

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''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_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       "owlvit_text_model"


							def __init__(     self			,				_a=4_9_4_0_8			,				_a=5_1_2			,				_a=2_0_4_8			,				_a=1_2			,				_a=8			,				_a=1_6			,				_a="quick_gelu"			,				_a=1e-5			,				_a=0.0			,				_a=0.02			,				_a=1.0			,				_a=0			,				_a=4_9_4_0_6			,				_a=4_9_4_0_7			,				**_a			,				)       ->       int:
												super().__init__(pad_token_id=_a			,				bos_token_id=_a			,				eos_token_id=_a			,				**_a			)

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




							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       "PretrainedConfig":
												cls._set_token_in_kwargs(_a			)

												_a ,   _a      :       Optional[int]   =						cls.get_config_dict(_a			,				**_a			)

												# get the text config dict if we are loading from OwlViTConfig
												if config_dict.get('''model_type'''			) == "owlvit":
																	_a      :       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_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "owlvit_vision_model"


							def __init__(     self			,				_a=7_6_8			,				_a=3_0_7_2			,				_a=1_2			,				_a=1_2			,				_a=3			,				_a=7_6_8			,				_a=3_2			,				_a="quick_gelu"			,				_a=1e-5			,				_a=0.0			,				_a=0.02			,				_a=1.0			,				**_a			,				)       ->       Dict:
												super().__init__(**_a			)

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




							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       "PretrainedConfig":
												cls._set_token_in_kwargs(_a			)

												_a ,   _a      :       Tuple   =						cls.get_config_dict(_a			,				**_a			)

												# get the vision config dict if we are loading from OwlViTConfig
												if config_dict.get('''model_type'''			) == "owlvit":
																	_a      :       str   =						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_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "owlvit"
							UpperCAmelCase__			:		str             =       True


							def __init__(     self			,				_a=None			,				_a=None			,				_a=5_1_2			,				_a=2.6592			,				_a=True			,				**_a			,				)       ->       Any:
												super().__init__(**_a			)

												if text_config is None:
																	_a      :       Union[str, Any]   =						{}
																	logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''			)

												if vision_config is None:
																	_a      :       List[str]   =						{}
																	logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''			)

												_a      :       str   =						OwlViTTextConfig(**_a			)
												_a      :       Dict   =						OwlViTVisionConfig(**_a			)

												_a      :       int   =						projection_dim
												_a      :       Optional[int]   =						logit_scale_init_value
												_a      :       List[Any]   =						return_dict
												_a      :       Optional[Any]   =						1.0


							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       "PretrainedConfig":
												cls._set_token_in_kwargs(_a			)

												_a ,   _a      :       Dict   =						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 							__lowercase							(     cls			,				_a			,				_a			,				**_a			)       ->       Dict:
												_a      :       int   =						{}
												_a      :       Optional[int]   =						text_config
												_a      :       Optional[Any]   =						vision_config

												return cls.from_dict(_a			,				**_a			)




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Any   =						copy.deepcopy(self.__dict__			)
												_a      :       Optional[int]   =						self.text_config.to_dict()
												_a      :       Optional[Any]   =						self.vision_config.to_dict()
												_a      :       Union[str, Any]   =						self.__class__.model_type
												return output



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												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 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('''logits_per_image''', {0: '''batch'''}),
												        ('''logits_per_text''', {0: '''batch'''}),
												        ('''text_embeds''', {0: '''batch'''}),
												        ('''image_embeds''', {0: '''batch'''}),
												    ]			)


							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4


							def 							__lowercase							(     self			,				_a			,				_a = -1			,				_a = -1			,				_a = None			,				)       ->       Mapping[str, Any]:
												_a      :       Dict   =						super().generate_dummy_inputs(
												    processor.tokenizer			,				batch_size=_a			,				seq_length=_a			,				framework=_a			)
												_a      :       Optional[Any]   =						super().generate_dummy_inputs(
												    processor.image_processor			,				batch_size=_a			,				framework=_a			)
												return {**text_input_dict, **image_input_dict}




							@property
							def 							__lowercase							(     self			)       ->       int:
												return 1_4





class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   str = ""       ,		snake_case__						:   bool = False					)						->      None:


          '''simple docstring'''


          snake_case			:  dict[str, RadixNode]     							= {}

          # A node will be a leaf if the tree contains its word
          snake_case			:  int     							= is_leaf

          snake_case			:  Dict     							= prefix



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str					)						->      tuple[str, str, str]:


          '''simple docstring'''


          snake_case			:  Any     							= 0
          for q, w in zip(self.prefix       ,		snake_case__					):
                 if q != w:
                        break

                 x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   list[str]					)						->      None:


          '''simple docstring'''


          for word in words:
                 self.insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str					)						->      None:


          '''simple docstring'''


          if self.prefix == word:
                 snake_case			:  str     							= True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
                 snake_case			:  Optional[int]     							= RadixNode(prefix=snake_case__       ,		is_leaf=snake_case__					)

          else:
                 snake_case			:  Dict     							= self.nodes[word[0]]
                 snake_case							,   snake_case							,   snake_case			:  int     							= incoming_node.match(
                     snake_case__					)

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                        self.nodes[matching_string[0]].insert(snake_case__					)

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                        snake_case			:  Tuple     							= remaining_prefix

                        snake_case			:  Dict     							= self.nodes[matching_string[0]]
                        snake_case			:  Tuple     							= RadixNode(snake_case__       ,		snake_case__					)
                        snake_case			:  List[str]     							= aux_node

                        if remaining_word == "":
                               snake_case			:  Tuple     							= True
                        else:
                               self.nodes[matching_string[0]].insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[int]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                        return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                        return incoming_node.find(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                        return incoming_node.delete(snake_case__					)
                 else:
                        # If it is not a leaf, we don't have to delete
                        if not incoming_node.is_leaf:
                               return False
                        else:
                               # We delete the nodes if no edges go from it
                               if len(incoming_node.nodes					) == 0:
                                      del self.nodes[word[0]]
                                      # We merge the current node with its only child
                                      if len(self.nodes					) == 1 and not self.is_leaf:
                                             snake_case			:  List[Any]     							= list(self.nodes.values()					)[0]
                                             snake_case			:  Optional[int]     							= merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             snake_case			:  str     							= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                               elif len(incoming_node.nodes					) > 1:
                                      snake_case			:  List[Any]     							= False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      snake_case			:  Any     							= list(incoming_node.nodes.values()					)[0]
                                      snake_case			:  Any     							= merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      snake_case			:  Union[str, Any]     							= merging_node.nodes

                               return True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int = 0					)						->      None:


          '''simple docstring'''


          if self.prefix != "":
                 print("-" * height       ,		self.prefix       ,		"  (leaf)" if self.is_leaf else ""					)

          for value in self.nodes.values():
                 value.print_tree(height + 1					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= "banana bananas bandana band apple all beast".split()
       snake_case			:  Tuple     							= RadixNode()
       root.insert_many(__lowerCamelCase       )

       assert all(root.find(__lowerCamelCase       ) for word in words       )
       assert not root.find("bandanas"       )
       assert not root.find("apps"       )
       root.delete("all"       )
       assert not root.find("all"       )
       root.delete("banana"       )
       assert not root.find("banana"       )
       assert root.find("bananas"       )

       return True


def      UpperCamelCase   (       ):
       assert test_trie()


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= RadixNode()
       snake_case			:  List[str]     							= "banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(__lowerCamelCase       )

       print("Words:"							,   __lowerCamelCase       )
       print("Tree:"       )
       root.print_tree()


if __name__ == "__main__":
   main()





