
'''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  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		['image_processor', 'tokenizer']
    lowerCAmelCase_				    =		'OwlViTImageProcessor'
    lowerCAmelCase_				    =		('CLIPTokenizer', 'CLIPTokenizerFast')



    def __init__(	self      :       List[Any],__A      :       str=None,__A      :       Optional[int]=None,**__A      :       Dict     ):
          _lowerCamelCase				:   int								=   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,)
                _lowerCamelCase				:   Union[str, Any]								=   kwargs.pop("feature_extractor"     )

          _lowerCamelCase				:   Any								=   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      :       Any,__A      :       Tuple=None,__A      :       Optional[Any]=None,__A      :       Optional[int]=None,__A      :       Union[str, Any]="max_length",__A      :       Dict="np",**__A      :       int     ):

          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     )):
                      _lowerCamelCase				:   Tuple								=   [self.tokenizer(__A,padding=__A,return_tensors=__A,**__A     )]

                elif isinstance(__A,__A     ) and isinstance(text[0],__A     ):
                      _lowerCamelCase				:   Dict								=   []

                      # Maximum number of queries across batch
                      _lowerCamelCase				:   Optional[int]								=   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:
                                  _lowerCamelCase				:   Optional[Any]								=   t + [" "] * (max_num_queries - len(__A     ))

                            _lowerCamelCase				:   Union[str, Any]								=   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":
                      _lowerCamelCase				:   int								=   np.concatenate([encoding["input_ids"] for encoding in encodings],axis=0     )
                      _lowerCamelCase				:   List[str]								=   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				:   str								=   jnp.concatenate([encoding["input_ids"] for encoding in encodings],axis=0     )
                      _lowerCamelCase				:   List[str]								=   jnp.concatenate([encoding["attention_mask"] for encoding in encodings],axis=0     )

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

                      _lowerCamelCase				:   Tuple								=   torch.cat([encoding["input_ids"] for encoding in encodings],dim=0     )
                      _lowerCamelCase				:   List[str]								=   torch.cat([encoding["attention_mask"] for encoding in encodings],dim=0     )

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

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

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

                _lowerCamelCase				:   Tuple								=   BatchEncoding()
                _lowerCamelCase				:   List[Any]								=   input_ids
                _lowerCamelCase				:   Dict								=   attention_mask

          if query_images is not None:
                _lowerCamelCase				:   Tuple								=   BatchEncoding()
                _lowerCamelCase				:   Optional[Any]								=   self.image_processor(
                    __A,return_tensors=__A,**__A     ).pixel_values
                _lowerCamelCase				:   Tuple								=   query_pixel_values

          if images is not None:
                _lowerCamelCase				:   Tuple								=   self.image_processor(__A,return_tensors=__A,**__A     )

          if text is not None and images is not None:
                _lowerCamelCase				:   Dict								=   image_features.pixel_values
                return encoding
          elif query_images is not None and images is not None:
                _lowerCamelCase				:   str								=   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   lowerCamelCase_  (	self      :       List[str],*__A      :       int,**__A      :       Tuple     ):
          return self.image_processor.post_process(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any],*__A      :       List[str],**__A      :       List[str]     ):
          return self.image_processor.post_process_object_detection(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],*__A      :       int,**__A      :       Union[str, Any]     ):
          return self.image_processor.post_process_image_guided_detection(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       Tuple,*__A      :       List[Any],**__A      :       Union[str, Any]     ):
          return self.tokenizer.batch_decode(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       Tuple,*__A      :       Tuple,**__A      :       Dict     ):
          return self.tokenizer.decode(*__A,**__A     )



    @property
    def   lowerCamelCase_  (	self      :       Dict     ):
          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   lowerCamelCase_  (	self      :       Any     ):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",__A,)
          return self.image_processor

'''simple docstring'''






from __future__ import annotations







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if (direction == 1 and array[indexa] > array[indexa]) or (
          direction == 0 and array[indexa] < array[indexa]
      ):
            _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   array[indexa], array[indexa]







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[int]								=   int(length / 2							)
            for i in range(_lowerCAmelCase				,	low + middle							):
                  comp_and_swap(_lowerCAmelCase				,	_lowerCAmelCase				,	i + middle				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[Any]								=   int(length / 2							)
            bitonic_sort(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	1							)
            bitonic_sort(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	0							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   Any   =       input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase_       :   Optional[int]   =       [int(item.strip()) for item in user_input.split(',')]

 bitonic_sort(unsorted, 0, len(unsorted), 1)
 print('\nSorted array in ascending order is: ', end='')
 print(*unsorted, sep=', ')

 bitonic_merge(unsorted, 0, len(unsorted), 0)
 print('Sorted array in descending order is: ', end='')
 print(*unsorted, sep=', ')