





'''simple docstring'''







from __future__ import annotations

from decimal import Decimal

from numpy import array






def  _lowerCamelCase				( lowercase						:      list[list[float]]						)	->	list[list[float]]:
				_a								= 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
								_a								= 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
								_a								= [[0.0, 0.0], [0.0, 0.0]]
								_a	,					_a								= matrix[1][1], matrix[0][0]
								_a	,					_a								= -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
								_a								= 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
								_a								= [
								    [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						)],
								]
								_a								= (d(matrix[1][1]						) * d(matrix[2][2]						)) - (
								    d(matrix[1][2]						) * d(matrix[2][1]						)
								)
								_a								= -(
								    (d(matrix[1][0]						) * d(matrix[2][2]						)) - (d(matrix[1][2]						) * d(matrix[2][0]						))
								)
								_a								= (d(matrix[1][0]						) * d(matrix[2][1]						)) - (
								    d(matrix[1][1]						) * d(matrix[2][0]						)
								)
								_a								= -(
								    (d(matrix[0][1]						) * d(matrix[2][2]						)) - (d(matrix[0][2]						) * d(matrix[2][1]						))
								)
								_a								= (d(matrix[0][0]						) * d(matrix[2][2]						)) - (
								    d(matrix[0][2]						) * d(matrix[2][0]						)
								)
								_a								= -(
								    (d(matrix[0][0]						) * d(matrix[2][1]						)) - (d(matrix[0][1]						) * d(matrix[2][0]						))
								)
								_a								= (d(matrix[0][1]						) * d(matrix[1][2]						)) - (
								    d(matrix[0][2]						) * d(matrix[1][1]						)
								)
								_a								= -(
								    (d(matrix[0][0]						) * d(matrix[1][2]						)) - (d(matrix[0][2]						) * d(matrix[1][0]						))
								)
								_a								= (d(matrix[0][0]						) * d(matrix[1][1]						)) - (
								    d(matrix[0][1]						) * d(matrix[1][0]						)
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								_a								= array(lowercase						)
								for i in range(3						):
												for j in range(3						):
																_a								= cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								_a								= 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."						)







'''simple docstring'''







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='OwlViTImageProcessor'
				__a            =('CLIPTokenizer', 'CLIPTokenizerFast')




				def __init__(  self     :					List[Any]					,				__a     :					str=None					,				__a     :					List[str]=None					,				**__a     :					List[Any]					):
								_a								= None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."					,				__a					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."					)
								if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."					)

								super().__init__(__a					,				__a					)




				def __call__(  self     :					Union[str, Any]					,				__a     :					Any=None					,				__a     :					List[str]=None					,				__a     :					int=None					,				__a     :					Optional[int]="max_length"					,				__a     :					List[str]="np"					,				**__a     :					Any					):

								if text is None and query_images is None and images is None:
												raise ValueError(
												    "You have to specify at least one text or query image or image. All three cannot be none."					)

								if text is not None:
												if isinstance(__a					,				__a					) or (isinstance(__a					,				__a					) and not isinstance(text[0]					,				__a					)):
																_a								= [self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)]

												elif isinstance(__a					,				__a					) and isinstance(text[0]					,				__a					):
																_a								= []

																# Maximum number of queries across batch
																_a								= max([len(__a					) for t in text]					)

																# Pad all batch samples to max number of text queries
																for t in text:
																				if len(__a					) != max_num_queries:
																								_a								= t + [" "] * (max_num_queries - len(__a					))

																				_a								= self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)
																				encodings.append(__a					)
												else:
																raise TypeError("Input text should be a string, a list of strings or a nested list of strings"					)

												if return_tensors == "np":
																_a								= np.concatenate([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= np.concatenate([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												elif return_tensors == "jax" and is_flax_available():
																import jax.numpy as jnp

																_a								= jnp.concatenate([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= jnp.concatenate([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												elif return_tensors == "pt" and is_torch_available():
																import torch

																_a								= torch.cat([encoding["input_ids"] for encoding in encodings]					,				dim=0					)
																_a								= torch.cat([encoding["attention_mask"] for encoding in encodings]					,				dim=0					)

												elif return_tensors == "tf" and is_tf_available():
																import tensorflow as tf

																_a								= tf.stack([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= tf.stack([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												else:
																raise ValueError("Target return tensor type could not be returned"					)

												_a								= BatchEncoding()
												_a								= input_ids
												_a								= attention_mask

								if query_images is not None:
												_a								= BatchEncoding()
												_a								= self.image_processor(
												    __a					,				return_tensors=__a					,				**__a					).pixel_values
												_a								= query_pixel_values

								if images is not None:
												_a								= self.image_processor(__a					,				return_tensors=__a					,				**__a					)

								if text is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif query_images is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif text is not None or query_images is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__a					)					,				tensor_type=__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					Union[str, Any]					,				**__a     :					int					):
								return self.image_processor.post_process(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				*__a     :					Optional[Any]					,				**__a     :					List[str]					):
								return self.image_processor.post_process_object_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*__a     :					Dict					,				**__a     :					Union[str, Any]					):
								return self.image_processor.post_process_image_guided_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					Tuple					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					List[Any]					,				**__a     :					Optional[int]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,				__a					,				)
								return self.image_processor_class




				@property
				def   UpperCamelCase__   (  self     :					str					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor
