
'''simple docstring'''


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__lowercase							:   Union[str, Any]					=     logging.get_logger(__name__)

@add_end_docstrings(_lowercase    )
class 	__lowercase				(  _lowercase    ):

			def __init__(self			,   *A			,   **A      ):
								super().__init__(*A			,   **A      )
								self.check_model_type(A      )

			def        UpperCAmelCase__ (self			,   A=None			,   A=None			,   A=None			,   **A      ):
								lowerCamelCase_,      lowerCamelCase_					:      Dict     =		{}, {}
								if padding is not None:
													lowerCamelCase_					:      Tuple     =		padding
								if truncation is not None:
													lowerCamelCase_					:      Optional[Any]     =		truncation
								if top_k is not None:
													lowerCamelCase_					:      List[str]     =		top_k
								return preprocess_params, {}, postprocess_params

			def __call__(self			,   A			,   A = None			,   **A      ):
								if isinstance(A			,   (Image.Image, str)      ) and isinstance(A			,   A      ):
													lowerCamelCase_					:      Union[str, Any]     =		{'''image''': image, '''question''': question}
								else:
													lowerCamelCase_					:      List[str]     =		image
								lowerCamelCase_					:      Any     =		super().__call__(A			,   **A      )
								return results

			def        UpperCAmelCase__ (self			,   A			,   A=False			,   A=False      ):
								lowerCamelCase_					:      Any     =		load_image(inputs['''image''']      )
								lowerCamelCase_					:      str     =		self.tokenizer(
								    inputs['''question''']			,   return_tensors=self.framework			,   padding=A			,   truncation=A      )
								lowerCamelCase_					:      int     =		self.image_processor(images=A			,   return_tensors=self.framework      )
								model_inputs.update(A      )
								return model_inputs

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[int]     =		self.model(**A      )
								return model_outputs






			def        UpperCAmelCase__ (self			,   A			,   A=5      ):
								if top_k > self.model.config.num_labels:
													lowerCamelCase_					:      Any     =		self.model.config.num_labels

								if self.framework == "pt":
													lowerCamelCase_					:      List[str]     =		model_outputs.logits.sigmoid()[0]
													lowerCamelCase_,      lowerCamelCase_					:      Dict     =		probs.topk(A      )
								else:
													raise ValueError(F"""Unsupported framework: {self.framework}"""      )

								lowerCamelCase_					:      List[str]     =		scores.tolist()
								lowerCamelCase_					:      Tuple     =		ids.tolist()
								return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(A			,   A      )]




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(_lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
										# Calculate the determinant of the matrix
										lowerCamelCase_					:      Union[str, Any]     =		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
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(_lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(_lowercase				) == 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
										lowerCamelCase_					:      Tuple     =		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
										lowerCamelCase_					:      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				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowerCamelCase_					:      str     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				lowerCamelCase_					:      int     =		cofactor_matrix[j][i]

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

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


