import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_						):




  @register_to_config
  def __init__(      self     :	Tuple     , a_     :	int = 128     , a_     :	int = 256     , a_     :	float = 2000.0     , a_     :	int = 768     , a_     :	int = 12     , a_     :	int = 12     , a_     :	int = 64     , a_     :	int = 2048     , a_     :	float = 0.1     , )->       Dict:




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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		nn.Sequential(
           nn.Linear(a_     , d_model * 4     , bias=a_				)     , nn.SiLU()     , nn.Linear(d_model * 4     , d_model * 4     , bias=a_				)     , nn.SiLU()     , )

       SCREAMING_SNAKE_CASE__				:				Any			=		nn.Embedding(a_     , a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		False

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.Linear(a_     , a_     , bias=a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		nn.Dropout(p=a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.ModuleList()
       for lyr_num in range(a_				):
            # FiLM conditional T5 decoder
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		DecoderLayer(d_model=a_     , d_kv=a_     , num_heads=a_     , d_ff=a_     , dropout_rate=a_				)
            self.decoders.append(a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		TaLayerNorm(a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		nn.Dropout(p=a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		nn.Linear(a_     , a_     , bias=a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.mul(query_input.unsqueeze(-1				)     , key_input.unsqueeze(-2				)				)
       return mask.unsqueeze(-3				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	Optional[Any]     , a_     :	Tuple				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

       # decoder_noise_time is in [0, 1), so rescale to expected timing range.
       SCREAMING_SNAKE_CASE__				:				Dict			=		get_timestep_embedding(
           decoder_noise_time * self.config.max_decoder_noise_time     , embedding_dim=self.config.d_model     , max_period=self.config.max_decoder_noise_time     , ).to(dtype=self.dtype				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.conditioning_emb(a_				).unsqueeze(1				)

       assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		decoder_input_tokens.shape[1]

       # If we want to use relative positions for audio context, we can just offset
       # this sequence by the length of encodings_and_masks.
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.broadcast_to(
           torch.arange(a_     , device=decoder_input_tokens.device				)     , (batch, seq_length)     , )

       SCREAMING_SNAKE_CASE__				:				str			=		self.position_encoding(a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.continuous_inputs_projection(a_				)
       inputs += position_encodings
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.dropout(a_				)

       # decoder: No padding present.
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.ones(
           decoder_input_tokens.shape[:2]     , device=decoder_input_tokens.device     , dtype=inputs.dtype				)

       # Translate encoding masks to encoder-decoder masks.
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[(x, self.encoder_decoder_mask(a_     , a_				)) for x, y in encodings_and_masks]

       # cross attend style: concat encodings
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat([x[0] for x in encodings_and_encdec_masks]     , dim=1				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.cat([x[1] for x in encodings_and_encdec_masks]     , dim=-1				)

       for lyr in self.decoders:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		lyr(
                a_     , conditioning_emb=a_     , encoder_hidden_states=a_     , encoder_attention_mask=a_     , )[0]

       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.decoder_norm(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.post_dropout(a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		self.spec_out(a_				)
       return spec_out




class       snake_case					(      nn.Module						):




  def __init__(      self     :	int     , a_     :	Any     , a_     :	int     , a_     :	Optional[int]     , a_     :	int     , a_     :	int     , a_     :	str=1e-6				)->       Any:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		nn.ModuleList()

       # cond self attention: layer 0
       self.layer.append(
           TaLayerSelfAttentionCond(d_model=a_     , d_kv=a_     , num_heads=a_     , dropout_rate=a_				)				)

       # cross attention: layer 1
       self.layer.append(
           TaLayerCrossAttention(
               d_model=a_     , d_kv=a_     , num_heads=a_     , dropout_rate=a_     , layer_norm_epsilon=a_     , )				)

       # Film Cond MLP + dropout: last layer
       self.layer.append(
           TaLayerFFCond(d_model=a_     , d_ff=a_     , dropout_rate=a_     , layer_norm_epsilon=a_				)				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.layer[0](
           a_     , conditioning_emb=a_     , attention_mask=a_     , )

       if encoder_hidden_states is not None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.where(encoder_attention_mask > 0     , 0     , -1e1_0				).to(
                encoder_hidden_states.dtype				)

            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.layer[1](
                a_     , key_value_states=a_     , attention_mask=a_     , )

       # Apply Film Conditional Feed Forward layer
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.layer[-1](a_     , a_				)

       return (hidden_states,)




class       snake_case					(      nn.Module						):




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




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		TaLayerNorm(a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		TaFiLMLayer(in_features=d_model * 4     , out_features=a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		Attention(query_dim=a_     , heads=a_     , dim_head=a_     , out_bias=a_     , scale_qk=a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		nn.Dropout(a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	List[str]     , a_     :	str=None     , a_     :	Tuple=None     , )->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.layer_norm(a_				)

       if conditioning_emb is not None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.FiLMLayer(a_     , a_				)

       # Self-attention block
       SCREAMING_SNAKE_CASE__				:				str			=		self.attention(a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		hidden_states + self.dropout(a_				)

       return hidden_states




class       snake_case					(      nn.Module						):




  def __init__(      self     :	List[Any]     , a_     :	str     , a_     :	int     , a_     :	str     , a_     :	Union[str, Any]     , a_     :	Optional[int]				)->       Union[str, Any]:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Dict			=		Attention(query_dim=a_     , heads=a_     , dim_head=a_     , out_bias=a_     , scale_qk=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		TaLayerNorm(a_     , eps=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		nn.Dropout(a_				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	Dict     , a_     :	List[str]=None     , a_     :	Tuple=None     , )->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.layer_norm(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		self.attention(
           a_     , encoder_hidden_states=a_     , attention_mask=attention_mask.squeeze(1				)     , )
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		hidden_states + self.dropout(a_				)
       return layer_output




class       snake_case					(      nn.Module						):




  def __init__(      self     :	List[str]     , a_     :	str     , a_     :	List[Any]     , a_     :	List[Any]     , a_     :	Any				)->       Optional[int]:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		TaDenseGatedActDense(d_model=a_     , d_ff=a_     , dropout_rate=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		TaFiLMLayer(in_features=d_model * 4     , out_features=a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		TaLayerNorm(a_     , eps=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		nn.Dropout(a_				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Dict     , a_     :	Any=None				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.layer_norm(a_				)
       if conditioning_emb is not None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.film(a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.DenseReluDense(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hidden_states + self.dropout(a_				)
       return hidden_states




class       snake_case					(      nn.Module						):




  def __init__(      self     :	Optional[Any]     , a_     :	Tuple     , a_     :	int     , a_     :	Optional[int]				)->       List[str]:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		nn.Linear(a_     , a_     , bias=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.Linear(a_     , a_     , bias=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.Linear(a_     , a_     , bias=a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		nn.Dropout(a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		NewGELUActivation()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.act(self.wi_a(a_				)				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.wi_a(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		hidden_gelu * hidden_linear
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.dropout(a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.wo(a_				)
       return hidden_states




class       snake_case					(      nn.Module						):




  def __init__(      self     :	str     , a_     :	Union[str, Any]     , a_     :	Optional[int]=1e-6				)->       Any:




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		nn.Parameter(torch.ones(a_				)				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		eps




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		hidden_states.to(torch.floataa				).pow(2				).mean(-1     , keepdim=a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

       # convert into half-precision if necessary
       if self.weight.dtype in [torch.floataa, torch.bfloataa]:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		hidden_states.to(self.weight.dtype				)

       return self.weight * hidden_states




class       snake_case					(      nn.Module						):




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	torch.Tensor				)->       torch.Tensor:




       """simple docstring"""
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(a_     , 3.0				))				))




class       snake_case					(      nn.Module						):




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




       """simple docstring"""
       super().__init__()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		nn.Linear(a_     , out_features * 2     , bias=a_				)




  def 	__lowercase(      self     :	int     , a_     :	List[Any]     , a_     :	Dict				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.scale_bias(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.chunk(a_     , 2     , -1				)
       SCREAMING_SNAKE_CASE__				:				int			=		x * (1 + scale) + shift
       return x




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()



