from dataclasses import asdict, dataclass
from typing import Optional

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


SCREAMING_SNAKE_CASE__		:					str						=					logging.get_logger(__name__)

# TODO Update this
SCREAMING_SNAKE_CASE__		:					Tuple						=					{
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'esm'




  def __init__(      self     :	int     , a_     :	Dict=None     , a_     :	Any=None     , a_     :	List[Any]=None     , a_     :	Dict=768     , a_     :	int=12     , a_     :	Optional[Any]=12     , a_     :	Tuple=3072     , a_     :	Dict=0.1     , a_     :	Any=0.1     , a_     :	List[str]=1026     , a_     :	List[Any]=0.02     , a_     :	Optional[Any]=1e-1_2     , a_     :	int="absolute"     , a_     :	List[str]=True     , a_     :	List[Any]=None     , a_     :	str=False     , a_     :	Any=False     , a_     :	List[str]=None     , a_     :	str=None     , **a_     :	List[str]     , )->       List[str]:




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

       SCREAMING_SNAKE_CASE__				:				str			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_hidden_layers
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		num_attention_heads
       SCREAMING_SNAKE_CASE__				:				Dict			=		intermediate_size
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Any			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				Tuple			=		layer_norm_eps
       SCREAMING_SNAKE_CASE__				:				str			=		position_embedding_type
       SCREAMING_SNAKE_CASE__				:				Dict			=		use_cache
       SCREAMING_SNAKE_CASE__				:				List[str]			=		emb_layer_norm_before
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		token_dropout
       SCREAMING_SNAKE_CASE__				:				Dict			=		is_folding_model
       if is_folding_model:
            if esmfold_config is None:
                 logger.info('No esmfold_config supplied for folding model, using default values.'				)
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		EsmFoldConfig()
            elif isinstance(a_     , a_				):
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		EsmFoldConfig(**a_				)
            SCREAMING_SNAKE_CASE__				:				Any			=		esmfold_config
            if vocab_list is None:
                 logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'				)
                 SCREAMING_SNAKE_CASE__				:				int			=		get_default_vocab_list()
            else:
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		vocab_list
       else:
            SCREAMING_SNAKE_CASE__				:				Dict			=		None
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		None
       if self.esmfold_config is not None and getattr(self.esmfold_config     , 'use_esm_attn_map'     , a_				):
            raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		super().to_dict()
       if isinstance(self.esmfold_config     , a_				):
            SCREAMING_SNAKE_CASE__				:				int			=		self.esmfold_config.to_dict()
       return output




@dataclass
class       snake_case					:
  lowercase_			=     None
  lowercase_			=     True
  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     0

  lowercase_			=     True
  lowercase_			=     False

  lowercase_			=     128
  lowercase_			=     None




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




       """simple docstring"""
       if self.trunk is None:
            SCREAMING_SNAKE_CASE__				:				str			=		TrunkConfig()
       elif isinstance(self.trunk     , a_				):
            SCREAMING_SNAKE_CASE__				:				Dict			=		TrunkConfig(**self.trunk				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		asdict(self				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.trunk.to_dict()
       return output




@dataclass
class       snake_case					:
  lowercase_			=     48
  lowercase_			=     1_024
  lowercase_			=     128
  lowercase_			=     32
  lowercase_			=     32
  lowercase_			=     32
  lowercase_			=     0
  lowercase_			=     0
  lowercase_			=     False
  lowercase_			=     4
  lowercase_			=     128
  lowercase_			=     None




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




       """simple docstring"""
       if self.structure_module is None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		StructureModuleConfig()
       elif isinstance(self.structure_module     , a_				):
            SCREAMING_SNAKE_CASE__				:				Dict			=		StructureModuleConfig(**self.structure_module				)

       if self.max_recycles <= 0:
            raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.'''				)
       if self.sequence_state_dim % self.sequence_state_dim != 0:
            raise ValueError(
                '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
                F''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''				)
       if self.pairwise_state_dim % self.pairwise_state_dim != 0:
            raise ValueError(
                '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
                F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''				)

       SCREAMING_SNAKE_CASE__				:				int			=		self.sequence_state_dim // self.sequence_head_width
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.pairwise_state_dim // self.pairwise_head_width

       if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
            raise ValueError(
                '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
                F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''				)
       if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
            raise ValueError(
                '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
                F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''				)
       if self.pairwise_state_dim % 2 != 0:
            raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''				)

       if self.dropout >= 0.4:
            raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.'''				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		asdict(self				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.structure_module.to_dict()
       return output




@dataclass
class       snake_case					:
  lowercase_			=     384
  lowercase_			=     128
  lowercase_			=     16
  lowercase_			=     128
  lowercase_			=     12
  lowercase_			=     4
  lowercase_			=     8
  lowercase_			=     0.1
  lowercase_			=     8
  lowercase_			=     1
  lowercase_			=     2
  lowercase_			=     7
  lowercase_			=     10
  lowercase_			=     1e-8
  lowercase_			=     1e5




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




       """simple docstring"""
       return asdict(self				)







def     _a				(      ):







     '''simple docstring'''


     return (
         "<cls>",
         "<pad>",
         "<eos>",
         "<unk>",
         "L",
         "A",
         "G",
         "V",
         "S",
         "E",
         "R",
         "T",
         "I",
         "D",
         "P",
         "K",
         "Q",
         "N",
         "F",
         "Y",
         "M",
         "H",
         "W",
         "C",
         "X",
         "B",
         "U",
         "Z",
         "O",
         ".",
         "-",
         "<null_1>",
         "<mask>",
     )




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()



