





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__     ( snake_case_ ,     snake_case_    ):





      @register_to_config
      def __init__(				self ,							A_ = 128 ,							A_ = 256 ,							A_ = 20_00.0 ,							A_ = 768 ,							A_ = 12 ,							A_ = 12 ,							A_ = 64 ,							A_ = 2048 ,							A_ = 0.1 ,							):
       '''simple docstring'''




       super().__init__()

       UpperCamelCase			:      str 			=     nn.Sequential(
           nn.Linear(A_ ,							d_model * 4 ,							bias=A_     ) ,							nn.SiLU() ,							nn.Linear(d_model * 4 ,							d_model * 4 ,							bias=A_     ) ,							nn.SiLU() ,							)

       UpperCamelCase			:      int 			=     nn.Embedding(A_ ,							A_     )
       UpperCamelCase			:      List[str] 			=     False

       UpperCamelCase			:      Optional[Any] 			=     nn.Linear(A_ ,							A_ ,							bias=A_     )

       UpperCamelCase			:      Any 			=     nn.Dropout(p=A_     )

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

       UpperCamelCase			:      Tuple 			=     TaLayerNorm(A_     )

       UpperCamelCase			:      Tuple 			=     nn.Dropout(p=A_     )
       UpperCamelCase			:      int 			=     nn.Linear(A_ ,							A_ ,							bias=A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

       # decoder_noise_time is in [0, 1), so rescale to expected timing range.
       UpperCamelCase			:      Optional[int] 			=     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     )

       UpperCamelCase			:      Union[str, Any] 			=     self.conditioning_emb(A_     ).unsqueeze(1     )

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

       UpperCamelCase			:      str 			=     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.
       UpperCamelCase			:      Tuple 			=     torch.broadcast_to(
           torch.arange(A_ ,							device=decoder_input_tokens.device     ) ,							(batch, seq_length) ,							)

       UpperCamelCase			:      Any 			=     self.position_encoding(A_     )

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

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

       # Translate encoding masks to encoder-decoder masks.
       UpperCamelCase			:      int 			=     [(x, self.encoder_decoder_mask(A_ ,							A_     )) for x, y in encodings_and_masks]

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

       for lyr in self.decoders:
        UpperCamelCase			:      Optional[int] 			=     lyr(
            A_ ,							conditioning_emb=A_ ,							encoder_hidden_states=A_ ,							encoder_attention_mask=A_ ,							)[0]

       UpperCamelCase			:      List[str] 			=     self.decoder_norm(A_     )
       UpperCamelCase			:      Any 			=     self.post_dropout(A_     )

       UpperCamelCase			:      Optional[Any] 			=     self.spec_out(A_     )
       return spec_out



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_=1e-6     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Optional[int] 			=     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  __UpperCamelCase(				self ,							A_ ,							A_=None ,							A_=None ,							A_=None ,							A_=None ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.layer[0](
           A_ ,							conditioning_emb=A_ ,							attention_mask=A_ ,							)

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

        UpperCamelCase			:      Dict 			=     self.layer[1](
            A_ ,							key_value_states=A_ ,							attention_mask=A_ ,							)

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

       return (hidden_states,)



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      str 			=     TaLayerNorm(A_     )
       UpperCamelCase			:      int 			=     TaFiLMLayer(in_features=d_model * 4 ,							out_features=A_     )
       UpperCamelCase			:      Optional[Any] 			=     Attention(query_dim=A_ ,							heads=A_ ,							dim_head=A_ ,							out_bias=A_ ,							scale_qk=A_     )
       UpperCamelCase			:      int 			=     nn.Dropout(A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_=None ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.layer_norm(A_     )

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

       # Self-attention block
       UpperCamelCase			:      Tuple 			=     self.attention(A_     )

       UpperCamelCase			:      Optional[int] 			=     hidden_states + self.dropout(A_     )

       return hidden_states



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      List[Any] 			=     Attention(query_dim=A_ ,							heads=A_ ,							dim_head=A_ ,							out_bias=A_ ,							scale_qk=A_     )
       UpperCamelCase			:      List[str] 			=     TaLayerNorm(A_ ,							eps=A_     )
       UpperCamelCase			:      List[Any] 			=     nn.Dropout(A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_=None ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.layer_norm(A_     )
       UpperCamelCase			:      str 			=     self.attention(
           A_ ,							encoder_hidden_states=A_ ,							attention_mask=attention_mask.squeeze(1     ) ,							)
       UpperCamelCase			:      str 			=     hidden_states + self.dropout(A_     )
       return layer_output



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Optional[int] 			=     TaDenseGatedActDense(d_model=A_ ,							d_ff=A_ ,							dropout_rate=A_     )
       UpperCamelCase			:      Optional[Any] 			=     TaFiLMLayer(in_features=d_model * 4 ,							out_features=A_     )
       UpperCamelCase			:      Tuple 			=     TaLayerNorm(A_ ,							eps=A_     )
       UpperCamelCase			:      Dict 			=     nn.Dropout(A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.layer_norm(A_     )
       if conditioning_emb is not None:
        UpperCamelCase			:      Union[str, Any] 			=     self.film(A_ ,							A_     )

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



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Tuple 			=     nn.Linear(A_ ,							A_ ,							bias=A_     )
       UpperCamelCase			:      Any 			=     nn.Linear(A_ ,							A_ ,							bias=A_     )
       UpperCamelCase			:      List[Any] 			=     nn.Linear(A_ ,							A_ ,							bias=A_     )
       UpperCamelCase			:      Optional[int] 			=     nn.Dropout(A_     )
       UpperCamelCase			:      List[str] 			=     NewGELUActivation()





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.act(self.wi_a(A_     )     )
       UpperCamelCase			:      str 			=     self.wi_a(A_     )
       UpperCamelCase			:      List[str] 			=     hidden_gelu * hidden_linear
       UpperCamelCase			:      int 			=     self.dropout(A_     )

       UpperCamelCase			:      str 			=     self.wo(A_     )
       return hidden_states



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_=1e-6     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Any 			=     nn.Parameter(torch.ones(A_     )     )
       UpperCamelCase			:      Optional[Any] 			=     eps





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     hidden_states.to(torch.floataa     ).pow(2     ).mean(-1 ,							keepdim=A_     )
       UpperCamelCase			:      int 			=     hidden_states * torch.rsqrt(variance + self.variance_epsilon     )

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

       return self.weight * hidden_states



class       A__     ( nn.Module    ):





      def  __UpperCamelCase(				self ,							A_     ):
       '''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       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Any 			=     nn.Linear(A_ ,							out_features * 2 ,							bias=A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.scale_bias(A_     )
       UpperCamelCase			,					UpperCamelCase			:      Any 			=     torch.chunk(A_ ,							2 ,							-1     )
       UpperCamelCase			:      Tuple 			=     x * (1 + scale) + shift
       return x







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     ArgumentParser(
     description=(
         "PyTorch TPU distributed training launch "
         "helper utility that will spawn up "
         "multiple distributed processes"
     )  )

 # Optional arguments for the launch helper
 parser.add_argument("--num_cores"							,     type=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

 # positional
 parser.add_argument(
     "training_script"							,     type=_lowerCAmelCase							,     help=(
         "The full path to the single TPU training "
         "program/script to be launched in parallel, "
         "followed by all the arguments for the "
         "training script"
     )							,     )

 # rest from the training program
 parser.add_argument("training_script_args"							,     nargs=_lowerCAmelCase  )

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

 # Import training_script as a module.
 UpperCamelCase			:      Union[str, Any] 			=     Path(args.training_script  )
 sys.path.append(str(script_fpath.parent.resolve()  )  )
 UpperCamelCase			:      List[Any] 			=     script_fpath.stem
 UpperCamelCase			:      Optional[Any] 			=     importlib.import_module(_lowerCAmelCase  )

 # Patch sys.argv
 UpperCamelCase			:      List[Any] 			=     [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores  )]
 xmp.spawn(mod._mp_fn							,     args=()							,     nprocs=args.num_cores  )


if __name__ == "__main__":
    main()
