

'''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     lowercase_			(__lowerCAmelCase   ):
  SCREAMING_SNAKE_CASE					:						int           =    ['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE					:						Tuple           =    'OwlViTImageProcessor'
  SCREAMING_SNAKE_CASE					:						List[str]           =    ('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(   self     :  str      ,lowercase__     :  Dict=None      ,lowercase__     :  Any=None      ,**lowercase__     :  List[Any]    ):
   __lowercase													=				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      ,)
    __lowercase													=				kwargs.pop('''feature_extractor'''    )

   __lowercase													=				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     :  int      ,lowercase__     :  Union[str, Any]=None      ,lowercase__     :  Optional[int]=None      ,lowercase__     :  Dict=None      ,lowercase__     :  Dict="max_length"      ,lowercase__     :  List[str]="np"      ,**lowercase__     :  Optional[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(_UpperCamelCase      ,_UpperCamelCase    ) or (isinstance(_UpperCamelCase      ,_UpperCamelCase    ) and not isinstance(text[0]      ,_UpperCamelCase    )):
     __lowercase													=				[self.tokenizer(_UpperCamelCase      ,padding=_UpperCamelCase      ,return_tensors=_UpperCamelCase      ,**_UpperCamelCase    )]

    elif isinstance(_UpperCamelCase      ,_UpperCamelCase    ) and isinstance(text[0]      ,_UpperCamelCase    ):
     __lowercase													=				[]

     # Maximum number of queries across batch
     __lowercase													=				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:
       __lowercase													=				t + [""" """] * (max_num_queries - len(_UpperCamelCase    ))

      __lowercase													=				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":
     __lowercase													=				np.concatenate([encoding['''input_ids'''] for encoding in encodings]      ,axis=0    )
     __lowercase													=				np.concatenate([encoding['''attention_mask'''] for encoding in encodings]      ,axis=0    )

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

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

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

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

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

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

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

    __lowercase													=				BatchEncoding()
    __lowercase													=				input_ids
    __lowercase													=				attention_mask

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

   if images is not None:
    __lowercase													=				self.image_processor(_UpperCamelCase      ,return_tensors=_UpperCamelCase      ,**_UpperCamelCase    )

   if text is not None and images is not None:
    __lowercase													=				image_features.pixel_values
    return encoding
   elif query_images is not None and images is not None:
    __lowercase													=				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     :  Union[str, Any]      ,*lowercase__     :  int      ,**lowercase__     :  int    ):
   return self.image_processor.post_process(*_UpperCamelCase      ,**_UpperCamelCase    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,*lowercase__     :  Optional[int]      ,**lowercase__     :  List[Any]    ):
   return self.image_processor.post_process_object_detection(*_UpperCamelCase      ,**_UpperCamelCase    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,*lowercase__     :  Dict      ,**lowercase__     :  str    ):
   return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase      ,**_UpperCamelCase    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,*lowercase__     :  Any      ,**lowercase__     :  str    ):
   return self.tokenizer.batch_decode(*_UpperCamelCase      ,**_UpperCamelCase    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,*lowercase__     :  Tuple      ,**lowercase__     :  List[Any]    ):
   return self.tokenizer.decode(*_UpperCamelCase      ,**_UpperCamelCase    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   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    ):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,_UpperCamelCase      ,)
   return self.image_processor




'''simple docstring'''






from __future__ import annotations

from typing import Any






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Any      ,lowercase__     :  int      ,lowercase__     :  int      ,lowercase__     :  float = 0    ):
   __lowercase						,			__lowercase													=				row, column
   __lowercase													=				[[default_value for c in range(lowercase__    )] for r in range(lowercase__    )]






  def __str__(   self     :  List[str]    ):
   __lowercase													=				F"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   __lowercase													=				0
   for row_vector in self.array:
    for obj in row_vector:
     __lowercase													=				max(lowercase__      ,len(str(lowercase__    )    )    )
   __lowercase													=				F"%{max_element_length}s"

   # Make string and return
   def single_line(lowercase__     :  list[float]    ) -> str:
    nonlocal string_format_identifier
    __lowercase													=				'''['''
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(lowercase__    ) for row_vector in self.array    )
   return s






  def __repr__(   self     :  List[str]    ):
   return str(self    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  tuple[int, int]    ):
   if not (isinstance(lowercase__      ,(list, tuple)    ) and len(lowercase__    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True






  def __getitem__(   self     :  Tuple      ,lowercase__     :  tuple[int, int]    ):
   assert self.validate_indicies(lowercase__    )
   return self.array[loc[0]][loc[1]]






  def __setitem__(   self     :  Tuple      ,lowercase__     :  tuple[int, int]      ,lowercase__     :  float    ):
   assert self.validate_indicies(lowercase__    )
   __lowercase													=				value






  def __add__(   self     :  List[Any]      ,lowercase__     :  Matrix    ):
   assert isinstance(lowercase__      ,lowercase__    )
   assert self.row == another.row and self.column == another.column

   # Add
   __lowercase													=				Matrix(self.row      ,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				self[r, c] + another[r, c]
   return result






  def __neg__(   self     :  List[str]    ):
   __lowercase													=				Matrix(self.row      ,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				-self[r, c]
   return result






  def __sub__(   self     :  str      ,lowercase__     :  Matrix    ):
   return self + (-another)






  def __mul__(   self     :  Dict      ,lowercase__     :  int | float | Matrix    ):

   if isinstance(lowercase__      ,(int, float)    ):  # Scalar multiplication
    __lowercase													=				Matrix(self.row      ,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      __lowercase													=				self[r, c] * another
    return result
   elif isinstance(lowercase__      ,lowercase__    ):  # Matrix multiplication
    assert self.column == another.row
    __lowercase													=				Matrix(self.row      ,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    __lowercase													=				F"Unsupported type given for another ({type(lowercase__    )})"
    raise TypeError(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				Matrix(self.column      ,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				self[r, c]
   return result






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Matrix      ,lowercase__     :  Matrix    ):
   assert isinstance(lowercase__      ,lowercase__    ) and isinstance(lowercase__      ,lowercase__    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   __lowercase													=				v.transpose()
   __lowercase													=				(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


 def 	_A  (					):






  """simple docstring"""



  __lowercase													=				Matrix(3  ,       3  ,       0   )
  for i in range(3   ):
   __lowercase													=				1
  print(F"a^(-1) is {ainv}"   )
  # u, v
  __lowercase													=				Matrix(3  ,       1  ,       0   )
  __lowercase						,			__lowercase						,			__lowercase													=				1, 2, -3
  __lowercase													=				Matrix(3  ,       1  ,       0   )
  __lowercase						,			__lowercase						,			__lowercase													=				4, -2, 5
  print(F"u is {u}"   )
  print(F"v is {v}"   )
  print(F"uv^T is {u * v.transpose()}"   )
  # Sherman Morrison
  print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(A__  ,       A__   )}"   )


 def 	_A  (					):






  """simple docstring"""



  import doctest

  doctest.testmod()






 testa()

