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					(      __a   ,						__a						):




		@register_to_config
		def __init__(      self     :	List[str]     , a_     :	Any = 128     , a_     :	Optional[int] = 256     , a_     :	Optional[Any] = 2000.0     , a_     :	int = 768     , a_     :	Optional[Any] = 12     , a_     :	Union[str, Any] = 12     , a_     :	int = 64     , a_     :	Optional[Any] = 2048     , a_     :	Optional[int] = 0.1     , )->       Any:




							"""simple docstring"""
							super().__init__()

							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		nn.Sequential(
							    nn.Linear(lowerCAmelCase_     , d_model * 4     , bias=lowerCAmelCase_				)     , nn.SiLU()     , nn.Linear(d_model * 4     , d_model * 4     , bias=lowerCAmelCase_				)     , nn.SiLU()     , )

							SCREAMING_SNAKE_CASE__				:				int			=		nn.Embedding(lowerCAmelCase_     , lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		False

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		nn.Linear(lowerCAmelCase_     , lowerCAmelCase_     , bias=lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				Any			=		nn.Dropout(p=lowerCAmelCase_				)

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

							SCREAMING_SNAKE_CASE__				:				List[str]			=		TaLayerNorm(lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		nn.Dropout(p=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				str			=		nn.Linear(lowerCAmelCase_     , lowerCAmelCase_     , bias=lowerCAmelCase_				)




		def 	__lowercase(      self     :	List[Any]     , a_     :	List[str]     , a_     :	Tuple				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.mul(query_input.unsqueeze(-1				)     , key_input.unsqueeze(-2				)				)
							return mask.unsqueeze(-3				)




		def 	__lowercase(      self     :	List[Any]     , a_     :	Optional[int]     , a_     :	Tuple     , a_     :	Dict				)->       List[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[str]			=		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__				:				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				)

							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.conditioning_emb(lowerCAmelCase_				).unsqueeze(1				)

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

							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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__				:				Optional[int]			=		torch.broadcast_to(
							    torch.arange(lowerCAmelCase_     , device=decoder_input_tokens.device				)     , (batch, seq_length)     , )

							SCREAMING_SNAKE_CASE__				:				int			=		self.position_encoding(lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.continuous_inputs_projection(lowerCAmelCase_				)
							inputs += position_encodings
							SCREAMING_SNAKE_CASE__				:				int			=		self.dropout(lowerCAmelCase_				)

							# decoder: No padding present.
							SCREAMING_SNAKE_CASE__				:				Tuple			=		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__				:				Any			=		[(x, self.encoder_decoder_mask(lowerCAmelCase_     , lowerCAmelCase_				)) for x, y in encodings_and_masks]

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

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

							SCREAMING_SNAKE_CASE__				:				Tuple			=		self.decoder_norm(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				int			=		self.post_dropout(lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				int			=		self.spec_out(lowerCAmelCase_				)
							return spec_out




class       snake_case					(      nn.Module						):




		def __init__(      self     :	List[Any]     , a_     :	List[Any]     , a_     :	Optional[int]     , a_     :	Optional[Any]     , a_     :	Optional[int]     , a_     :	List[str]     , a_     :	Tuple=1e-6				)->       Any:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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     :	Union[str, Any]     , a_     :	Dict     , a_     :	Tuple=None     , a_     :	Any=None     , a_     :	Tuple=None     , a_     :	Dict=None     , a_     :	List[Any]=None     , )->       List[str]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.layer[0](
							    lowerCAmelCase_     , conditioning_emb=lowerCAmelCase_     , attention_mask=lowerCAmelCase_     , )

							if encoder_hidden_states is not None:
												SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.where(encoder_attention_mask > 0     , 0     , -1e1_0				).to(
												    encoder_hidden_states.dtype				)

												SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.layer[1](
												    lowerCAmelCase_     , key_value_states=lowerCAmelCase_     , attention_mask=lowerCAmelCase_     , )

							# Apply Film Conditional Feed Forward layer
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.layer[-1](lowerCAmelCase_     , lowerCAmelCase_				)

							return (hidden_states,)




class       snake_case					(      nn.Module						):




		def __init__(      self     :	Dict     , a_     :	List[str]     , a_     :	List[str]     , a_     :	Union[str, Any]     , a_     :	List[Any]				)->       List[Any]:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				List[str]			=		TaLayerNorm(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				str			=		TaFiLMLayer(in_features=d_model * 4     , out_features=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Attention(query_dim=lowerCAmelCase_     , heads=lowerCAmelCase_     , dim_head=lowerCAmelCase_     , out_bias=lowerCAmelCase_     , scale_qk=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		nn.Dropout(lowerCAmelCase_				)




		def 	__lowercase(      self     :	str     , a_     :	List[str]     , a_     :	str=None     , a_     :	Dict=None     , )->       Optional[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.layer_norm(lowerCAmelCase_				)

							if conditioning_emb is not None:
												SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.FiLMLayer(lowerCAmelCase_     , lowerCAmelCase_				)

							# Self-attention block
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.attention(lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				List[str]			=		hidden_states + self.dropout(lowerCAmelCase_				)

							return hidden_states




class       snake_case					(      nn.Module						):




		def __init__(      self     :	Dict     , a_     :	Optional[int]     , a_     :	List[str]     , a_     :	List[str]     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]				)->       Any:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Attention(query_dim=lowerCAmelCase_     , heads=lowerCAmelCase_     , dim_head=lowerCAmelCase_     , out_bias=lowerCAmelCase_     , scale_qk=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		TaLayerNorm(lowerCAmelCase_     , eps=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		nn.Dropout(lowerCAmelCase_				)




		def 	__lowercase(      self     :	Dict     , a_     :	int     , a_     :	int=None     , a_     :	Tuple=None     , )->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.layer_norm(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.attention(
							    lowerCAmelCase_     , encoder_hidden_states=lowerCAmelCase_     , attention_mask=attention_mask.squeeze(1				)     , )
							SCREAMING_SNAKE_CASE__				:				List[str]			=		hidden_states + self.dropout(lowerCAmelCase_				)
							return layer_output




class       snake_case					(      nn.Module						):




		def __init__(      self     :	List[str]     , a_     :	List[str]     , a_     :	Any     , a_     :	str     , a_     :	str				)->       Optional[int]:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		TaDenseGatedActDense(d_model=lowerCAmelCase_     , d_ff=lowerCAmelCase_     , dropout_rate=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		TaFiLMLayer(in_features=d_model * 4     , out_features=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				int			=		TaLayerNorm(lowerCAmelCase_     , eps=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Tuple			=		nn.Dropout(lowerCAmelCase_				)




		def 	__lowercase(      self     :	Any     , a_     :	str     , a_     :	Tuple=None				)->       int:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		self.layer_norm(lowerCAmelCase_				)
							if conditioning_emb is not None:
												SCREAMING_SNAKE_CASE__				:				int			=		self.film(lowerCAmelCase_     , lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.DenseReluDense(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Any			=		hidden_states + self.dropout(lowerCAmelCase_				)
							return hidden_states




class       snake_case					(      nn.Module						):




		def __init__(      self     :	Any     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , a_     :	Optional[int]				)->       Optional[Any]:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Tuple			=		nn.Linear(lowerCAmelCase_     , lowerCAmelCase_     , bias=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.Linear(lowerCAmelCase_     , lowerCAmelCase_     , bias=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		nn.Linear(lowerCAmelCase_     , lowerCAmelCase_     , bias=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.Dropout(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		NewGELUActivation()




		def 	__lowercase(      self     :	int     , a_     :	Dict				)->       Optional[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.act(self.wi_a(lowerCAmelCase_				)				)
							SCREAMING_SNAKE_CASE__				:				Any			=		self.wi_a(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				int			=		hidden_gelu * hidden_linear
							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.dropout(lowerCAmelCase_				)

							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.wo(lowerCAmelCase_				)
							return hidden_states




class       snake_case					(      nn.Module						):




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




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		nn.Parameter(torch.ones(lowerCAmelCase_				)				)
							SCREAMING_SNAKE_CASE__				:				int			=		eps




		def 	__lowercase(      self     :	Optional[int]     , a_     :	Dict				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		hidden_states.to(torch.floataa				).pow(2				).mean(-1     , keepdim=lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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__				:				Any			=		hidden_states.to(self.weight.dtype				)

							return self.weight * hidden_states




class       snake_case					(      nn.Module						):




		def 	__lowercase(      self     :	Tuple     , a_     :	str				)->       torch.Tensor:




							"""simple docstring"""
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(lowerCAmelCase_     , 3.0				))				))




class       snake_case					(      nn.Module						):




		def __init__(      self     :	Dict     , a_     :	str     , a_     :	Optional[Any]				)->       Tuple:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				str			=		nn.Linear(lowerCAmelCase_     , out_features * 2     , bias=lowerCAmelCase_				)




		def 	__lowercase(      self     :	Optional[Any]     , a_     :	Optional[Any]     , a_     :	List[str]				)->       List[str]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.scale_bias(lowerCAmelCase_				)
							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				str			=		torch.chunk(lowerCAmelCase_     , 2     , -1				)
							SCREAMING_SNAKE_CASE__				:				Tuple			=		x * (1 + scale) + shift
							return x




from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     if not is_accelerate_available():
          return method
     SCREAMING_SNAKE_CASE__				:				str			=		version.parse(accelerate.__version__ ).base_version
     if version.parse(lowercase__ ) < version.parse('0.17.0' ):
          return method

     def wrapper(self  :			Optional[int]     ,  *lowercase__  :			int     ,  **lowercase__  :			Tuple ):
          if hasattr(self     ,  '_hf_hook' ) and hasattr(self._hf_hook     ,  'pre_forward' ):
               self._hf_hook.pre_forward(self )
          return method(self     ,  *lowercase__     ,  **lowercase__ )

     return wrapper



