






'''simple docstring'''




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase_												=					logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCamelCase_												=					{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase_												=					{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase_												=					sorted(arg_to_scheduler.keys())
lowerCamelCase_												=					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class    _UpperCAmelCase			(     pl.LightningModule						):




   """simple docstring"""

   def __init__(		self :	str      ,      __UpperCAmelCase :	argparse.Namespace      ,      __UpperCAmelCase :	Tuple=None      ,      __UpperCAmelCase :	Dict="base"      ,      __UpperCAmelCase :	Union[str, Any]=None      ,      __UpperCAmelCase :	Dict=None      ,      __UpperCAmelCase :	Union[str, Any]=None      ,      **__UpperCAmelCase :	Optional[Any]      ,      ):





         '''simple docstring'''





         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(__UpperCAmelCase		)
         _A			     =	0
         _A			     =	Path(self.hparams.output_dir		)
         _A			     =	self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               _A			     =	AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path      ,      **({"num_labels": num_labels} if num_labels is not None else {})      ,      cache_dir=__UpperCAmelCase      ,      **__UpperCAmelCase      ,      )
         else:
               _A			     =	config

         _A			     =	("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
         for p in extra_model_params:
               if getattr(self.hparams      ,      __UpperCAmelCase      ,      __UpperCAmelCase		):
                     assert hasattr(self.config      ,      __UpperCAmelCase		), f'''model config doesn\'t have a `{p}` attribute'''
                     setattr(self.config      ,      __UpperCAmelCase      ,      getattr(self.hparams      ,      __UpperCAmelCase		)		)

         if tokenizer is None:
               _A			     =	AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,      cache_dir=__UpperCAmelCase      ,      )
         else:
               _A			     =	tokenizer
         _A			     =	MODEL_MODES[mode]
         if model is None:
               _A			     =	self.model_type.from_pretrained(
                   self.hparams.model_name_or_path      ,      from_tf=bool(".ckpt" in self.hparams.model_name_or_path		)      ,      config=self.config      ,      cache_dir=__UpperCAmelCase      ,      )
         else:
               _A			     =	model

   def 						lowerCAmelCase (		self :	Any      ,      *__UpperCAmelCase :	Optional[Any]      ,      **__UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	self.model_type.from_pretrained(*__UpperCAmelCase      ,      **__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Dict		):





         '''simple docstring'''





         _A			     =	arg_to_scheduler[self.hparams.lr_scheduler]
         _A			     =	get_schedule_func(
             self.opt      ,      num_warmup_steps=self.hparams.warmup_steps      ,      num_training_steps=self.total_steps()		)
         _A			     =	{"scheduler": scheduler, "interval": "step", "frequency": 1}
         return scheduler

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	self.model
         _A			     =	["bias", "LayerNorm.weight"]
         _A			     =	[
             {
                 "params": [
                     p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)
                 ],  # check this named paramters
                 "weight_decay": self.hparams.weight_decay,
             },
             {
                 "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)],
                 "weight_decay": 0.0,
             },
         ]
         if self.hparams.adafactor:
               _A			     =	Adafactor(
                   __UpperCAmelCase      ,      lr=self.hparams.learning_rate      ,      scale_parameter=__UpperCAmelCase      ,      relative_step=__UpperCAmelCase		)

         else:
               _A			     =	AdamW(
                   __UpperCAmelCase      ,      lr=self.hparams.learning_rate      ,      eps=self.hparams.adam_epsilon		)
         _A			     =	optimizer

         _A			     =	self.get_lr_scheduler()

         return [optimizer], [scheduler]

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	Dict		):





         '''simple docstring'''





         return self.validation_step(__UpperCAmelCase      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





         return self.validation_end(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         _A			     =	max(1      ,      self.hparams.gpus		)  # TODO: consider num_tpu_cores
         _A			     =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

   def 						lowerCAmelCase (		self :	int      ,      __UpperCAmelCase :	Dict		):





         '''simple docstring'''





         if stage == "test":
               _A			     =	len(self.test_dataloader().dataset		)
         else:
               _A			     =	self.get_dataloader("train"      ,      self.hparams.train_batch_size      ,      shuffle=__UpperCAmelCase		)
               _A			     =	len(self.train_dataloader().dataset		)

   def 						lowerCAmelCase (		self :	Tuple      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	bool = False		):





         '''simple docstring'''





         raise NotImplementedError("You must implement this for your task"		)

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         return self.train_loader

   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         return self.get_dataloader("dev"      ,      self.hparams.eval_batch_size      ,      shuffle=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         return self.get_dataloader("test"      ,      self.hparams.eval_batch_size      ,      shuffle=__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	Tuple		):





         '''simple docstring'''





         return os.path.join(
             self.hparams.data_dir      ,      "cached_{}_{}_{}".format(
                 __UpperCAmelCase      ,      list(filter(__UpperCAmelCase      ,      self.hparams.model_name_or_path.split("/"		)		)		).pop()      ,      str(self.hparams.max_seq_length		)      ,      )      ,      )

   @pl.utilities.rank_zero_only
   def 						lowerCAmelCase (		self :	int      ,      __UpperCAmelCase :	Dict[str, Any]		):





         '''simple docstring'''





         _A			     =	self.output_dir.joinpath("best_tfmr"		)
         _A			     =	self.step_count
         self.model.save_pretrained(__UpperCAmelCase		)
         self.tokenizer.save_pretrained(__UpperCAmelCase		)

   @staticmethod
   def 						lowerCAmelCase (		__UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         parser.add_argument(
             "--model_name_or_path"      ,      default=__UpperCAmelCase      ,      type=__UpperCAmelCase      ,      required=__UpperCAmelCase      ,      help="Path to pretrained model or model identifier from huggingface.co/models"      ,      )
         parser.add_argument(
             "--config_name"      ,      default=""      ,      type=__UpperCAmelCase      ,      help="Pretrained config name or path if not the same as model_name"		)
         parser.add_argument(
             "--tokenizer_name"      ,      default=__UpperCAmelCase      ,      type=__UpperCAmelCase      ,      help="Pretrained tokenizer name or path if not the same as model_name"      ,      )
         parser.add_argument(
             "--cache_dir"      ,      default=str(Path(__UpperCAmelCase		).parent / "test_run" / "cache"		)      ,      type=__UpperCAmelCase      ,      help="Where do you want to store the pre-trained models downloaded from huggingface.co"      ,      )
         parser.add_argument(
             "--encoder_layerdrop"      ,      type=__UpperCAmelCase      ,      help="Encoder layer dropout probability (Optional). Goes into model.config"      ,      )
         parser.add_argument(
             "--decoder_layerdrop"      ,      type=__UpperCAmelCase      ,      help="Decoder layer dropout probability (Optional). Goes into model.config"      ,      )
         parser.add_argument(
             "--dropout"      ,      type=__UpperCAmelCase      ,      help="Dropout probability (Optional). Goes into model.config"      ,      )
         parser.add_argument(
             "--attention_dropout"      ,      type=__UpperCAmelCase      ,      help="Attention dropout probability (Optional). Goes into model.config"      ,      )
         parser.add_argument("--learning_rate"      ,      default=5E-5      ,      type=__UpperCAmelCase      ,      help="The initial learning rate for Adam."		)
         parser.add_argument(
             "--lr_scheduler"      ,      default="linear"      ,      choices=__UpperCAmelCase      ,      metavar=__UpperCAmelCase      ,      type=__UpperCAmelCase      ,      help="Learning rate scheduler"      ,      )
         parser.add_argument("--weight_decay"      ,      default=0.0      ,      type=__UpperCAmelCase      ,      help="Weight decay if we apply some."		)
         parser.add_argument("--adam_epsilon"      ,      default=1E-8      ,      type=__UpperCAmelCase      ,      help="Epsilon for Adam optimizer."		)
         parser.add_argument("--warmup_steps"      ,      default=0      ,      type=__UpperCAmelCase      ,      help="Linear warmup over warmup_steps."		)
         parser.add_argument("--num_workers"      ,      default=4      ,      type=__UpperCAmelCase      ,      help="kwarg passed to DataLoader"		)
         parser.add_argument("--num_train_epochs"      ,      dest="max_epochs"      ,      default=3      ,      type=__UpperCAmelCase		)
         parser.add_argument("--train_batch_size"      ,      default=32      ,      type=__UpperCAmelCase		)
         parser.add_argument("--eval_batch_size"      ,      default=32      ,      type=__UpperCAmelCase		)
         parser.add_argument("--adafactor"      ,      action="store_true"		)





class    _UpperCAmelCase			(     pl.Callback						):




   """simple docstring"""

   def 						lowerCAmelCase (		self :	str      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Union[str, Any]		):





         '''simple docstring'''





         if (
             trainer.is_global_zero and trainer.global_rank == 0
         ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class    _UpperCAmelCase			(     pl.Callback						):




   """simple docstring"""

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	List[Any]		):





         '''simple docstring'''





         for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                     print(__UpperCAmelCase		)





class    _UpperCAmelCase			(     pl.Callback						):




   """simple docstring"""

   def 						lowerCAmelCase (		self :	Union[str, Any]      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	str		):





         '''simple docstring'''





         _A			     =	trainer.lr_schedulers[0]["scheduler"]
         _A			     =	{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
         pl_module.logger.log_metrics(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	pl.Trainer      ,      __UpperCAmelCase :	pl.LightningModule		):





         '''simple docstring'''





         rank_zero_info("***** Validation results *****"		)
         _A			     =	trainer.callback_metrics
         # Log results
         for key in sorted(__UpperCAmelCase		):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info("{} = {}\n".format(__UpperCAmelCase      ,      str(metrics[key]		)		)		)

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	pl.Trainer      ,      __UpperCAmelCase :	pl.LightningModule		):





         '''simple docstring'''





         rank_zero_info("***** Test results *****"		)
         _A			     =	trainer.callback_metrics
         # Log and save results to file
         _A			     =	os.path.join(pl_module.hparams.output_dir      ,      "test_results.txt"		)
         with open(__UpperCAmelCase      ,      "w"		) as writer:
               for key in sorted(__UpperCAmelCase		):
                     if key not in ["log", "progress_bar"]:
                           rank_zero_info("{} = {}\n".format(__UpperCAmelCase      ,      str(metrics[key]		)		)		)
                           writer.write("{} = {}\n".format(__UpperCAmelCase      ,      str(metrics[key]		)		)		)





def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						None:

      '''simple docstring'''


      parser.add_argument(
          "--output_dir"   ,  default=str(Path(__lowercase       ).parent / "test_run" / "model_checkpoints"       )   ,  type=__lowercase   ,  help="The output directory where the model predictions and checkpoints will be written."   ,  )
      parser.add_argument(
          "--fp16"   ,  action="store_true"   ,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   ,  )

      parser.add_argument(
          "--fp16_opt_level"   ,  type=__lowercase   ,  default="O2"   ,  help=(
              "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
              "See details at https://nvidia.github.io/apex/amp.html"
          )   ,  )
      parser.add_argument("--n_tpu_cores"   ,  dest="tpu_cores"   ,  type=__lowercase       )
      parser.add_argument("--max_grad_norm"   ,  dest="gradient_clip_val"   ,  default=1.0   ,  type=__lowercase   ,  help="Max gradient norm"       )
      parser.add_argument("--do_train"   ,  action="store_true"   ,  help="Whether to run training."       )
      parser.add_argument("--do_predict"   ,  action="store_true"   ,  help="Whether to run predictions on the test set."       )
      parser.add_argument(
          "--gradient_accumulation_steps"   ,  dest="accumulate_grad_batches"   ,  type=__lowercase   ,  default=1   ,  help="Number of updates steps to accumulate before performing a backward/update pass."   ,  )
      parser.add_argument("--seed"   ,  type=__lowercase   ,  default=42   ,  help="random seed for initialization"       )
      parser.add_argument(
          "--data_dir"   ,  default=str(Path(__lowercase       ).parent / "test_run" / "dummy-train-data"       )   ,  type=__lowercase   ,  help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."   ,  )


def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase=None   ,  __lowercase=True   ,  __lowercase=[]   ,  __lowercase=None   ,  __lowercase=None   ,  **__lowercase   ,  )       ->						Any:

      '''simple docstring'''


      pl.seed_everything(args.seed       )

      # init model
      _A			     =	Path(model.hparams.output_dir       )
      odir.mkdir(exist_ok=__lowercase       )

      # add custom checkpoints
      if checkpoint_callback is None:
            _A			     =	pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir   ,  prefix="checkpoint"   ,  monitor="val_loss"   ,  mode="min"   ,  save_top_k=1       )
      if early_stopping_callback:
            extra_callbacks.append(__lowercase       )
      if logging_callback is None:
            _A			     =	LoggingCallback()

      _A			     =	{}

      if args.fpaa:
            _A			     =	16

      if args.gpus > 1:
            _A			     =	"auto"
            _A			     =	"ddp"

      _A			     =	args.accumulate_grad_batches
      _A			     =	None
      _A			     =	"auto"

      _A			     =	pl.Trainer.from_argparse_args(
          __lowercase   ,  weights_summary=__lowercase   ,  callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]   ,  logger=__lowercase   ,  val_check_interval=1   ,  num_sanity_val_steps=2   ,  **__lowercase   ,  )

      if args.do_train:
            trainer.fit(__lowercase       )

      else:
            print("RAG modeling tests with new set functions successfuly executed!"       )
      return trainer













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					:   str							,   SCREAMING_SNAKE_CASE					:   int = 1_28							,   SCREAMING_SNAKE_CASE					:   int = 2_56							,   SCREAMING_SNAKE_CASE					:   float = 2_0_0_0.0							,   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							,   ):


      '''simple docstring'''






      super().__init__()

      UpperCamelCase__			:   Optional[Any]       				=  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()							,   )

      UpperCamelCase__			:   Optional[int]       				=  nn.Embedding(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  False

      UpperCamelCase__			:   Union[str, Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  nn.ModuleList()
      for lyr_num in range(SCREAMING_SNAKE_CASE						):
           # FiLM conditional T5 decoder
           UpperCamelCase__			:   Optional[int]       				=  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						)

      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(p=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

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

      UpperCamelCase__			:   Any       				=  self.conditioning_emb(SCREAMING_SNAKE_CASE						).unsqueeze(1						)

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

      UpperCamelCase__			:   Optional[int]       				=  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__			:   Optional[int]       				=  torch.broadcast_to(
          torch.arange(SCREAMING_SNAKE_CASE							,   device=decoder_input_tokens.device						)							,   (batch, seq_length)							,   )

      UpperCamelCase__			:   Dict       				=  self.position_encoding(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  self.continuous_inputs_projection(SCREAMING_SNAKE_CASE						)
      inputs += position_encodings
      UpperCamelCase__			:   Optional[Any]       				=  self.dropout(SCREAMING_SNAKE_CASE						)

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

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

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

      for lyr in self.decoders:
           UpperCamelCase__			:   int       				=  lyr(
               SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   encoder_attention_mask=SCREAMING_SNAKE_CASE							,   )[0]

      UpperCamelCase__			:   Tuple       				=  self.decoder_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  self.post_dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.spec_out(SCREAMING_SNAKE_CASE						)
      return spec_out







class 			__a  (    nn.Module       ):







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  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 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  self.layer[0](
          SCREAMING_SNAKE_CASE							,   conditioning_emb=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      if encoder_hidden_states is not None:
           UpperCamelCase__			:   int       				=  torch.where(encoder_attention_mask > 0							,   0							,   -1e1_0						).to(
               encoder_hidden_states.dtype						)

           UpperCamelCase__			:   Tuple       				=  self.layer[1](
               SCREAMING_SNAKE_CASE							,   key_value_states=SCREAMING_SNAKE_CASE							,   attention_mask=SCREAMING_SNAKE_CASE							,   )

      # Apply Film Conditional Feed Forward layer
      UpperCamelCase__			:   Any       				=  self.layer[-1](SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      return (hidden_states,)







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  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						)
      UpperCamelCase__			:   Any       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   List[Any]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)

      if conditioning_emb is not None:
           UpperCamelCase__			:   List[Any]       				=  self.FiLMLayer(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      # Self-attention block
      UpperCamelCase__			:   Optional[Any]       				=  self.attention(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)

      return hidden_states







class 			__a  (    nn.Module       ):







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


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   str       				=  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						)
      UpperCamelCase__			:   Union[str, Any]       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  self.attention(
          SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   attention_mask=attention_mask.squeeze(1						)							,   )
      UpperCamelCase__			:   Optional[Any]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return layer_output







class 			__a  (    nn.Module       ):







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


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Any       				=  TaDenseGatedActDense(d_model=SCREAMING_SNAKE_CASE							,   d_ff=SCREAMING_SNAKE_CASE							,   dropout_rate=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  TaFiLMLayer(in_features=d_model * 4							,   out_features=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  TaLayerNorm(SCREAMING_SNAKE_CASE							,   eps=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict=None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.layer_norm(SCREAMING_SNAKE_CASE						)
      if conditioning_emb is not None:
           UpperCamelCase__			:   Optional[int]       				=  self.film(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[int]       				=  self.DenseReluDense(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  hidden_states + self.dropout(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   str						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Tuple       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   bias=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  nn.Dropout(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  NewGELUActivation()







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.act(self.wi_a(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Dict       				=  self.wi_a(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  hidden_gelu * hidden_linear
      UpperCamelCase__			:   int       				=  self.dropout(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  self.wo(SCREAMING_SNAKE_CASE						)
      return hidden_states







class 			__a  (    nn.Module       ):







 def __init__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   str=1e-6						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   List[str]       				=  nn.Parameter(torch.ones(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Any       				=  eps







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  hidden_states.to(torch.floataa						).pow(2						).mean(-1							,   keepdim=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  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 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   torch.Tensor						):


      '''simple docstring'''






      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.0_4_4_7_1_5 * torch.pow(SCREAMING_SNAKE_CASE							,   3.0						))						))







class 			__a  (    nn.Module       ):







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


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   int       				=  nn.Linear(SCREAMING_SNAKE_CASE							,   out_features * 2							,   bias=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.scale_bias(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  torch.chunk(SCREAMING_SNAKE_CASE							,   2							,   -1						)
      UpperCamelCase__			:   Dict       				=  x * (1 + scale) + shift
      return x