"""simple docstring"""






from __future__ import annotations


class     UpperCAmelCase						:



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[Any]					=      order

       # a_{0} ... a_{k}
       lowercase__:   Optional[int]					=      [1.0] + [0.0] * order
       # b_{0} ... b_{k}
       lowercase__:   Optional[Any]					=      [1.0] + [0.0] * order

       # x[n-1] ... x[n-k]
       lowercase__:   str					=      [0.0] * self.order
       # y[n-1] ... y[n-k]
       lowercase__:   Dict					=      [0.0] * self.order






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):






       if len(_UpperCAmelCase							) < self.order:
          lowercase__:   Any					=      [1.0, *a_coeffs]

       if len(_UpperCAmelCase							) != self.order + 1:
          lowercase__:   Optional[Any]					=      (
              F"""Expected a_coeffs to have {self.order + 1} elements """
              F"""for {self.order}-order filter, got {len(_UpperCAmelCase							)}"""
          )
          raise ValueError(_UpperCAmelCase							)

       if len(_UpperCAmelCase							) != self.order + 1:
          lowercase__:   List[str]					=      (
              F"""Expected b_coeffs to have {self.order + 1} elements """
              F"""for {self.order}-order filter, got {len(_UpperCAmelCase							)}"""
          )
          raise ValueError(_UpperCAmelCase							)

       lowercase__:   List[Any]					=      a_coeffs
       lowercase__:   Union[str, Any]					=      b_coeffs






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      0.0

       # Start at index 1 and do index 0 at the end.
       for i in range(1    ,			self.order + 1							):
          result += (
              self.b_coeffs[i] * self.input_history[i - 1]
              - self.a_coeffs[i] * self.output_history[i - 1]
          )

       lowercase__:   List[Any]					=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

       lowercase__:   Dict					=      self.input_history[:-1]
       lowercase__:   Tuple					=      self.output_history[:-1]

       lowercase__:   List[Any]					=      sample
       lowercase__:   Union[str, Any]					=      result

       return result








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 			__a  (    A__       ,   A__       ):







 @register_to_config
 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int = 1_28							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   float = 2_0_0_0.0							,   SCREAMING_SNAKE_CASE					:   int = 7_68							,   SCREAMING_SNAKE_CASE					:   int = 12							,   SCREAMING_SNAKE_CASE					:   int = 12							,   SCREAMING_SNAKE_CASE					:   int = 64							,   SCREAMING_SNAKE_CASE					:   int = 20_48							,   SCREAMING_SNAKE_CASE					:   float = 0.1							,   ):


      '''simple docstring'''






      super().__init__()

      UpperCamelCase__			:   Optional[Any]       				=  nn.Sequential(
          nn.Linear(SCREAMING_SNAKE_CASE							,   d_model * 4							,   bias=SCREAMING_SNAKE_CASE						)							,   nn.SiLU()							,   nn.Linear(d_model * 4							,   d_model * 4							,   bias=SCREAMING_SNAKE_CASE						)							,   nn.SiLU()							,   )

      UpperCamelCase__			:   Optional[int]       				=  nn.Embedding(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  False

      UpperCamelCase__			:   Union[str, Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)

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

      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  torch.mul(query_input.unsqueeze(-1						)							,   key_input.unsqueeze(-2						)						)
      return mask.unsqueeze(-3						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      UpperCamelCase__			:   List[str]       				=  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						)

      UpperCamelCase__			:   Any       				=  self.conditioning_emb(SCREAMING_SNAKE_CASE						).unsqueeze(1						)

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

      UpperCamelCase__			:   Optional[int]       				=  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.
      UpperCamelCase__			:   Optional[int]       				=  torch.broadcast_to(
          torch.arange(SCREAMING_SNAKE_CASE							,   device=decoder_input_tokens.device						)							,   (batch, seq_length)							,   )

      UpperCamelCase__			:   Dict       				=  self.position_encoding(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  self.continuous_inputs_projection(SCREAMING_SNAKE_CASE						)
      inputs += position_encodings
      UpperCamelCase__			:   Optional[Any]       				=  self.dropout(SCREAMING_SNAKE_CASE						)

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

      # Translate encoding masks to encoder-decoder masks.
      UpperCamelCase__			:   Optional[int]       				=  [(x, self.encoder_decoder_mask(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)) for x, y in encodings_and_masks]

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

      for lyr in self.decoders:
           UpperCamelCase__			:   int       				=  lyr(
               SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   encoder_attention_mask=SCREAMING_SNAKE_CASE							,   )[0]

      UpperCamelCase__			:   Tuple       				=  self.decoder_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  self.post_dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.spec_out(SCREAMING_SNAKE_CASE						)
      return spec_out







class 			__a  (    nn.Module       ):







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.ModuleList()

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

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

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







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  self.layer[0](
          SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      if encoder_hidden_states is not None:
           UpperCamelCase__			:   int       				=  torch.where(encoder_attention_mask > 0							,   0							,   -1e1_0						).to(
               encoder_hidden_states.dtype						)

           UpperCamelCase__			:   Tuple       				=  self.layer[1](
               SCREAMING_SNAKE_CASE							,   key_value_states=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      # Apply Film Conditional Feed Forward layer
      UpperCamelCase__			:   Any       				=  self.layer[-1](SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      return (hidden_states,)







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  Attention(query_dim=SCREAMING_SNAKE_CASE							,   heads=SCREAMING_SNAKE_CASE							,   dim_head=SCREAMING_SNAKE_CASE							,   out_bias=SCREAMING_SNAKE_CASE							,   scale_qk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   List[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)

      if conditioning_emb is not None:
           UpperCamelCase__			:   List[Any]       				=  self.FiLMLayer(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      # Self-attention block
      UpperCamelCase__			:   Optional[Any]       				=  self.attention(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)

      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   str       				=  Attention(query_dim=SCREAMING_SNAKE_CASE							,   heads=SCREAMING_SNAKE_CASE							,   dim_head=SCREAMING_SNAKE_CASE							,   out_bias=SCREAMING_SNAKE_CASE							,   scale_qk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  self.attention(
          SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   attention_mask=attention_mask.squeeze(1						)							,   )
      UpperCamelCase__			:   Optional[Any]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return layer_output







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Any       				=  TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict=None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      if conditioning_emb is not None:
           UpperCamelCase__			:   Optional[int]       				=  self.film(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[int]       				=  self.DenseReluDense(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Tuple       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  NewGELUActivation()







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.act(self.wi_a(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Dict       				=  self.wi_a(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  hidden_gelu * hidden_linear
      UpperCamelCase__			:   int       				=  self.dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.wo(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







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


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Any       				=  eps







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  hidden_states.to(torch.floataa						).pow(2						).mean(-1							,   keepdim=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  hidden_states * torch.rsqrt(variance + self.variance_epsilon						)

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

      return self.weight * hidden_states







class 			__a  (    nn.Module       ):







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   torch.Tensor						):


      '''simple docstring'''






      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.0_4_4_7_1_5 * torch.pow(SCREAMING_SNAKE_CASE							,   3.0						))						))







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   int       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   out_features * 2							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.scale_bias(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  torch.chunk(SCREAMING_SNAKE_CASE							,   2							,   -1						)
      UpperCamelCase__			:   Dict       				=  x * (1 + scale) + shift
      return x