + step_size			,  A__       ))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()