def      _a					(   lowercase__	:      list  ,	lowercase__	:      int = 0			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		length or len(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		False
     for i in range(length - 1			):
          if list_data[i] > list_data[i + 1]:
               SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		list_data[i + 1], list_data[i]
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		True

     return list_data if not swapped else bubble_sort(lowercase__  ,	length - 1			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




'''simple docstring'''

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 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase ):
      '''simple docstring'''


      @register_to_config
      def __init__(					self       ,lowerCamelCase_ = 128       ,lowerCamelCase_ = 256       ,lowerCamelCase_ = 2000.0       ,lowerCamelCase_ = 768       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 64       ,lowerCamelCase_ = 2048       ,lowerCamelCase_ = 0.1       ,)      ->      List[Any]:



         '''simple docstring'''



         super().__init__()

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

         UpperCAmelCase__				:					List[str] 					=	nn.Embedding(lowerCamelCase_       ,lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	False

         UpperCAmelCase__				:					List[str] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(p=lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	nn.ModuleList()
         for lyr_num in range(lowerCamelCase_				):
            # FiLM conditional T5 decoder
            UpperCAmelCase__				:					str 					=	DecoderLayer(d_model=lowerCamelCase_       ,d_kv=lowerCamelCase_       ,num_heads=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)
            self.decoders.append(lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	TaLayerNorm(lowerCamelCase_				)

         UpperCAmelCase__				:					Any 					=	nn.Dropout(p=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         UpperCAmelCase__				:					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__				:					List[Any] 					=	self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

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

         UpperCAmelCase__				:					List[Any] 					=	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__				:					int 					=	torch.broadcast_to(
             torch.arange(lowerCamelCase_       ,device=decoder_input_tokens.device				)       ,(batch, seq_length)       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	self.position_encoding(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         UpperCAmelCase__				:					str 					=	self.dropout(lowerCamelCase_				)

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

         # Translate encoding masks to encoder-decoder masks.
         UpperCAmelCase__				:					Dict 					=	[(x, self.encoder_decoder_mask(lowerCamelCase_       ,lowerCamelCase_				)) for x, y in encodings_and_masks]

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

         for lyr in self.decoders:
            UpperCAmelCase__				:					Optional[Any] 					=	lyr(
                lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,encoder_attention_mask=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					List[Any] 					=	self.decoder_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	self.post_dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.spec_out(lowerCamelCase_				)
         return spec_out




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					int 					=	nn.ModuleList()

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

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

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[0](
             lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         if encoder_hidden_states is not None:
            UpperCAmelCase__				:					Any 					=	torch.where(encoder_attention_mask > 0       ,0       ,-1e10				).to(
                encoder_hidden_states.dtype				)

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

         # Apply Film Conditional Feed Forward layer
         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[-1](lowerCamelCase_       ,lowerCamelCase_				)

         return (hidden_states,)




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	TaLayerNorm(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
            UpperCAmelCase__				:					Any 					=	self.FiLMLayer(lowerCamelCase_       ,lowerCamelCase_				)

         # Self-attention block
         UpperCAmelCase__				:					List[Any] 					=	self.attention(lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.layer_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	self.attention(
             lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,attention_mask=attention_mask.squeeze(1				)       ,)
         UpperCAmelCase__				:					int 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	TaDenseGatedActDense(d_model=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
            UpperCAmelCase__				:					Optional[Any] 					=	self.film(lowerCamelCase_       ,lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	self.DenseReluDense(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Dropout(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	NewGELUActivation()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.act(self.wi_a(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[str] 					=	self.wi_a(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	hidden_gelu * hidden_linear
         UpperCAmelCase__				:					int 					=	self.dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.wo(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Tuple 					=	nn.Parameter(torch.ones(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Dict 					=	eps




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	hidden_states.to(torch.floataa				).pow(2				).mean(-1       ,keepdim=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

         return self.weight * hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      torch.Tensor:



         '''simple docstring'''



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




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,out_features * 2       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scale_bias(lowerCamelCase_				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	torch.chunk(lowerCamelCase_       ,2       ,-1				)
         UpperCAmelCase__				:					List[Any] 					=	x * (1 + scale) + shift
         return x




