

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 (       UpperCAmelCase__			,							UpperCAmelCase__							):




      """simple docstring"""

      @register_to_config
      def __init__(					self				:							Tuple		,       UpperCAmelCase				:							int = 128		,       UpperCAmelCase				:							int = 256		,       UpperCAmelCase				:							float = 2_000.0		,       UpperCAmelCase				:							int = 768		,       UpperCAmelCase				:							int = 12		,       UpperCAmelCase				:							int = 12		,       UpperCAmelCase				:							int = 64		,       UpperCAmelCase				:							int = 2048		,       UpperCAmelCase				:							float = 0.1		,       ):
            super().__init__()

            A_		   =							nn.Sequential(
                nn.Linear(__lowercase		,       d_model * 4		,       bias=__lowercase     )		,       nn.SiLU()		,       nn.Linear(d_model * 4		,       d_model * 4		,       bias=__lowercase     )		,       nn.SiLU()		,       )

            A_		   =							nn.Embedding(__lowercase		,       __lowercase     )
            A_		   =							False

            A_		   =							nn.Linear(__lowercase		,       __lowercase		,       bias=__lowercase     )

            A_		   =							nn.Dropout(p=__lowercase     )

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

            A_		   =							TaLayerNorm(__lowercase     )

            A_		   =							nn.Dropout(p=__lowercase     )
            A_		   =							nn.Linear(__lowercase		,       __lowercase		,       bias=__lowercase     )





      def 						__A     (					self				:							int		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							Union[str, Any]     ):
            A_		   =							torch.mul(query_input.unsqueeze(-1     )		,       key_input.unsqueeze(-2     )     )
            return mask.unsqueeze(-3     )







      def 						__A     (					self				:							Any		,       UpperCAmelCase				:							Tuple		,       UpperCAmelCase				:							List[str]		,       UpperCAmelCase				:							List[str]     ):
            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(__lowercase     ).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(__lowercase		,       device=decoder_input_tokens.device     )		,       (batch, seq_length)		,       )

            A_		   =							self.position_encoding(__lowercase     )

            A_		   =							self.continuous_inputs_projection(__lowercase     )
            inputs += position_encodings
            A_		   =							self.dropout(__lowercase     )

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

            A_		   =							self.decoder_norm(__lowercase     )
            A_		   =							self.post_dropout(__lowercase     )

            A_		   =							self.spec_out(__lowercase     )
            return spec_out




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							List[Any]		,       UpperCAmelCase				:							str		,       UpperCAmelCase				:							Dict		,       UpperCAmelCase				:							Optional[int]		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							Tuple=1E-6     ):
            super().__init__()
            A_		   =							nn.ModuleList()

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

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

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







      def 						__A     (					self				:							List[Any]		,       UpperCAmelCase				:							Tuple		,       UpperCAmelCase				:							int=None		,       UpperCAmelCase				:							Optional[Any]=None		,       UpperCAmelCase				:							Dict=None		,       UpperCAmelCase				:							Dict=None		,       UpperCAmelCase				:							str=None		,       ):
            A_		   =							self.layer[0](
                __lowercase		,       conditioning_emb=__lowercase		,       attention_mask=__lowercase		,       )

            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](
                      __lowercase		,       key_value_states=__lowercase		,       attention_mask=__lowercase		,       )

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

            return (hidden_states,)




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							List[Any]		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							str		,       UpperCAmelCase				:							Union[str, Any]     ):
            super().__init__()
            A_		   =							TaLayerNorm(__lowercase     )
            A_		   =							TaFiLMLayer(in_features=d_model * 4		,       out_features=__lowercase     )
            A_		   =							Attention(query_dim=__lowercase		,       heads=__lowercase		,       dim_head=__lowercase		,       out_bias=__lowercase		,       scale_qk=__lowercase     )
            A_		   =							nn.Dropout(__lowercase     )







      def 						__A     (					self				:							str		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							Union[str, Any]=None		,       UpperCAmelCase				:							Optional[int]=None		,       ):
            A_		   =							self.layer_norm(__lowercase     )

            if conditioning_emb is not None:
                  A_		   =							self.FiLMLayer(__lowercase		,       __lowercase     )

            # Self-attention block
            A_		   =							self.attention(__lowercase     )

            A_		   =							hidden_states + self.dropout(__lowercase     )

            return hidden_states




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							Optional[Any]		,       UpperCAmelCase				:							Optional[Any]		,       UpperCAmelCase				:							Optional[int]		,       UpperCAmelCase				:							Any		,       UpperCAmelCase				:							Optional[Any]		,       UpperCAmelCase				:							Any     ):
            super().__init__()
            A_		   =							Attention(query_dim=__lowercase		,       heads=__lowercase		,       dim_head=__lowercase		,       out_bias=__lowercase		,       scale_qk=__lowercase     )
            A_		   =							TaLayerNorm(__lowercase		,       eps=__lowercase     )
            A_		   =							nn.Dropout(__lowercase     )







      def 						__A     (					self				:							Optional[Any]		,       UpperCAmelCase				:							str		,       UpperCAmelCase				:							Tuple=None		,       UpperCAmelCase				:							str=None		,       ):
            A_		   =							self.layer_norm(__lowercase     )
            A_		   =							self.attention(
                __lowercase		,       encoder_hidden_states=__lowercase		,       attention_mask=attention_mask.squeeze(1     )		,       )
            A_		   =							hidden_states + self.dropout(__lowercase     )
            return layer_output




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							List[Any]		,       UpperCAmelCase				:							Optional[int]		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							List[Any]     ):
            super().__init__()
            A_		   =							TaDenseGatedActDense(d_model=__lowercase		,       d_ff=__lowercase		,       dropout_rate=__lowercase     )
            A_		   =							TaFiLMLayer(in_features=d_model * 4		,       out_features=__lowercase     )
            A_		   =							TaLayerNorm(__lowercase		,       eps=__lowercase     )
            A_		   =							nn.Dropout(__lowercase     )







      def 						__A     (					self				:							str		,       UpperCAmelCase				:							List[str]		,       UpperCAmelCase				:							Dict=None     ):
            A_		   =							self.layer_norm(__lowercase     )
            if conditioning_emb is not None:
                  A_		   =							self.film(__lowercase		,       __lowercase     )

            A_		   =							self.DenseReluDense(__lowercase     )
            A_		   =							hidden_states + self.dropout(__lowercase     )
            return hidden_states




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							Optional[Any]		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							Any		,       UpperCAmelCase				:							List[str]     ):
            super().__init__()
            A_		   =							nn.Linear(__lowercase		,       __lowercase		,       bias=__lowercase     )
            A_		   =							nn.Linear(__lowercase		,       __lowercase		,       bias=__lowercase     )
            A_		   =							nn.Linear(__lowercase		,       __lowercase		,       bias=__lowercase     )
            A_		   =							nn.Dropout(__lowercase     )
            A_		   =							NewGELUActivation()







      def 						__A     (					self				:							Optional[Any]		,       UpperCAmelCase				:							Any     ):
            A_		   =							self.act(self.wi_a(__lowercase     )     )
            A_		   =							self.wi_a(__lowercase     )
            A_		   =							hidden_gelu * hidden_linear
            A_		   =							self.dropout(__lowercase     )

            A_		   =							self.wo(__lowercase     )
            return hidden_states




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							Any		,       UpperCAmelCase				:							Dict		,       UpperCAmelCase				:							Any=1E-6     ):
            super().__init__()
            A_		   =							nn.Parameter(torch.ones(__lowercase     )     )
            A_		   =							eps







      def 						__A     (					self				:							Union[str, Any]		,       UpperCAmelCase				:							List[str]     ):
            A_		   =							hidden_states.to(torch.floataa     ).pow(2     ).mean(-1		,       keepdim=__lowercase     )
            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							):




      """simple docstring"""

      def 						__A     (					self				:							List[Any]		,       UpperCAmelCase				:							torch.Tensor     ):
            return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.044_715 * torch.pow(__lowercase		,       3.0     ))     ))




class    _a (       nn.Module							):




      """simple docstring"""

      def __init__(					self				:							Tuple		,       UpperCAmelCase				:							List[Any]		,       UpperCAmelCase				:							Union[str, Any]     ):
            super().__init__()
            A_		   =							nn.Linear(__lowercase		,       out_features * 2		,       bias=__lowercase     )







      def 						__A     (					self				:							List[str]		,       UpperCAmelCase				:							int		,       UpperCAmelCase				:							Optional[Any]     ):
            A_		   =							self.scale_bias(__lowercase     )
            A_   ,			A_		   =							torch.chunk(__lowercase		,       2		,       -1     )
            A_		   =							x * (1 + scale) + shift
            return x





from torch import nn


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f"Unsupported activation function: {act_fn}"							)
