




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        __snake_case  (_a		):
				lowerCAmelCase__				=     ["image_processor", "tokenizer"]
				lowerCAmelCase__				=     "OwlViTImageProcessor"
				lowerCAmelCase__				=     ("CLIPTokenizer", "CLIPTokenizerFast")



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Any=None		,	_UpperCAmelCase      :		str=None		,	**_UpperCAmelCase      :		Any      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
												    """ instead."""		,	_UpperCAmelCase		,	)
												_lowerCAmelCase    :    int										=      kwargs.pop("""feature_extractor"""      )

								_lowerCAmelCase    :    Optional[int]										=      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__(_UpperCAmelCase		,	_UpperCAmelCase      )



				def __call__(      self      :		str		,	_UpperCAmelCase      :		Dict=None		,	_UpperCAmelCase      :		List[Any]=None		,	_UpperCAmelCase      :		Tuple=None		,	_UpperCAmelCase      :		List[str]="max_length"		,	_UpperCAmelCase      :		Dict="np"		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								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(_UpperCAmelCase		,	_UpperCAmelCase      ) or (isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and not isinstance(text[0]		,	_UpperCAmelCase      )):
																_lowerCAmelCase    :    Optional[Any]										=      [self.tokenizer(_UpperCAmelCase		,	padding=_UpperCAmelCase		,	return_tensors=_UpperCAmelCase		,	**_UpperCAmelCase      )]

												elif isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) and isinstance(text[0]		,	_UpperCAmelCase      ):
																_lowerCAmelCase    :    Any										=      []

																# Maximum number of queries across batch
																_lowerCAmelCase    :    Any										=      max([len(_UpperCAmelCase      ) for t in text]      )

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

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

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

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

																_lowerCAmelCase    :    List[Any]										=      jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]		,	axis=0      )
																_lowerCAmelCase    :    Optional[int]										=      jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,	axis=0      )

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

																_lowerCAmelCase    :    Optional[Any]										=      torch.cat([encoding["""input_ids"""] for encoding in encodings]		,	dim=0      )
																_lowerCAmelCase    :    Any										=      torch.cat([encoding["""attention_mask"""] for encoding in encodings]		,	dim=0      )

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

																_lowerCAmelCase    :    Optional[Any]										=      tf.stack([encoding["""input_ids"""] for encoding in encodings]		,	axis=0      )
																_lowerCAmelCase    :    Union[str, Any]										=      tf.stack([encoding["""attention_mask"""] for encoding in encodings]		,	axis=0      )

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

												_lowerCAmelCase    :    Optional[Any]										=      BatchEncoding()
												_lowerCAmelCase    :    Optional[int]										=      input_ids
												_lowerCAmelCase    :    Optional[Any]										=      attention_mask

								if query_images is not None:
												_lowerCAmelCase    :    Tuple										=      BatchEncoding()
												_lowerCAmelCase    :    Optional[int]										=      self.image_processor(
												    _UpperCAmelCase		,	return_tensors=_UpperCAmelCase		,	**_UpperCAmelCase      ).pixel_values
												_lowerCAmelCase    :    List[str]										=      query_pixel_values

								if images is not None:
												_lowerCAmelCase    :    Optional[Any]										=      self.image_processor(_UpperCAmelCase		,	return_tensors=_UpperCAmelCase		,	**_UpperCAmelCase      )

								if text is not None and images is not None:
												_lowerCAmelCase    :    Optional[Any]										=      image_features.pixel_values
												return encoding
								elif query_images is not None and images is not None:
												_lowerCAmelCase    :    Optional[int]										=      image_features.pixel_values
												return encoding
								elif text is not None or query_images is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_UpperCAmelCase      )		,	tensor_type=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	*_UpperCAmelCase      :		int		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								return self.image_processor.post_process(*_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	*_UpperCAmelCase      :		Tuple		,	**_UpperCAmelCase      :		int      )  ->    List[Any]:





								'''simple docstring'''




								return self.image_processor.post_process_object_detection(*_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	*_UpperCAmelCase      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	*_UpperCAmelCase      :		Union[str, Any]		,	**_UpperCAmelCase      :		int      )  ->    Tuple:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	*_UpperCAmelCase      :		List[Any]		,	**_UpperCAmelCase      :		str      )  ->    Dict:





								'''simple docstring'''




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



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Optional[int]:





								'''simple docstring'''




								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,	_UpperCAmelCase		,	)
								return self.image_processor_class



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Tuple:





								'''simple docstring'''




								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,	_UpperCAmelCase		,	)
								return self.image_processor







from __future__ import annotations

from decimal import Decimal

from numpy import array




def 							_UpperCAmelCase  (UpperCamelCase_ : list[list[float]]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      Decimal

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

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

								# Transpose the cofactor matrix (Adjoint matrix)
								_lowerCAmelCase    :    List[str]										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																_lowerCAmelCase    :    List[Any]										=      cofactor_matrix[j][i]

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

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

