



class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     pass






class       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							Any				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[
          [],
          [],
          [],
      ]





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							int     ,  UpperCamelCase :							int				)->None:
      try:
       if len(self.queues[priority]				) >= 1_0_0:
        raise OverflowError("Maximum queue size is 100"				)
       self.queues[priority].append(UpperCamelCase				)
      except IndexError:
       raise ValueError("Valid priorities are 0, 1, and 2"				)





     def 	__snake_case    (       self :							Union[str, Any]				)->int:
      for queue in self.queues:
       if queue:
        return queue.pop(0				)
      raise UnderFlowError("All queues are empty"				)






     def __str__(       self :							List[str]				)->str:
      return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues				)				)






class       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							Tuple				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Dict       =	[]





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							int				)->None:
      if len(self.queue				) == 1_0_0:
       raise OverFlowError("Maximum queue size is 100"				)
      self.queue.append(UpperCamelCase				)





     def 	__snake_case    (       self :							Any				)->int:
      if not self.queue:
       raise UnderFlowError("The queue is empty"				)
      else:
       __SCREAMING_SNAKE_CASE       :    Any       =	min(self.queue				)
       self.queue.remove(UpperCamelCase				)
       return data






     def __str__(       self :							Any				)->str:
      return str(self.queue				)






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	FixedPriorityQueue()
 fpq.enqueue(0		,	10     )
 fpq.enqueue(1		,	70     )
 fpq.enqueue(0		,	100     )
 fpq.enqueue(2		,	1     )
 fpq.enqueue(2		,	5     )
 fpq.enqueue(1		,	7     )
 fpq.enqueue(2		,	4     )
 fpq.enqueue(1		,	64     )
 fpq.enqueue(0		,	128     )
 print(__lowerCamelCase     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(__lowerCamelCase     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )
 print(fpq.dequeue()     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ElementPriorityQueue()
 epq.enqueue(10     )
 epq.enqueue(70     )
 epq.enqueue(100     )
 epq.enqueue(1     )
 epq.enqueue(5     )
 epq.enqueue(7     )
 epq.enqueue(4     )
 epq.enqueue(64     )
 epq.enqueue(128     )
 print(__lowerCamelCase     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(__lowerCamelCase     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )
 print(epq.dequeue()     )


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











import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  {
    """BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""altclip_text_model"""





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							List[Any]=2_5_0_0_0_2     ,  UpperCamelCase :							int=1_0_2_4     ,  UpperCamelCase :							Dict=2_4     ,  UpperCamelCase :							Dict=1_6     ,  UpperCamelCase :							Dict=4_0_9_6     ,  UpperCamelCase :							List[str]="gelu"     ,  UpperCamelCase :							Any=0.1     ,  UpperCamelCase :							List[str]=0.1     ,  UpperCamelCase :							Union[str, Any]=5_1_4     ,  UpperCamelCase :							List[Any]=1     ,  UpperCamelCase :							Any=0.0_2     ,  UpperCamelCase :							Any=0.0_2     ,  UpperCamelCase :							Union[str, Any]=1E-05     ,  UpperCamelCase :							List[str]=1     ,  UpperCamelCase :							Dict=0     ,  UpperCamelCase :							Tuple=2     ,  UpperCamelCase :							Union[str, Any]="absolute"     ,  UpperCamelCase :							Optional[int]=True     ,  UpperCamelCase :							Any=7_6_8     ,  **UpperCamelCase :							str     ,  )->Any:
      super().__init__(pad_token_id=UpperCamelCase     ,  bos_token_id=UpperCamelCase     ,  eos_token_id=UpperCamelCase     ,  **UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	vocab_size
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    Any       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    int       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    List[str]       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Dict       =	max_position_embeddings
      __SCREAMING_SNAKE_CASE       :    List[str]       =	type_vocab_size
      __SCREAMING_SNAKE_CASE       :    Any       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    int       =	initializer_factor
      __SCREAMING_SNAKE_CASE       :    List[str]       =	layer_norm_eps
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	position_embedding_type
      __SCREAMING_SNAKE_CASE       :    List[str]       =	use_cache
      __SCREAMING_SNAKE_CASE       :    Any       =	project_dim






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""altclip_vision_model"""





     def __init__(       self :							Dict     ,  UpperCamelCase :							Tuple=7_6_8     ,  UpperCamelCase :							Optional[int]=3_0_7_2     ,  UpperCamelCase :							Optional[Any]=5_1_2     ,  UpperCamelCase :							int=1_2     ,  UpperCamelCase :							List[Any]=1_2     ,  UpperCamelCase :							List[str]=3     ,  UpperCamelCase :							List[Any]=2_2_4     ,  UpperCamelCase :							int=3_2     ,  UpperCamelCase :							Optional[Any]="quick_gelu"     ,  UpperCamelCase :							List[Any]=1E-5     ,  UpperCamelCase :							List[str]=0.0     ,  UpperCamelCase :							Tuple=0.0_2     ,  UpperCamelCase :							str=1.0     ,  **UpperCamelCase :							Tuple     ,  )->Optional[int]:
      super().__init__(**UpperCamelCase				)

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






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

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

      # get the vision config dict if we are loading from AltCLIPConfig
      if config_dict.get("model_type"				) == "altclip":
       __SCREAMING_SNAKE_CASE       :    List[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(UpperCamelCase     ,  **UpperCamelCase				)






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""altclip"""
     lowerCAmelCase					=			True





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							List[str]=None     ,  UpperCamelCase :							List[str]=None     ,  UpperCamelCase :							Union[str, Any]=7_6_8     ,  UpperCamelCase :							Optional[Any]=2.6_5_9_2     ,  **UpperCamelCase :							int				)->Any:
      # If `_config_dict` exist, we use them for the backward compatibility.
      # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
      # of confusion!).
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs.pop("text_config_dict"     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	kwargs.pop("vision_config_dict"     ,  UpperCamelCase				)

      super().__init__(**UpperCamelCase				)

      # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
      # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
      # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
      if text_config_dict is not None:
       if text_config is None:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	{}

       # This is the complete result when using `text_config_dict`.
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	AltCLIPTextConfig(**UpperCamelCase				).to_dict()

       # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
       for key, value in _text_config_dict.items():
        if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
         # If specified in `text_config_dict`
         if key in text_config_dict:
          __SCREAMING_SNAKE_CASE       :    List[str]       =	(
              F"""`{key}` is found in both `text_config_dict` and `text_config` but with different values. """
              F"""The value `text_config_dict[\"{key}\"]` will be used instead."""
          )
         # If inferred from default argument values (just to be super careful)
         else:
          __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(
              F"""`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The """
              F"""value `text_config[\"{key}\"]` will be overriden."""
          )
         logger.warning(UpperCamelCase				)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
       text_config.update(_text_config_dict				)

      if vision_config_dict is not None:
       if vision_config is None:
        __SCREAMING_SNAKE_CASE       :    int       =	{}

       # This is the complete result when using `vision_config_dict`.
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AltCLIPVisionConfig(**UpperCamelCase				).to_dict()
       # convert keys to string instead of integer
       if "id2label" in _vision_config_dict:
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	{
            str(UpperCamelCase				): value for key, value in _vision_config_dict["id2label"].items()
        }

       # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
       for key, value in _vision_config_dict.items():
        if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
         # If specified in `vision_config_dict`
         if key in vision_config_dict:
          __SCREAMING_SNAKE_CASE       :    Dict       =	(
              F"""`{key}` is found in both `vision_config_dict` and `vision_config` but with different """
              F"""values. The value `vision_config_dict[\"{key}\"]` will be used instead."""
          )
         # If inferred from default argument values (just to be super careful)
         else:
          __SCREAMING_SNAKE_CASE       :    Dict       =	(
              F"""`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. """
              F"""The value `vision_config[\"{key}\"]` will be overriden."""
          )
         logger.warning(UpperCamelCase				)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
       vision_config.update(_vision_config_dict				)

      if text_config is None:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	{}
       logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."				)

      if vision_config is None:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	{}
       logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	AltCLIPTextConfig(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	AltCLIPVisionConfig(**UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	projection_dim
      __SCREAMING_SNAKE_CASE       :    int       =	logit_scale_init_value
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	1.0





     @classmethod
     def 	__snake_case    (       cls :							Dict     ,  UpperCamelCase :							AltCLIPTextConfig     ,  UpperCamelCase :							AltCLIPVisionConfig     ,  **UpperCamelCase :							int				)->Dict:
      return cls(text_config=text_config.to_dict()     ,  vision_config=vision_config.to_dict()     ,  **UpperCamelCase				)






     def 	__snake_case    (       self :							str				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	copy.deepcopy(self.__dict__				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.text_config.to_dict()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.vision_config.to_dict()
      __SCREAMING_SNAKE_CASE       :    int       =	self.__class__.model_type
      return output






