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 :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int = 1_28     ,							__SCREAMING_SNAKE_CASE :      int = 2_56     ,							__SCREAMING_SNAKE_CASE :      float = 20_00.0     ,							__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     ,							):
   super().__init__()

   __a       =  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()     ,							)

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Linear(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							bias=__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    # FiLM conditional T5 decoder
    __a       =  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       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Linear(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							bias=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Tuple       ):
   __a       =  torch.mul(query_input.unsqueeze(-1       )     ,							key_input.unsqueeze(-2       )       )
   return mask.unsqueeze(-3       )

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __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(__SCREAMING_SNAKE_CASE       ).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(__SCREAMING_SNAKE_CASE     ,							device=decoder_input_tokens.device       )     ,							(batch, seq_length)     ,							)

   __a       =  self.position_encoding(__SCREAMING_SNAKE_CASE       )

   __a       =  self.continuous_inputs_projection(__SCREAMING_SNAKE_CASE       )
   inputs += position_encodings
   __a       =  self.dropout(__SCREAMING_SNAKE_CASE       )

   # 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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )) 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(
        __SCREAMING_SNAKE_CASE     ,							conditioning_emb=__SCREAMING_SNAKE_CASE     ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,							encoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							)[0]

   __a       =  self.decoder_norm(__SCREAMING_SNAKE_CASE       )
   __a       =  self.post_dropout(__SCREAMING_SNAKE_CASE       )

   __a       =  self.spec_out(__SCREAMING_SNAKE_CASE       )
   return spec_out
class    A_ (       nn.Module ):

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple=1E-6       ):
   super().__init__()
   __a       =  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 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							):
   __a       =  self.layer[0](
       __SCREAMING_SNAKE_CASE     ,							conditioning_emb=__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE     ,							)

   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](
        __SCREAMING_SNAKE_CASE     ,							key_value_states=__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE     ,							)

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

   return (hidden_states,)
class    A_ (       nn.Module ):

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   super().__init__()
   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  TaFiLMLayer(in_features=d_model * 4     ,							out_features=__SCREAMING_SNAKE_CASE       )
   __a       =  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       )
   __a       =  nn.Dropout(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=None     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							):
   # pre_self_attention_layer_norm
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   if conditioning_emb is not None:
    __a       =  self.FiLMLayer(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   # Self-attention block
   __a       =  self.attention(__SCREAMING_SNAKE_CASE       )

   __a       =  hidden_states + self.dropout(__SCREAMING_SNAKE_CASE       )

   return hidden_states
class    A_ (       nn.Module ):

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   super().__init__()
   __a       =  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       )
   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE     ,							eps=__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      int=None     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							):
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )
   __a       =  self.attention(
       __SCREAMING_SNAKE_CASE     ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,							attention_mask=attention_mask.squeeze(1       )     ,							)
   __a       =  hidden_states + self.dropout(__SCREAMING_SNAKE_CASE       )
   return layer_output
class    A_ (       nn.Module ):

  def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   super().__init__()
   __a       =  TaDenseGatedActDense(d_model=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE       )
   __a       =  TaFiLMLayer(in_features=d_model * 4     ,							out_features=__SCREAMING_SNAKE_CASE       )
   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE     ,							eps=__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None       ):
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )
   if conditioning_emb is not None:
    __a       =  self.film(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  self.DenseReluDense(__SCREAMING_SNAKE_CASE       )
   __a       =  hidden_states + self.dropout(__SCREAMING_SNAKE_CASE       )
   return hidden_states
class    A_ (       nn.Module ):

  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   super().__init__()
   __a       =  nn.Linear(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							bias=__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Linear(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							bias=__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Linear(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							bias=__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(__SCREAMING_SNAKE_CASE       )
   __a       =  NewGELUActivation()

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  self.act(self.wi_a(__SCREAMING_SNAKE_CASE       )       )
   __a       =  self.wi_a(__SCREAMING_SNAKE_CASE       )
   __a       =  hidden_gelu * hidden_linear
   __a       =  self.dropout(__SCREAMING_SNAKE_CASE       )

   __a       =  self.wo(__SCREAMING_SNAKE_CASE       )
   return hidden_states
class    A_ (       nn.Module ):

  def __init__(		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-6       ):
   super().__init__()
   __a       =  nn.Parameter(torch.ones(__SCREAMING_SNAKE_CASE       )       )
   __a       =  eps

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
   # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
   # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
   # half-precision inputs is done in fp32

   __a       =  hidden_states.to(torch.floataa       ).pow(2       ).mean(-1     ,							keepdim=__SCREAMING_SNAKE_CASE       )
   __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    A_ (       nn.Module ):

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      torch.Tensor       ):
   return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_47_15 * torch.pow(__SCREAMING_SNAKE_CASE     ,							3.0       ))       ))
class    A_ (       nn.Module ):

  def __init__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   super().__init__()
   __a       =  nn.Linear(__SCREAMING_SNAKE_CASE     ,							out_features * 2     ,							bias=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  self.scale_bias(__SCREAMING_SNAKE_CASE       )
   __a       ,     __a       =  torch.chunk(__SCREAMING_SNAKE_CASE     ,							2     ,							-1       )
   __a       =  x * (1 + scale) + shift
   return x



def   __A		(			_A					):



 """simple docstring"""


 __a       =  []
 for data in source_data:
  for i, el in enumerate(_A					):
   if len(_A					) < i + 1:
    data_lists.append([]					)
   data_lists[i].append(float(_A					)					)
 return data_lists



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 for dlist, weight in zip(_A							,				_A					):
  __a       =  min(_A					)
  __a       =  max(_A					)

  __a       =  []
  # for weight 0 score is 1 - actual score
  if weight == 0:
   for item in dlist:
    try:
     score.append(1 - ((item - mind) / (maxd - mind))					)
    except ZeroDivisionError:
     score.append(1					)

  elif weight == 1:
   for item in dlist:
    try:
     score.append((item - mind) / (maxd - mind)					)
    except ZeroDivisionError:
     score.append(0					)

        # weight not 0 or 1
  else:
   __a       =  f"""Invalid weight of {weight:f} provided"""
   raise ValueError(_A					)

  score_lists.append(_A					)

 return score_lists



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [0 for i in range(len(score_lists[0]					)					)]

 for slist in score_lists:
  for j, ele in enumerate(_A					):
   __a       =  final_scores[j] + ele

 return final_scores






def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  get_data(_A					)
 __a       =  calculate_each_score(_A							,				_A					)
 __a       =  generate_final_scores(_A					)

 # append scores to source data
 for i, ele in enumerate(_A					):
  source_data[i].append(_A					)

 return source_data


