





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


__lowercase  :   str						      =logging.get_logger(__name__)

__lowercase  :   Any						      ={
    """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      A       (	lowercase__						):
      _snake_case							     ='''owlvit_text_model'''



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						Optional[int]=4_9408		,  _lowerCAmelCase:						int=512		,  _lowerCAmelCase:						Dict=2048		,  _lowerCAmelCase:						int=12		,  _lowerCAmelCase:						Dict=8		,  _lowerCAmelCase:						Any=16		,  _lowerCAmelCase:						List[Any]="quick_gelu"		,  _lowerCAmelCase:						Union[str, Any]=1e-5		,  _lowerCAmelCase:						List[Any]=0.0		,  _lowerCAmelCase:						Optional[Any]=0.02		,  _lowerCAmelCase:						Union[str, Any]=1.0		,  _lowerCAmelCase:						Any=0		,  _lowerCAmelCase:						Dict=4_9406		,  _lowerCAmelCase:						str=4_9407		,  **_lowerCAmelCase:						Tuple		,  )						->    Dict:
         '''simple docstring'''

         super().__init__(pad_token_id=UpperCAmelCase__		,  bos_token_id=UpperCAmelCase__		,  eos_token_id=UpperCAmelCase__		,  **UpperCAmelCase__       )

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






      @classmethod
      def 	lowerCAmelCase__    (	cls:						Optional[int]		,  _lowerCAmelCase:						Union[str, os.PathLike]		,  **_lowerCAmelCase:						List[str]       )						->    Optional[Any]:
         '''simple docstring'''

         cls._set_token_in_kwargs(UpperCAmelCase__       )

         UpperCAmelCase_     :   List[Any]											=cls.get_config_dict(UpperCAmelCase__		,  **UpperCAmelCase__       )

         # get the text config dict if we are loading from OwlViTConfig
         if config_dict.get("model_type"       ) == "owlvit":
            UpperCAmelCase_     :   Optional[Any]											=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(UpperCAmelCase__		,  **UpperCAmelCase__       )

class      A       (	lowercase__						):
      _snake_case							     ='''owlvit_vision_model'''



      def __init__(	self:						Tuple		,  _lowerCAmelCase:						int=768		,  _lowerCAmelCase:						Tuple=3072		,  _lowerCAmelCase:						Dict=12		,  _lowerCAmelCase:						Optional[Any]=12		,  _lowerCAmelCase:						Any=3		,  _lowerCAmelCase:						Union[str, Any]=768		,  _lowerCAmelCase:						int=32		,  _lowerCAmelCase:						int="quick_gelu"		,  _lowerCAmelCase:						str=1e-5		,  _lowerCAmelCase:						Optional[Any]=0.0		,  _lowerCAmelCase:						Union[str, Any]=0.02		,  _lowerCAmelCase:						Dict=1.0		,  **_lowerCAmelCase:						Union[str, Any]		,  )						->    Dict:
         '''simple docstring'''

         super().__init__(**UpperCAmelCase__       )

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






      @classmethod
      def 	lowerCAmelCase__    (	cls:						Tuple		,  _lowerCAmelCase:						Union[str, os.PathLike]		,  **_lowerCAmelCase:						Union[str, Any]       )						->    str:
         '''simple docstring'''

         cls._set_token_in_kwargs(UpperCAmelCase__       )

         UpperCAmelCase_     :   str											=cls.get_config_dict(UpperCAmelCase__		,  **UpperCAmelCase__       )

         # get the vision config dict if we are loading from OwlViTConfig
         if config_dict.get("model_type"       ) == "owlvit":
            UpperCAmelCase_     :   Optional[Any]											=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(UpperCAmelCase__		,  **UpperCAmelCase__       )

class      A       (	lowercase__						):
      _snake_case							     ='''owlvit'''
      _snake_case							     =True



      def __init__(	self:						str		,  _lowerCAmelCase:						str=None		,  _lowerCAmelCase:						Any=None		,  _lowerCAmelCase:						Union[str, Any]=512		,  _lowerCAmelCase:						List[Any]=2.65_92		,  _lowerCAmelCase:						Tuple=True		,  **_lowerCAmelCase:						Optional[int]		,  )						->    Tuple:
         '''simple docstring'''

         super().__init__(**UpperCAmelCase__       )

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

         if vision_config is None:
            UpperCAmelCase_     :   Dict											={}
            logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."       )

         UpperCAmelCase_     :   Tuple											=OwlViTTextConfig(**UpperCAmelCase__       )
         UpperCAmelCase_     :   Any											=OwlViTVisionConfig(**UpperCAmelCase__       )

         UpperCAmelCase_     :   int											=projection_dim
         UpperCAmelCase_     :   List[str]											=logit_scale_init_value
         UpperCAmelCase_     :   Union[str, Any]											=return_dict
         UpperCAmelCase_     :   int											=1.0



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Tuple		,  _lowerCAmelCase:						Union[str, os.PathLike]		,  **_lowerCAmelCase:						Tuple       )						->    Optional[Any]:
         '''simple docstring'''

         cls._set_token_in_kwargs(UpperCAmelCase__       )

         UpperCAmelCase_     :   int											=cls.get_config_dict(UpperCAmelCase__		,  **UpperCAmelCase__       )

         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(UpperCAmelCase__		,  **UpperCAmelCase__       )



      @classmethod
      def 	lowerCAmelCase__    (	cls:						str		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Dict		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_     :   Dict											={}
         UpperCAmelCase_     :   Tuple											=text_config
         UpperCAmelCase_     :   Optional[Any]											=vision_config

         return cls.from_dict(UpperCAmelCase__		,  **UpperCAmelCase__       )






      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_     :   Optional[int]											=copy.deepcopy(self.__dict__       )
         UpperCAmelCase_     :   List[str]											=self.text_config.to_dict()
         UpperCAmelCase_     :   Optional[Any]											=self.vision_config.to_dict()
         UpperCAmelCase_     :   Tuple											=self.__class__.model_type
         return output

class      A       (	lowercase__						):



      @property
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         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:						Dict       )						->    Tuple:
         '''simple docstring'''

         return OrderedDict(
             [
                 ("logits_per_image", {0: "batch"}),
                 ("logits_per_text", {0: "batch"}),
                 ("text_embeds", {0: "batch"}),
                 ("image_embeds", {0: "batch"}),
             ]       )



      @property
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[Any]:
         '''simple docstring'''

         return 1e-4



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						"ProcessorMixin"		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						Optional["TensorType"] = None		,  )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_     :   Dict											=super().generate_dummy_inputs(
             processor.tokenizer		,  batch_size=UpperCAmelCase__		,  seq_length=UpperCAmelCase__		,  framework=UpperCAmelCase__       )
         UpperCAmelCase_     :   List[str]											=super().generate_dummy_inputs(
             processor.image_processor		,  batch_size=UpperCAmelCase__		,  framework=UpperCAmelCase__       )
         return {**text_input_dict, **image_input_dict}






      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    int:
         '''simple docstring'''

         return 14











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowercase  :   Optional[int]						      =TypeVar("""T""")




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (position - 1) // 2




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 1




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 2

class      A       (	Generic[T]						):



      def __init__(	self:						List[str]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __len__(	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

         return self.elements



      def __repr__(	self:						Dict       )						->    str:
         '''simple docstring'''

         return str(self.heap       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    bool:
         '''simple docstring'''

         return self.elements == 0



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.heap.append((elem, weight)       )
         UpperCAmelCase_											=self.elements
         self.elements += 1
         self._bubble_up(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    T:
         '''simple docstring'''

         if self.elements > 1:
            self._swap_nodes(0		,  self.elements - 1       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[0]
            self._bubble_down(_lowerCAmelCase       )
         return elem



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_											=(elem, weight)
         if position > 0:
            UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
            if parent_weight > weight:
               self._bubble_up(_lowerCAmelCase       )
            else:
               self._bubble_down(_lowerCAmelCase       )
         else:
            self._bubble_down(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         if curr_pos == 0:
            return None
         UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
         if parent_weight > weight:
            self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
            return self._bubble_up(_lowerCAmelCase       )
         return None



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_											=get_child_left_position(_lowerCAmelCase       )
         UpperCAmelCase_											=get_child_right_position(_lowerCAmelCase       )
         if child_left_position < self.elements and child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < child_left_weight and child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         if child_left_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            if child_left_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         else:
            return None
         if child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         return None






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_						,       UpperCAmelCase_											=(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         UpperCAmelCase_											=nodea_pos
         UpperCAmelCase_											=nodea_pos

class      A       (	Generic[T]						):



      def __init__(	self:						Tuple       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __repr__(	self:						List[str]       )						->    str:
         '''simple docstring'''

         return str(self.connections       )



      def __len__(	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         return self.nodes



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         if node not in self.connections:
            UpperCAmelCase_											={}
            self.nodes += 1






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.add_node(_lowerCAmelCase       )
         self.add_node(_lowerCAmelCase       )
         UpperCAmelCase_											=weight
         UpperCAmelCase_											=weight




def     a__ (					lowercase__  ,    ):






   '''simple docstring'''






   UpperCAmelCase_											={node: maxsize for node in graph.connections}
   UpperCAmelCase_											={node: None for node in graph.connections}

   UpperCAmelCase_											=MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(lowercase__  ,    lowercase__  )

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCAmelCase_											=priority_queue.extract_min()
   UpperCAmelCase_											=0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCAmelCase_											=dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(lowercase__  ,    dist[neighbour]  )
         UpperCAmelCase_											=node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCAmelCase_											=priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCAmelCase_											=dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(lowercase__  ,    dist[neighbour]  )
            UpperCAmelCase_											=node
   return dist, parent




