





'''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 lowerCamelCase						(a__						,      a__					):







  @register_to_config
  def __init__(		self     ,     lowercase__ = 128     ,     lowercase__ = 256     ,     lowercase__ = 2_000.0     ,     lowercase__ = 768     ,     lowercase__ = 12     ,     lowercase__ = 12     ,     lowercase__ = 64     ,     lowercase__ = 2_048     ,     lowercase__ = 0.1     ,     )		->						Optional[int]:






        """simple docstring"""





        super().__init__()

        _snake_case		:	str					     =     nn.Sequential(
            nn.Linear(lowercase__     ,     d_model * 4     ,     bias=lowercase__		)     ,     nn.SiLU()     ,     nn.Linear(d_model * 4     ,     d_model * 4     ,     bias=lowercase__		)     ,     nn.SiLU()     ,     )

        _snake_case		:	List[str]					     =     nn.Embedding(lowercase__     ,     lowercase__		)
        _snake_case		:	Optional[int]					     =     False

        _snake_case		:	str					     =     nn.Linear(lowercase__     ,     lowercase__     ,     bias=lowercase__		)

        _snake_case		:	Union[str, Any]					     =     nn.Dropout(p=lowercase__		)

        _snake_case		:	List[Any]					     =     nn.ModuleList()
        for lyr_num in range(lowercase__		):
              # FiLM conditional T5 decoder
              _snake_case		:	List[str]					     =     DecoderLayer(d_model=lowercase__     ,     d_kv=lowercase__     ,     num_heads=lowercase__     ,     d_ff=lowercase__     ,     dropout_rate=lowercase__		)
              self.decoders.append(lowercase__		)

        _snake_case		:	int					     =     TaLayerNorm(lowercase__		)

        _snake_case		:	int					     =     nn.Dropout(p=lowercase__		)
        _snake_case		:	List[str]					     =     nn.Linear(lowercase__     ,     lowercase__     ,     bias=lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case     ,							_snake_case     ,							_snake_case		:	str					     =     decoder_input_tokens.shape
        assert decoder_noise_time.shape == (batch,)

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

        _snake_case		:	Tuple					     =     self.conditioning_emb(lowercase__		).unsqueeze(1		)

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

        _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.
        _snake_case		:	Any					     =     torch.broadcast_to(
            torch.arange(lowercase__     ,     device=decoder_input_tokens.device		)     ,     (batch, seq_length)     ,     )

        _snake_case		:	int					     =     self.position_encoding(lowercase__		)

        _snake_case		:	List[Any]					     =     self.continuous_inputs_projection(lowercase__		)
        inputs += position_encodings
        _snake_case		:	int					     =     self.dropout(lowercase__		)

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

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

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

        for lyr in self.decoders:
              _snake_case		:	List[Any]					     =     lyr(
                  lowercase__     ,     conditioning_emb=lowercase__     ,     encoder_hidden_states=lowercase__     ,     encoder_attention_mask=lowercase__     ,     )[0]

        _snake_case		:	Any					     =     self.decoder_norm(lowercase__		)
        _snake_case		:	int					     =     self.post_dropout(lowercase__		)

        _snake_case		:	Tuple					     =     self.spec_out(lowercase__		)
        return spec_out





class lowerCamelCase						(nn.Module					):







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__=1E-6		)		->						Optional[Any]:






        """simple docstring"""





        super().__init__()
        _snake_case		:	Dict					     =     nn.ModuleList()

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

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

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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     )		->						List[str]:






        """simple docstring"""





        _snake_case		:	str					     =     self.layer[0](
            lowercase__     ,     conditioning_emb=lowercase__     ,     attention_mask=lowercase__     ,     )

        if encoder_hidden_states is not None:
              _snake_case		:	List[str]					     =     torch.where(encoder_attention_mask > 0     ,     0     ,     -1E1_0		).to(
                  encoder_hidden_states.dtype		)

              _snake_case		:	Any					     =     self.layer[1](
                  lowercase__     ,     key_value_states=lowercase__     ,     attention_mask=lowercase__     ,     )

        # Apply Film Conditional Feed Forward layer
        _snake_case		:	int					     =     self.layer[-1](lowercase__     ,     lowercase__		)

        return (hidden_states,)





class lowerCamelCase						(nn.Module					):







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






        """simple docstring"""





        super().__init__()
        _snake_case		:	int					     =     TaLayerNorm(lowercase__		)
        _snake_case		:	Optional[int]					     =     TaFiLMLayer(in_features=d_model * 4     ,     out_features=lowercase__		)
        _snake_case		:	Dict					     =     Attention(query_dim=lowercase__     ,     heads=lowercase__     ,     dim_head=lowercase__     ,     out_bias=lowercase__     ,     scale_qk=lowercase__		)
        _snake_case		:	List[Any]					     =     nn.Dropout(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=None     ,     lowercase__=None     ,     )		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     self.layer_norm(lowercase__		)

        if conditioning_emb is not None:
              _snake_case		:	str					     =     self.FiLMLayer(lowercase__     ,     lowercase__		)

        # Self-attention block
        _snake_case		:	Tuple					     =     self.attention(lowercase__		)

        _snake_case		:	Optional[Any]					     =     hidden_states + self.dropout(lowercase__		)

        return hidden_states





class lowerCamelCase						(nn.Module					):







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        super().__init__()
        _snake_case		:	Optional[Any]					     =     Attention(query_dim=lowercase__     ,     heads=lowercase__     ,     dim_head=lowercase__     ,     out_bias=lowercase__     ,     scale_qk=lowercase__		)
        _snake_case		:	Optional[Any]					     =     TaLayerNorm(lowercase__     ,     eps=lowercase__		)
        _snake_case		:	Any					     =     nn.Dropout(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=None     ,     lowercase__=None     ,     )		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.layer_norm(lowercase__		)
        _snake_case		:	int					     =     self.attention(
            lowercase__     ,     encoder_hidden_states=lowercase__     ,     attention_mask=attention_mask.squeeze(1		)     ,     )
        _snake_case		:	str					     =     hidden_states + self.dropout(lowercase__		)
        return layer_output





class lowerCamelCase						(nn.Module					):







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=None		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.layer_norm(lowercase__		)
        if conditioning_emb is not None:
              _snake_case		:	Any					     =     self.film(lowercase__     ,     lowercase__		)

        _snake_case		:	Tuple					     =     self.DenseReluDense(lowercase__		)
        _snake_case		:	Any					     =     hidden_states + self.dropout(lowercase__		)
        return hidden_states





class lowerCamelCase						(nn.Module					):







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






        """simple docstring"""





        super().__init__()
        _snake_case		:	int					     =     nn.Linear(lowercase__     ,     lowercase__     ,     bias=lowercase__		)
        _snake_case		:	Tuple					     =     nn.Linear(lowercase__     ,     lowercase__     ,     bias=lowercase__		)
        _snake_case		:	Any					     =     nn.Linear(lowercase__     ,     lowercase__     ,     bias=lowercase__		)
        _snake_case		:	str					     =     nn.Dropout(lowercase__		)
        _snake_case		:	Dict					     =     NewGELUActivation()







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.act(self.wi_a(lowercase__		)		)
        _snake_case		:	Any					     =     self.wi_a(lowercase__		)
        _snake_case		:	Tuple					     =     hidden_gelu * hidden_linear
        _snake_case		:	Dict					     =     self.dropout(lowercase__		)

        _snake_case		:	Dict					     =     self.wo(lowercase__		)
        return hidden_states





class lowerCamelCase						(nn.Module					):







  def __init__(		self     ,     lowercase__     ,     lowercase__=1E-6		)		->						Any:






        """simple docstring"""





        super().__init__()
        _snake_case		:	List[Any]					     =     nn.Parameter(torch.ones(lowercase__		)		)
        _snake_case		:	Optional[Any]					     =     eps







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     hidden_states.to(torch.floataa		).pow(2		).mean(-1     ,     keepdim=lowercase__		)
        _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]:
              _snake_case		:	str					     =     hidden_states.to(self.weight.dtype		)

        return self.weight * hidden_states





class lowerCamelCase						(nn.Module					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						torch.Tensor:






        """simple docstring"""





        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044_715 * torch.pow(lowercase__     ,     3.0		))		))





class lowerCamelCase						(nn.Module					):







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						Any:






        """simple docstring"""





        super().__init__()
        _snake_case		:	List[Any]					     =     nn.Linear(lowercase__     ,     out_features * 2     ,     bias=lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.scale_bias(lowercase__		)
        _snake_case     ,							_snake_case		:	Optional[Any]					     =     torch.chunk(lowercase__     ,     2     ,     -1		)
        _snake_case		:	Dict					     =     x * (1 + scale) + shift
        return x









'''simple docstring'''

from __future__ import annotations




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = None	):





      """simple docstring"""



      if start is None:
            _snake_case		:	Optional[Any]					     =     0

      if end is None:
            _snake_case		:	Any					     =     len(lowerCAmelCase_	) - 1

      if start >= end:
            return

      _snake_case		:	Optional[Any]					     =     (start + end) // 2

      slowsort(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      slowsort(lowerCAmelCase_			,      mid + 1			,      lowerCAmelCase_	)

      if sequence[end] < sequence[mid]:
            _snake_case     ,							_snake_case		:	int					     =     sequence[mid], sequence[end]

      slowsort(lowerCAmelCase_			,      lowerCAmelCase_			,      end - 1	)


if __name__ == "__main__":
   from doctest import testmod

   testmod()


