






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     (  _lowerCAmelCase ):
	__snake_case						:List[str] =							['image_processor', 'tokenizer']
	__snake_case						:Dict =							'BlipImageProcessor'
	__snake_case						:Any =							'AutoTokenizer'







	def __init__(	self   :				Optional[int]							,					_lowerCAmelCase   :				Optional[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Optional[Any]      )		->			Tuple:
		"""simple docstring"""







		super().__init__(_lowerCAmelCase							,					_lowerCAmelCase      )

		# add QFormer tokenizer
		__lowercase   							=	qformer_tokenizer







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







		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=_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      )
			__lowercase   							=	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							,					)
			__lowercase   							=	qformer_text_encoding.pop("""input_ids"""      )
			__lowercase   							=	qformer_text_encoding.pop("""attention_mask"""      )

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

		return encoding







	def 			_a (	self   :				Optional[int]							,					*_lowerCAmelCase   :				Optional[int]							,					**_lowerCAmelCase   :				int      )		->			Union[str, Any]:
		"""simple docstring"""







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







	def 			_a (	self   :				List[Any]							,					*_lowerCAmelCase   :				List[str]							,					**_lowerCAmelCase   :				Union[str, Any]      )		->			Any:
		"""simple docstring"""







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







	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def 			_a (	self   :				Tuple      )		->			Union[str, Any]:
		"""simple docstring"""







		__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 			_a (	self   :				Any							,					_lowerCAmelCase   :				str							,					**_lowerCAmelCase   :				List[str]      )		->			List[str]:
		"""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      )
		__lowercase   							=	os.path.join(_lowerCAmelCase							,					"""qformer_tokenizer"""      )
		self.qformer_tokenizer.save_pretrained(_lowerCAmelCase      )
		return super().save_pretrained(_lowerCAmelCase							,					**_lowerCAmelCase      )







	@classmethod
	def 			_a (	cls   :				Any							,					_lowerCAmelCase   :				Optional[Any]							,					**_lowerCAmelCase   :				Tuple      )		->			int:
		"""simple docstring"""







		__lowercase   							=	AutoTokenizer.from_pretrained(_lowerCAmelCase							,					subfolder="""qformer_tokenizer"""      )
		__lowercase   							=	cls._get_arguments_from_pretrained(_lowerCAmelCase							,					**_lowerCAmelCase      )
		args.append(_lowerCAmelCase      )
		return cls(*_lowerCAmelCase      )





'''simple docstring'''
from __future__ import annotations

from decimal import Decimal

from numpy import array


def      a (     __a  )    ->      list[list[float]]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(__a  ) == 2 and len(matrix[0]  ) == 2 and len(matrix[1]  ) == 2:
										# Calculate the determinant of the matrix
										UpperCamelCase__  :str				   =					float(
										    d(matrix[0][0]  ) * d(matrix[1][1]  ) - d(matrix[1][0]  ) * d(matrix[0][1]  )  )
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''  )

										# Creates a copy of the matrix with swapped positions of the elements
										UpperCamelCase__  :List[Any]				   =					[[0.0, 0.0], [0.0, 0.0]]
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					matrix[1][1], matrix[0][0]
										UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(__a  )  ) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(__a  ) == 3
					    and len(matrix[0]  ) == 3
					    and len(matrix[1]  ) == 3
					    and len(matrix[2]  ) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										UpperCamelCase__  :int				   =					float(
										    (
										        (d(matrix[0][0]  ) * d(matrix[1][1]  ) * d(matrix[2][2]  ))
										        + (d(matrix[0][1]  ) * d(matrix[1][2]  ) * d(matrix[2][0]  ))
										        + (d(matrix[0][2]  ) * d(matrix[1][0]  ) * d(matrix[2][1]  ))
										    )
										    - (
										        (d(matrix[0][2]  ) * d(matrix[1][1]  ) * d(matrix[2][0]  ))
										        + (d(matrix[0][1]  ) * d(matrix[1][0]  ) * d(matrix[2][2]  ))
										        + (d(matrix[0][0]  ) * d(matrix[1][2]  ) * d(matrix[2][1]  ))
										    )  )
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''  )

										# Creating cofactor matrix
										UpperCamelCase__  :List[Any]				   =					[
										    [d(0.0  ), d(0.0  ), d(0.0  )],
										    [d(0.0  ), d(0.0  ), d(0.0  )],
										    [d(0.0  ), d(0.0  ), d(0.0  )],
										]
										UpperCamelCase__  :Tuple				   =					(d(matrix[1][1]  ) * d(matrix[2][2]  )) - (
										    d(matrix[1][2]  ) * d(matrix[2][1]  )
										)
										UpperCamelCase__  :List[str]				   =					-(
										    (d(matrix[1][0]  ) * d(matrix[2][2]  )) - (d(matrix[1][2]  ) * d(matrix[2][0]  ))
										)
										UpperCamelCase__  :Union[str, Any]				   =					(d(matrix[1][0]  ) * d(matrix[2][1]  )) - (
										    d(matrix[1][1]  ) * d(matrix[2][0]  )
										)
										UpperCamelCase__  :List[Any]				   =					-(
										    (d(matrix[0][1]  ) * d(matrix[2][2]  )) - (d(matrix[0][2]  ) * d(matrix[2][1]  ))
										)
										UpperCamelCase__  :Optional[int]				   =					(d(matrix[0][0]  ) * d(matrix[2][2]  )) - (
										    d(matrix[0][2]  ) * d(matrix[2][0]  )
										)
										UpperCamelCase__  :List[Any]				   =					-(
										    (d(matrix[0][0]  ) * d(matrix[2][1]  )) - (d(matrix[0][1]  ) * d(matrix[2][0]  ))
										)
										UpperCamelCase__  :List[Any]				   =					(d(matrix[0][1]  ) * d(matrix[1][2]  )) - (
										    d(matrix[0][2]  ) * d(matrix[1][1]  )
										)
										UpperCamelCase__  :List[str]				   =					-(
										    (d(matrix[0][0]  ) * d(matrix[1][2]  )) - (d(matrix[0][2]  ) * d(matrix[1][0]  ))
										)
										UpperCamelCase__  :Dict				   =					(d(matrix[0][0]  ) * d(matrix[1][1]  )) - (
										    d(matrix[0][1]  ) * d(matrix[1][0]  )
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										UpperCamelCase__  :Optional[Any]				   =					array(__a  )
										for i in range(3  ):
															for j in range(3  ):
																				UpperCamelCase__  :Optional[Any]				   =					cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										UpperCamelCase__  :Any				   =					array(__a  )
										for i in range(3  ):
															for j in range(3  ):
																				inverse_matrix[i][j] /= d(__a  )

        # Calculate the inverse of the matrix
										return [[float(d(__a  )  ) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''  )