
'''simple docstring'''






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
 from ...processing_utils import ProcessorMixin
 from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   int   =       {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'owlvit_text_model'



    def __init__(	self      :       List[Any],__A      :       int=4_9_4_0_8,__A      :       int=5_1_2,__A      :       int=2_0_4_8,__A      :       str=1_2,__A      :       List[str]=8,__A      :       Optional[Any]=1_6,__A      :       Dict="quick_gelu",__A      :       Tuple=1e-5,__A      :       Optional[Any]=0.0,__A      :       Any=0.02,__A      :       Any=1.0,__A      :       str=0,__A      :       Dict=4_9_4_0_6,__A      :       str=4_9_4_0_7,**__A      :       Dict,):
          super().__init__(pad_token_id=__A,bos_token_id=__A,eos_token_id=__A,**__A     )

          _lowerCamelCase				:   Any								=   vocab_size
          _lowerCamelCase				:   List[Any]								=   hidden_size
          _lowerCamelCase				:   Dict								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   num_hidden_layers
          _lowerCamelCase				:   Union[str, Any]								=   num_attention_heads
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Any								=   hidden_act
          _lowerCamelCase				:   int								=   layer_norm_eps
          _lowerCamelCase				:   Optional[Any]								=   attention_dropout
          _lowerCamelCase				:   Optional[int]								=   initializer_range
          _lowerCamelCase				:   Any								=   initializer_factor



    @classmethod
    def   lowerCamelCase_  (	cls      :       int,__A      :       Union[str, os.PathLike],**__A      :       Tuple     ):
          cls._set_token_in_kwargs(__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   cls.get_config_dict(__A,**__A     )

          # get the text config dict if we are loading from OwlViTConfig
          if config_dict.get("model_type"     ) == "owlvit":
                _lowerCamelCase				:   int								=   config_dict["text_config"]

          if "model_type" in config_dict and hasattr(cls,"model_type"     ) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'     )

          return cls.from_dict(__A,**__A     )


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'owlvit_vision_model'



    def __init__(	self      :       Dict,__A      :       Optional[Any]=7_6_8,__A      :       Any=3_0_7_2,__A      :       int=1_2,__A      :       Optional[int]=1_2,__A      :       Optional[int]=3,__A      :       List[Any]=7_6_8,__A      :       List[str]=3_2,__A      :       int="quick_gelu",__A      :       Dict=1e-5,__A      :       Optional[Any]=0.0,__A      :       str=0.02,__A      :       Union[str, Any]=1.0,**__A      :       int,):
          super().__init__(**__A     )

          _lowerCamelCase				:   Tuple								=   hidden_size
          _lowerCamelCase				:   Optional[int]								=   intermediate_size
          _lowerCamelCase				:   Optional[int]								=   num_hidden_layers
          _lowerCamelCase				:   Union[str, Any]								=   num_attention_heads
          _lowerCamelCase				:   Any								=   num_channels
          _lowerCamelCase				:   Union[str, Any]								=   image_size
          _lowerCamelCase				:   Optional[Any]								=   patch_size
          _lowerCamelCase				:   List[Any]								=   hidden_act
          _lowerCamelCase				:   Dict								=   layer_norm_eps
          _lowerCamelCase				:   Any								=   attention_dropout
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   Optional[Any]								=   initializer_factor



    @classmethod
    def   lowerCamelCase_  (	cls      :       Dict,__A      :       Union[str, os.PathLike],**__A      :       Tuple     ):
          cls._set_token_in_kwargs(__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   int								=   cls.get_config_dict(__A,**__A     )

          # get the vision config dict if we are loading from OwlViTConfig
          if config_dict.get("model_type"     ) == "owlvit":
                _lowerCamelCase				:   Dict								=   config_dict["vision_config"]

          if "model_type" in config_dict and hasattr(cls,"model_type"     ) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'     )

          return cls.from_dict(__A,**__A     )


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'owlvit'
    lowerCAmelCase_				    =		True



    def __init__(	self      :       Tuple,__A      :       str=None,__A      :       Tuple=None,__A      :       str=5_1_2,__A      :       Union[str, Any]=2.6592,__A      :       List[Any]=True,**__A      :       str,):
          super().__init__(**__A     )

          if text_config is None:
                _lowerCamelCase				:   Any								=   {}
                logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."     )

          if vision_config is None:
                _lowerCamelCase				:   List[Any]								=   {}
                logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."     )

          _lowerCamelCase				:   Any								=   OwlViTTextConfig(**__A     )
          _lowerCamelCase				:   str								=   OwlViTVisionConfig(**__A     )

          _lowerCamelCase				:   Tuple								=   projection_dim
          _lowerCamelCase				:   Union[str, Any]								=   logit_scale_init_value
          _lowerCamelCase				:   int								=   return_dict
          _lowerCamelCase				:   Optional[int]								=   1.0



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       Union[str, os.PathLike],**__A      :       int     ):
          cls._set_token_in_kwargs(__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   cls.get_config_dict(__A,**__A     )

          if "model_type" in config_dict and hasattr(cls,"model_type"     ) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'     )

          return cls.from_dict(__A,**__A     )



    @classmethod
    def   lowerCamelCase_  (	cls      :       str,__A      :       Dict,__A      :       Dict,**__A      :       Any     ):
          _lowerCamelCase				:   List[Any]								=   {}
          _lowerCamelCase				:   List[str]								=   text_config
          _lowerCamelCase				:   str								=   vision_config

          return cls.from_dict(__A,**__A     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   int								=   copy.deepcopy(self.__dict__     )
          _lowerCamelCase				:   Optional[int]								=   self.text_config.to_dict()
          _lowerCamelCase				:   Optional[int]								=   self.vision_config.to_dict()
          _lowerCamelCase				:   Dict								=   self.__class__.model_type
          return output


class  UpperCAmelCase__						(       A ):



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          return OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "sequence"}),
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("attention_mask", {0: "batch", 1: "sequence"}),
              ]     )



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          return OrderedDict(
              [
                  ("logits_per_image", {0: "batch"}),
                  ("logits_per_text", {0: "batch"}),
                  ("text_embeds", {0: "batch"}),
                  ("image_embeds", {0: "batch"}),
              ]     )



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          return 1e-4



    def   lowerCamelCase_  (	self      :       List[str],__A      :       "ProcessorMixin",__A      :       int = -1,__A      :       int = -1,__A      :       Optional["TensorType"] = None,):
          _lowerCamelCase				:   int								=   super().generate_dummy_inputs(
              processor.tokenizer,batch_size=__A,seq_length=__A,framework=__A     )
          _lowerCamelCase				:   List[Any]								=   super().generate_dummy_inputs(
              processor.image_processor,batch_size=__A,framework=__A     )
          return {**text_input_dict, **image_input_dict}



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          return 1_4

'''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=', ')