"""simple docstring"""






from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings


@maybe_allow_in_graph
class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = "geglu"    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = "layer_norm"    ,			_UpperCAmelCase = False    ,			):
       super().__init__()
       lowercase__:   Optional[int]					=      only_cross_attention

       lowercase__:   str					=      (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
       lowercase__:   Optional[Any]					=      (num_embeds_ada_norm is not None) and norm_type == "ada_norm"

       if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
          raise ValueError(
              F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
              F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""							)

       # Define 3 blocks. Each block has its own normalization layer.
       # 1. Self-Attn
       if self.use_ada_layer_norm:
          lowercase__:   List[Any]					=      AdaLayerNorm(_UpperCAmelCase    ,			_UpperCAmelCase							)
       elif self.use_ada_layer_norm_zero:
          lowercase__:   Dict					=      AdaLayerNormZero(_UpperCAmelCase    ,			_UpperCAmelCase							)
       else:
          lowercase__:   str					=      nn.LayerNorm(_UpperCAmelCase    ,			elementwise_affine=_UpperCAmelCase							)
       lowercase__:   List[Any]					=      Attention(
           query_dim=_UpperCAmelCase    ,			heads=_UpperCAmelCase    ,			dim_head=_UpperCAmelCase    ,			dropout=_UpperCAmelCase    ,			bias=_UpperCAmelCase    ,			cross_attention_dim=cross_attention_dim if only_cross_attention else None    ,			upcast_attention=_UpperCAmelCase    ,			)

       # 2. Cross-Attn
       if cross_attention_dim is not None or double_self_attention:
          # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
          # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
          # the second cross attention block.
          lowercase__:   Optional[int]					=      (
              AdaLayerNorm(_UpperCAmelCase    ,			_UpperCAmelCase							)
              if self.use_ada_layer_norm
              else nn.LayerNorm(_UpperCAmelCase    ,			elementwise_affine=_UpperCAmelCase							)
          )
          lowercase__:   int					=      Attention(
              query_dim=_UpperCAmelCase    ,			cross_attention_dim=cross_attention_dim if not double_self_attention else None    ,			heads=_UpperCAmelCase    ,			dim_head=_UpperCAmelCase    ,			dropout=_UpperCAmelCase    ,			bias=_UpperCAmelCase    ,			upcast_attention=_UpperCAmelCase    ,			)  # is self-attn if encoder_hidden_states is none
       else:
          lowercase__:   Union[str, Any]					=      None
          lowercase__:   str					=      None

       # 3. Feed-forward
       lowercase__:   List[Any]					=      nn.LayerNorm(_UpperCAmelCase    ,			elementwise_affine=_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      FeedForward(_UpperCAmelCase    ,			dropout=_UpperCAmelCase    ,			activation_fn=_UpperCAmelCase    ,			final_dropout=_UpperCAmelCase							)

       # let chunk size default to None
       lowercase__:   List[str]					=      None
       lowercase__:   Optional[Any]					=      0






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   int					=      chunk_size
       lowercase__:   List[str]					=      dim






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):






       if self.use_ada_layer_norm:
          lowercase__:   str					=      self.norma(_UpperCAmelCase    ,			_UpperCAmelCase							)
       elif self.use_ada_layer_norm_zero:
          lowercase__:   Any					=      self.norma(
              _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			hidden_dtype=hidden_states.dtype							)
       else:
          lowercase__:   List[Any]					=      self.norma(_UpperCAmelCase							)

       lowercase__:   int					=      cross_attention_kwargs if cross_attention_kwargs is not None else {}

       lowercase__:   int					=      self.attna(
           _UpperCAmelCase    ,			encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None    ,			attention_mask=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)
       if self.use_ada_layer_norm_zero:
          lowercase__:   int					=      gate_msa.unsqueeze(1							) * attn_output
       lowercase__:   Dict					=      attn_output + hidden_states

       # 2. Cross-Attention
       if self.attna is not None:
          lowercase__:   Optional[int]					=      (
              self.norma(_UpperCAmelCase    ,			_UpperCAmelCase							) if self.use_ada_layer_norm else self.norma(_UpperCAmelCase							)
          )

          lowercase__:   Optional[Any]					=      self.attna(
              _UpperCAmelCase    ,			encoder_hidden_states=_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)
          lowercase__:   Union[str, Any]					=      attn_output + hidden_states

       # 3. Feed-forward
       lowercase__:   Dict					=      self.norma(_UpperCAmelCase							)

       if self.use_ada_layer_norm_zero:
          lowercase__:   Optional[Any]					=      norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

       if self._chunk_size is not None:
          # "feed_forward_chunk_size" can be used to save memory
          if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
             raise ValueError(
                 F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""							)

          lowercase__:   Union[str, Any]					=      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
          lowercase__:   List[str]					=      torch.cat(
              [self.ff(_UpperCAmelCase							) for hid_slice in norm_hidden_states.chunk(_UpperCAmelCase    ,			dim=self._chunk_dim							)]    ,			dim=self._chunk_dim    ,			)
       else:
          lowercase__:   Union[str, Any]					=      self.ff(_UpperCAmelCase							)

       if self.use_ada_layer_norm_zero:
          lowercase__:   List[str]					=      gate_mlp.unsqueeze(1							) * ff_output

       lowercase__:   str					=      ff_output + hidden_states

       return hidden_states



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = 4    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = "geglu"    ,			_UpperCAmelCase = False    ,			):
       super().__init__()
       lowercase__:   Tuple					=      int(dim * mult							)
       lowercase__:   Dict					=      dim_out if dim_out is not None else dim

       if activation_fn == "gelu":
          lowercase__:   List[str]					=      GELU(_UpperCAmelCase    ,			_UpperCAmelCase							)
       if activation_fn == "gelu-approximate":
          lowercase__:   Dict					=      GELU(_UpperCAmelCase    ,			_UpperCAmelCase    ,			approximate='''tanh'''							)
       elif activation_fn == "geglu":
          lowercase__:   str					=      GEGLU(_UpperCAmelCase    ,			_UpperCAmelCase							)
       elif activation_fn == "geglu-approximate":
          lowercase__:   str					=      ApproximateGELU(_UpperCAmelCase    ,			_UpperCAmelCase							)

       lowercase__:   Any					=      nn.ModuleList([]							)
       # project in
       self.net.append(_UpperCAmelCase							)
       # project dropout
       self.net.append(nn.Dropout(_UpperCAmelCase							)							)
       # project out
       self.net.append(nn.Linear(_UpperCAmelCase    ,			_UpperCAmelCase							)							)
       # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
       if final_dropout:
          self.net.append(nn.Dropout(_UpperCAmelCase							)							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):






       for module in self.net:
          lowercase__:   Optional[Any]					=      module(_UpperCAmelCase							)
       return hidden_states



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = "none"							):
       super().__init__()
       lowercase__:   Optional[int]					=      nn.Linear(_UpperCAmelCase    ,			_UpperCAmelCase							)
       lowercase__:   Any					=      approximate






    def _snake_case			(   self    ,			_UpperCAmelCase							):






       if gate.device.type != "mps":
          return F.gelu(_UpperCAmelCase    ,			approximate=self.approximate							)
       # mps: gelu is not implemented for float16
       return F.gelu(gate.to(dtype=torch.floataa							)    ,			approximate=self.approximate							).to(dtype=gate.dtype							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Tuple					=      self.proj(_UpperCAmelCase							)
       lowercase__:   Optional[int]					=      self.gelu(_UpperCAmelCase							)
       return hidden_states



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   int					=      nn.Linear(_UpperCAmelCase    ,			dim_out * 2							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):






       if gate.device.type != "mps":
          return F.gelu(_UpperCAmelCase							)
       # mps: gelu is not implemented for float16
       return F.gelu(gate.to(dtype=torch.floataa							)							).to(dtype=gate.dtype							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      self.proj(_UpperCAmelCase							).chunk(2    ,			dim=-1							)
       return hidden_states * self.gelu(_UpperCAmelCase							)



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   int					=      nn.Linear(_UpperCAmelCase    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Tuple					=      self.proj(_UpperCAmelCase							)
       return x * torch.sigmoid(1.702 * x							)



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   Optional[int]					=      nn.Embedding(_UpperCAmelCase    ,			_UpperCAmelCase							)
       lowercase__:   Any					=      nn.SiLU()
       lowercase__:   Optional[Any]					=      nn.Linear(_UpperCAmelCase    ,			embedding_dim * 2							)
       lowercase__:   Union[str, Any]					=      nn.LayerNorm(_UpperCAmelCase    ,			elementwise_affine=_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      self.linear(self.silu(self.emb(_UpperCAmelCase							)							)							)
       lowercase__:   Optional[Any]					=      torch.chunk(_UpperCAmelCase    ,			2							)
       lowercase__:   Optional[Any]					=      self.norm(_UpperCAmelCase							) * (1 + scale) + shift
       return x



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()

       lowercase__:   Tuple					=      CombinedTimestepLabelEmbeddings(_UpperCAmelCase    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      nn.SiLU()
       lowercase__:   str					=      nn.Linear(_UpperCAmelCase    ,			6 * embedding_dim    ,			bias=_UpperCAmelCase							)
       lowercase__:   List[Any]					=      nn.LayerNorm(_UpperCAmelCase    ,			elementwise_affine=_UpperCAmelCase    ,			eps=1e-6							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   List[str]					=      self.linear(self.silu(self.emb(_UpperCAmelCase    ,			_UpperCAmelCase    ,			hidden_dtype=_UpperCAmelCase							)							)							)
       lowercase__:   str					=      emb.chunk(6    ,			dim=1							)
       lowercase__:   str					=      self.norm(_UpperCAmelCase							) * (1 + scale_msa[:, None]) + shift_msa[:, None]
       return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = 1e-5							):
       super().__init__()
       lowercase__:   Optional[Any]					=      num_groups
       lowercase__:   Tuple					=      eps

       if act_fn is None:
          lowercase__:   Optional[Any]					=      None
       else:
          lowercase__:   Optional[int]					=      get_activation(_UpperCAmelCase							)

       lowercase__:   Optional[Any]					=      nn.Linear(_UpperCAmelCase    ,			out_dim * 2							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):






       if self.act:
          lowercase__:   Optional[int]					=      self.act(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      self.linear(_UpperCAmelCase							)
       lowercase__:   Any					=      emb[:, :, None, None]
       lowercase__:   Optional[int]					=      emb.chunk(2    ,			dim=1							)

       lowercase__:   Optional[Any]					=      F.group_norm(_UpperCAmelCase    ,			self.num_groups    ,			eps=self.eps							)
       lowercase__:   Union[str, Any]					=      x * (1 + scale) + shift
       return x








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