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 __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_		):




   @register_to_config
   def __init__(    self			:				List[str]       ,	_lowercase			:				Dict = 1_28       ,	_lowercase			:				List[Any] = 2_56       ,	_lowercase			:				str = 20_00.0       ,	_lowercase			:				List[Any] = 7_68       ,	_lowercase			:				Union[str, Any] = 12       ,	_lowercase			:				Dict = 12       ,	_lowercase			:				Any = 64       ,	_lowercase			:				Union[str, Any] = 20_48       ,	_lowercase			:				List[str] = 0.1       ,	):



     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE__										=      nn.Sequential(
         nn.Linear(_a       ,	d_model * 4       ,	bias=_a      )       ,	nn.SiLU()       ,	nn.Linear(d_model * 4       ,	d_model * 4       ,	bias=_a      )       ,	nn.SiLU()       ,	)

     SCREAMING_SNAKE_CASE__										=      nn.Embedding(_a       ,	_a      )
     SCREAMING_SNAKE_CASE__										=      False

     SCREAMING_SNAKE_CASE__										=      nn.Linear(_a       ,	_a       ,	bias=_a      )

     SCREAMING_SNAKE_CASE__										=      nn.Dropout(p=_a      )

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

     SCREAMING_SNAKE_CASE__										=      TaLayerNorm(_a      )

     SCREAMING_SNAKE_CASE__										=      nn.Dropout(p=_a      )
     SCREAMING_SNAKE_CASE__										=      nn.Linear(_a       ,	_a       ,	bias=_a      )




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Dict       ,	_lowercase			:				Tuple      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				str       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Tuple      ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__										=      self.conditioning_emb(_a      ).unsqueeze(1      )

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

     SCREAMING_SNAKE_CASE__										=      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.
     SCREAMING_SNAKE_CASE__										=      torch.broadcast_to(
         torch.arange(_a       ,	device=decoder_input_tokens.device      )       ,	(batch, seq_length)       ,	)

     SCREAMING_SNAKE_CASE__										=      self.position_encoding(_a      )

     SCREAMING_SNAKE_CASE__										=      self.continuous_inputs_projection(_a      )
     inputs += position_encodings
     SCREAMING_SNAKE_CASE__										=      self.dropout(_a      )

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

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

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

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

     SCREAMING_SNAKE_CASE__										=      self.decoder_norm(_a      )
     SCREAMING_SNAKE_CASE__										=      self.post_dropout(_a      )

     SCREAMING_SNAKE_CASE__										=      self.spec_out(_a      )
     return spec_out







class __snake_case				(						nn.Module		):




   def __init__(    self			:				Tuple       ,	_lowercase			:				Any       ,	_lowercase			:				List[str]       ,	_lowercase			:				Tuple       ,	_lowercase			:				List[Any]       ,	_lowercase			:				str       ,	_lowercase			:				List[str]=1E-6      ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__										=      nn.ModuleList()

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

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

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




   def 	__a						(    self			:				int       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Union[str, Any]=None       ,	_lowercase			:				Optional[int]=None       ,	_lowercase			:				Dict=None       ,	_lowercase			:				List[Any]=None       ,	_lowercase			:				int=None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.layer[0](
         _a       ,	conditioning_emb=_a       ,	attention_mask=_a       ,	)

     if encoder_hidden_states is not None:
       SCREAMING_SNAKE_CASE__										=      torch.where(encoder_attention_mask > 0       ,	0       ,	-1E10      ).to(
           encoder_hidden_states.dtype      )

       SCREAMING_SNAKE_CASE__										=      self.layer[1](
           _a       ,	key_value_states=_a       ,	attention_mask=_a       ,	)

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

     return (hidden_states,)







class __snake_case				(						nn.Module		):




   def __init__(    self			:				List[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple       ,	_lowercase			:				Dict       ,	_lowercase			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Dict       ,	_lowercase			:				Any       ,	_lowercase			:				Optional[int]=None       ,	_lowercase			:				Optional[int]=None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.layer_norm(_a      )

     if conditioning_emb is not None:
       SCREAMING_SNAKE_CASE__										=      self.FiLMLayer(_a       ,	_a      )

     # Self-attention block
     SCREAMING_SNAKE_CASE__										=      self.attention(_a      )

     SCREAMING_SNAKE_CASE__										=      hidden_states + self.dropout(_a      )

     return hidden_states







class __snake_case				(						nn.Module		):




   def __init__(    self			:				Any       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Tuple       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Dict       ,	_lowercase			:				List[Any]=None       ,	_lowercase			:				Any=None       ,	):



     """simple docstring"""






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







class __snake_case				(						nn.Module		):




   def __init__(    self			:				List[str]       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				str       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				int=None      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.layer_norm(_a      )
     if conditioning_emb is not None:
       SCREAMING_SNAKE_CASE__										=      self.film(_a       ,	_a      )

     SCREAMING_SNAKE_CASE__										=      self.DenseReluDense(_a      )
     SCREAMING_SNAKE_CASE__										=      hidden_states + self.dropout(_a      )
     return hidden_states







class __snake_case				(						nn.Module		):




   def __init__(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Tuple      ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__										=      nn.Linear(_a       ,	_a       ,	bias=_a      )
     SCREAMING_SNAKE_CASE__										=      nn.Linear(_a       ,	_a       ,	bias=_a      )
     SCREAMING_SNAKE_CASE__										=      nn.Linear(_a       ,	_a       ,	bias=_a      )
     SCREAMING_SNAKE_CASE__										=      nn.Dropout(_a      )
     SCREAMING_SNAKE_CASE__										=      NewGELUActivation()




   def 	__a						(    self			:				str       ,	_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.act(self.wi_a(_a      )      )
     SCREAMING_SNAKE_CASE__										=      self.wi_a(_a      )
     SCREAMING_SNAKE_CASE__										=      hidden_gelu * hidden_linear
     SCREAMING_SNAKE_CASE__										=      self.dropout(_a      )

     SCREAMING_SNAKE_CASE__										=      self.wo(_a      )
     return hidden_states







class __snake_case				(						nn.Module		):




   def __init__(    self			:				List[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				List[str]=1E-6      ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__										=      nn.Parameter(torch.ones(_a      )      )
     SCREAMING_SNAKE_CASE__										=      eps




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				Any      ):



     """simple docstring"""






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

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

     return self.weight * hidden_states







class __snake_case				(						nn.Module		):




   def 	__a						(    self			:				Any       ,	_lowercase			:				str      ):



     """simple docstring"""






     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.04_47_15 * torch.pow(_a       ,	3.0      ))      ))







class __snake_case				(						nn.Module		):




   def __init__(    self			:				List[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__										=      nn.Linear(_a       ,	out_features * 2       ,	bias=_a      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






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


import math






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  float   )				->		float:







	'''simple docstring'''


	return math.pow(_snake_case      ,   2   ) - a






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	return 2 * x






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     2.0

	while start <= a:
		__magic_name__		:		str	   =     math.pow(_snake_case      ,   2   )

	return start






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  int = 9999      ,   _snake_case      :  float = 0.00_000_000_000_001   )				->		float:







	'''simple docstring'''


	if a < 0:
		raise ValueError("math domain error"   )

	__magic_name__		:		Optional[int]	   =     get_initial_point(_snake_case   )

	for _ in range(_snake_case   ):
		__magic_name__		:		int	   =     value
		__magic_name__		:		str	   =     value - fx(_snake_case      ,   _snake_case   ) / fx_derivative(_snake_case   )
		if abs(prev_value - value   ) < tolerance:
			return value

	return value


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

				testmod()
