
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__		:     str     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     str     			=      {
    '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   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    '''owlvit_text_model'''


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

      """simple docstring"""
      super().__init__(pad_token_id=lowerCAmelCase_			,			bos_token_id=lowerCAmelCase_			,			eos_token_id=lowerCAmelCase_			,			**lowerCAmelCase_     )

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


   @classmethod
   def  __magic_name__				(					cls     :							Optional[int]			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			**lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      cls._set_token_in_kwargs(lowerCAmelCase_     )

      _A			,  _A:  Optional[Any]					=       cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )

      # get the text config dict if we are loading from OwlViTConfig
      if config_dict.get('''model_type'''     ) == "owlvit":
         _A:  Dict					=       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(lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[int] 				=    '''owlvit_vision_model'''


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

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

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


   @classmethod
   def  __magic_name__				(					cls     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			**lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      cls._set_token_in_kwargs(lowerCAmelCase_     )

      _A			,  _A:  Optional[Any]					=       cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )

      # get the vision config dict if we are loading from OwlViTConfig
      if config_dict.get('''model_type'''     ) == "owlvit":
         _A:  Tuple					=       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(lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[str] 				=    '''owlvit'''
   __UpperCamelCase		:    Any 				=    True


   def __init__(					self     :							Tuple			,			lowerCAmelCase_     :							Optional[Any]=None			,			lowerCAmelCase_     :							List[Any]=None			,			lowerCAmelCase_     :							Tuple=5_1_2			,			lowerCAmelCase_     :							Tuple=2.6592			,			lowerCAmelCase_     :							Optional[int]=True			,			**lowerCAmelCase_     :							List[str]			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      if text_config is None:
         _A:  int					=       {}
         logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''     )

      if vision_config is None:
         _A:  Optional[int]					=       {}
         logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''     )

      _A:  Tuple					=       OwlViTTextConfig(**lowerCAmelCase_     )
      _A:  Optional[Any]					=       OwlViTVisionConfig(**lowerCAmelCase_     )

      _A:  Tuple					=       projection_dim
      _A:  List[Any]					=       logit_scale_init_value
      _A:  Dict					=       return_dict
      _A:  Any					=       1.0


   @classmethod
   def  __magic_name__				(					cls     :							Dict			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			**lowerCAmelCase_     :							List[str]     ):

      """simple docstring"""
      cls._set_token_in_kwargs(lowerCAmelCase_     )

      _A			,  _A:  int					=       cls.get_config_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )

      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(lowerCAmelCase_			,			**lowerCAmelCase_     )


   @classmethod
   def  __magic_name__				(					cls     :							str			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Dict			,			**lowerCAmelCase_     :							List[str]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       {}
      _A:  str					=       text_config
      _A:  Optional[int]					=       vision_config

      return cls.from_dict(lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Dict					=       copy.deepcopy(self.__dict__     )
      _A:  Tuple					=       self.text_config.to_dict()
      _A:  Dict					=       self.vision_config.to_dict()
      _A:  Any					=       self.__class__.model_type
      return output






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							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  __magic_name__				(					self     :							Any     ):

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


   @property
   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      return 1e-4


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							"ProcessorMixin"			,			lowerCAmelCase_     :							int = -1			,			lowerCAmelCase_     :							int = -1			,			lowerCAmelCase_     :							Optional["TensorType"] = None			,			):

      """simple docstring"""
      _A:  Union[str, Any]					=       super().generate_dummy_inputs(
          processor.tokenizer			,			batch_size=lowerCAmelCase_			,			seq_length=lowerCAmelCase_			,			framework=lowerCAmelCase_     )
      _A:  str					=       super().generate_dummy_inputs(
          processor.image_processor			,			batch_size=lowerCAmelCase_			,			framework=lowerCAmelCase_     )
      return {**text_input_dict, **image_input_dict}


   @property
   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      return 1_4





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCAmelCase__		:     Union[str, Any]     			=      TypeVar('T')






class UpperCAmelCase   (						Generic[T]   ):





   '''simple docstring'''
   __UpperCamelCase		:    deque[T]  # Cache store of keys
   __UpperCamelCase		:    set[T]  # References of the keys in cache
   __UpperCamelCase		:    int 				=    10  # Maximum capacity of cache


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  Tuple					=       deque()
      _A:  List[Any]					=       set()
      if not n:
         _A:  str					=       sys.maxsize
      elif n < 0:
         raise ValueError('''n should be an integer greater than 0.'''     )
      else:
         _A:  Dict					=       n


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							T     ):

      """simple docstring"""
      if x not in self.key_reference:
         if len(self.dq_store     ) == LRUCache._MAX_CAPACITY:
            _A:  Optional[Any]					=       self.dq_store.pop()
            self.key_reference.remove(lowerCAmelCase_     )
      else:
         self.dq_store.remove(lowerCAmelCase_     )

      self.dq_store.appendleft(lowerCAmelCase_     )
      self.key_reference.add(lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      for k in self.dq_store:
         print(lowerCAmelCase_     )


   def __repr__(					self     :							Dict     ):

      """simple docstring"""
      return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store     )}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase__		:     LRUCache[str | int]     			=      LRUCache(4)
   lru_cache.refer('A')
   lru_cache.refer(2)
   lru_cache.refer(3)
   lru_cache.refer('A')
   lru_cache.refer(4)
   lru_cache.refer(5)
   lru_cache.display()

   print(lru_cache)
   assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



