



'''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 	UpperCAmelCase	(		__a					,	__a):


 '''simple docstring'''




 @register_to_config
 def __init__(   self							,  lowerCAmelCase_ = 1_2_8							,  lowerCAmelCase_ = 2_5_6							,  lowerCAmelCase_ = 2_0_0_0.0							,  lowerCAmelCase_ = 7_6_8							,  lowerCAmelCase_ = 1_2							,  lowerCAmelCase_ = 1_2							,  lowerCAmelCase_ = 6_4							,  lowerCAmelCase_ = 2_0_4_8							,  lowerCAmelCase_ = 0.1							,  ) ->      Any:



      """simple docstring"""







      super().__init__()

      a_       		=nn.Sequential(
          nn.Linear(lowerCAmelCase_							,  d_model * 4							,  bias=lowerCAmelCase_)							,  nn.SiLU()							,  nn.Linear(d_model * 4							,  d_model * 4							,  bias=lowerCAmelCase_)							,  nn.SiLU()							,  )

      a_       		=nn.Embedding(lowerCAmelCase_							,  lowerCAmelCase_)
      a_       		=False

      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)

      a_       		=nn.Dropout(p=lowerCAmelCase_)

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

      a_       		=TaLayerNorm(lowerCAmelCase_)

      a_       		=nn.Dropout(p=lowerCAmelCase_)
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=torch.mul(query_input.unsqueeze(-1)							,  key_input.unsqueeze(-2))
      return mask.unsqueeze(-3)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      a_		,						a_		,						a_       		=decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      a_       		=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)

      a_       		=self.conditioning_emb(lowerCAmelCase_).unsqueeze(1)

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

      a_       		=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.
      a_       		=torch.broadcast_to(
          torch.arange(lowerCAmelCase_							,  device=decoder_input_tokens.device)							,  (batch, seq_length)							,  )

      a_       		=self.position_encoding(lowerCAmelCase_)

      a_       		=self.continuous_inputs_projection(lowerCAmelCase_)
      inputs += position_encodings
      a_       		=self.dropout(lowerCAmelCase_)

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

      # Translate encoding masks to encoder-decoder masks.
      a_       		=[(x, self.encoder_decoder_mask(lowerCAmelCase_							,  lowerCAmelCase_)) for x, y in encodings_and_masks]

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

      for lyr in self.decoders:
           a_       		=lyr(
               lowerCAmelCase_							,  conditioning_emb=lowerCAmelCase_							,  encoder_hidden_states=lowerCAmelCase_							,  encoder_attention_mask=lowerCAmelCase_							,  )[0]

      a_       		=self.decoder_norm(lowerCAmelCase_)
      a_       		=self.post_dropout(lowerCAmelCase_)

      a_       		=self.spec_out(lowerCAmelCase_)
      return spec_out







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_=1e-6) ->      Any:



      """simple docstring"""







      super().__init__()
      a_       		=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       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  ) ->      List[str]:



      """simple docstring"""







      a_       		=self.layer[0](
          lowerCAmelCase_							,  conditioning_emb=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  )

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

           a_       		=self.layer[1](
               lowerCAmelCase_							,  key_value_states=lowerCAmelCase_							,  attention_mask=lowerCAmelCase_							,  )

      # Apply Film Conditional Feed Forward layer
      a_       		=self.layer[-1](lowerCAmelCase_							,  lowerCAmelCase_)

      return (hidden_states,)







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      super().__init__()
      a_       		=TaLayerNorm(lowerCAmelCase_)
      a_       		=TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase_)
      a_       		=Attention(query_dim=lowerCAmelCase_							,  heads=lowerCAmelCase_							,  dim_head=lowerCAmelCase_							,  out_bias=lowerCAmelCase_							,  scale_qk=lowerCAmelCase_)
      a_       		=nn.Dropout(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  ) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.layer_norm(lowerCAmelCase_)

      if conditioning_emb is not None:
           a_       		=self.FiLMLayer(lowerCAmelCase_							,  lowerCAmelCase_)

      # Self-attention block
      a_       		=self.attention(lowerCAmelCase_)

      a_       		=hidden_states + self.dropout(lowerCAmelCase_)

      return hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




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



      """simple docstring"""







      super().__init__()
      a_       		=Attention(query_dim=lowerCAmelCase_							,  heads=lowerCAmelCase_							,  dim_head=lowerCAmelCase_							,  out_bias=lowerCAmelCase_							,  scale_qk=lowerCAmelCase_)
      a_       		=TaLayerNorm(lowerCAmelCase_							,  eps=lowerCAmelCase_)
      a_       		=nn.Dropout(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  ) ->      str:



      """simple docstring"""







      a_       		=self.layer_norm(lowerCAmelCase_)
      a_       		=self.attention(
          lowerCAmelCase_							,  encoder_hidden_states=lowerCAmelCase_							,  attention_mask=attention_mask.squeeze(1)							,  )
      a_       		=hidden_states + self.dropout(lowerCAmelCase_)
      return layer_output







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




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



      """simple docstring"""







      super().__init__()
      a_       		=TaDenseGatedActDense(d_model=lowerCAmelCase_							,  d_ff=lowerCAmelCase_							,  dropout_rate=lowerCAmelCase_)
      a_       		=TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase_)
      a_       		=TaLayerNorm(lowerCAmelCase_							,  eps=lowerCAmelCase_)
      a_       		=nn.Dropout(lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_=None) ->      int:



      """simple docstring"""







      a_       		=self.layer_norm(lowerCAmelCase_)
      if conditioning_emb is not None:
           a_       		=self.film(lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=self.DenseReluDense(lowerCAmelCase_)
      a_       		=hidden_states + self.dropout(lowerCAmelCase_)
      return hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




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



      """simple docstring"""







      super().__init__()
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)
      a_       		=nn.Linear(lowerCAmelCase_							,  lowerCAmelCase_							,  bias=lowerCAmelCase_)
      a_       		=nn.Dropout(lowerCAmelCase_)
      a_       		=NewGELUActivation()




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		=self.act(self.wi_a(lowerCAmelCase_))
      a_       		=self.wi_a(lowerCAmelCase_)
      a_       		=hidden_gelu * hidden_linear
      a_       		=self.dropout(lowerCAmelCase_)

      a_       		=self.wo(lowerCAmelCase_)
      return hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_=1e-6) ->      List[str]:



      """simple docstring"""







      super().__init__()
      a_       		=nn.Parameter(torch.ones(lowerCAmelCase_))
      a_       		=eps




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      a_       		=hidden_states.to(torch.floataa).pow(2).mean(-1							,  keepdim=lowerCAmelCase_)
      a_       		=hidden_states * torch.rsqrt(variance + self.variance_epsilon)

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

      return self.weight * hidden_states







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      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(lowerCAmelCase_							,  3.0))))







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




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



      """simple docstring"""







      super().__init__()
      a_       		=nn.Linear(lowerCAmelCase_							,  out_features * 2							,  bias=lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      a_       		=self.scale_bias(lowerCAmelCase_)
      a_		,						a_       		=torch.chunk(lowerCAmelCase_							,  2							,  -1)
      a_       		=x * (1 + scale) + shift
      return x







'''simple docstring'''


import os
from math import logaa






def  UpperCAmelCase_       (		lowercase__ = "base_exp.txt"       ):


     '''simple docstring'''







     a_       		=0
     a_       		=0
     for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__       )       ,	lowercase__       )       )       ):
          a_		,						a_       		=list(map(lowercase__       ,	line.split(","       )       )       )
          if x * logaa(lowercase__       ) > largest:
               a_       		=x * logaa(lowercase__       )
               a_       		=i + 1
     return result


if __name__ == "__main__":
  print(solution())


