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


SCREAMING_SNAKE_CASE__		:					str						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					List[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       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'owlvit_text_model'




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




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

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




  @classmethod
  def 	__lowercase(      cls     :	Dict     , a_     :	Union[str, os.PathLike]     , **a_     :	Any				)->       "PretrainedConfig":




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

       SCREAMING_SNAKE_CASE__				:				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":
            SCREAMING_SNAKE_CASE__				:				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(a_     , **a_				)




class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'owlvit_vision_model'




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




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

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




  @classmethod
  def 	__lowercase(      cls     :	Optional[Any]     , a_     :	Union[str, os.PathLike]     , **a_     :	str				)->       "PretrainedConfig":




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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		cls.get_config_dict(a_     , **a_				)

       # get the vision config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'				) == "owlvit":
            SCREAMING_SNAKE_CASE__				:				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       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'owlvit'
  lowercase_			=     True




  def __init__(      self     :	Tuple     , a_     :	Union[str, Any]=None     , a_     :	Any=None     , a_     :	Dict=512     , a_     :	int=2.6592     , a_     :	int=True     , **a_     :	int     , )->       str:




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

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

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

       SCREAMING_SNAKE_CASE__				:				str			=		OwlViTTextConfig(**a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		OwlViTVisionConfig(**a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		projection_dim
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		logit_scale_init_value
       SCREAMING_SNAKE_CASE__				:				str			=		return_dict
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		1.0




  @classmethod
  def 	__lowercase(      cls     :	int     , a_     :	Union[str, os.PathLike]     , **a_     :	str				)->       "PretrainedConfig":




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

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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     :	int     , a_     :	Dict     , a_     :	Dict     , **a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{}
       SCREAMING_SNAKE_CASE__				:				str			=		text_config
       SCREAMING_SNAKE_CASE__				:				int			=		vision_config

       return cls.from_dict(a_     , **a_				)




  def 	__lowercase(      self     :	List[str]				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		copy.deepcopy(self.__dict__				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.text_config.to_dict()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.vision_config.to_dict()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.__class__.model_type
       return output




class       snake_case					(      UpperCamelCase_						):




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       Mapping[str, Mapping[int, 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 	__lowercase(      self     :	List[Any]				)->       Mapping[str, Mapping[int, str]]:




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




  @property
  def 	__lowercase(      self     :	int				)->       float:




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




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	"ProcessorMixin"     , a_     :	int = -1     , a_     :	int = -1     , a_     :	Optional["TensorType"] = None     , )->       Mapping[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		super().generate_dummy_inputs(
           processor.tokenizer     , batch_size=a_     , seq_length=a_     , framework=a_				)
       SCREAMING_SNAKE_CASE__				:				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				)->       int:




       """simple docstring"""
       return 14




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					(      UpperCamelCase_						):
  pass




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		[
           [],
           [],
           [],
       ]




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	int				)->       None:




       """simple docstring"""
       try:
            if len(self.queues[priority]				) >= 100:
                 raise OverflowError('Maximum queue size is 100'				)
            self.queues[priority].append(a_				)
       except IndexError:
            raise ValueError('Valid priorities are 0, 1, and 2'				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       for queue in self.queues:
            if queue:
                 return queue.pop(0				)
       raise UnderFlowError('All queues are empty'				)




  def __str__(      self     :	Any				)->       str:




       """simple docstring"""
       return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)




class       snake_case					:




  def __init__(      self     :	Union[str, Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]




  def 	__lowercase(      self     :	List[str]     , a_     :	int				)->       None:




       """simple docstring"""
       if len(self.queue				) == 100:
            raise OverFlowError('Maximum queue size is 100'				)
       self.queue.append(a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       if not self.queue:
            raise UnderFlowError('The queue is empty'				)
       else:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		min(self.queue				)
            self.queue.remove(a_				)
            return data




  def __str__(      self     :	List[str]				)->       str:




       """simple docstring"""
       return str(self.queue				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		FixedPriorityQueue()
     fpq.enqueue(0     ,  10 )
     fpq.enqueue(1     ,  70 )
     fpq.enqueue(0     ,  1_00 )
     fpq.enqueue(2     ,  1 )
     fpq.enqueue(2     ,  5 )
     fpq.enqueue(1     ,  7 )
     fpq.enqueue(2     ,  4 )
     fpq.enqueue(1     ,  64 )
     fpq.enqueue(0     ,  1_28 )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(lowercase__ )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )
     print(fpq.dequeue() )







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		ElementPriorityQueue()
     epq.enqueue(10 )
     epq.enqueue(70 )
     epq.enqueue(1_00 )
     epq.enqueue(1 )
     epq.enqueue(5 )
     epq.enqueue(7 )
     epq.enqueue(4 )
     epq.enqueue(64 )
     epq.enqueue(1_28 )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(lowercase__ )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )
     print(epq.dequeue() )


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()



