


"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A:     Optional[Any]										=	logging.getLogger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[Any]		=		'token-classification'

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    if type(_SCREAMING_SNAKE_CASE		) == dict:
     UpperCAmelCase			:      Union[str, Any]			=			Namespace(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			import_module("""tasks"""		)
    try:
     UpperCAmelCase			:      Any			=			getattr(_SCREAMING_SNAKE_CASE   ,  hparams.task_type		)
     UpperCAmelCase			:      TokenClassificationTask			=			token_classification_task_clazz()
    except AttributeError:
     raise ValueError(
         F"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
         F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"		)
    UpperCAmelCase			:      List[str]			=			self.token_classification_task.get_labels(hparams.labels		)
    UpperCAmelCase			:      Dict			=			CrossEntropyLoss().ignore_index
    super().__init__(_SCREAMING_SNAKE_CASE   ,  len(self.labels		)   ,  self.mode		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    return self.model(**_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     UpperCAmelCase			:      Dict			=			(
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids

    UpperCAmelCase			:      Optional[Any]			=			self(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			outputs[0]
    # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
    return {"loss": loss}

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.hparams
    for mode in ["train", "dev", "test"]:
     UpperCAmelCase			:      List[Any]			=			self._feature_file(_SCREAMING_SNAKE_CASE		)
     if os.path.exists(_SCREAMING_SNAKE_CASE		) and not args.overwrite_cache:
      logger.info("""Loading features from cached file %s"""   ,  _SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Optional[Any]			=			torch.load(_SCREAMING_SNAKE_CASE		)
     else:
      logger.info("""Creating features from dataset file at %s"""   ,  args.data_dir		)
      UpperCAmelCase			:      int			=			self.token_classification_task.read_examples_from_file(args.data_dir   ,  _SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Dict			=			self.token_classification_task.convert_examples_to_features(
          _SCREAMING_SNAKE_CASE   ,  self.labels   ,  args.max_seq_length   ,  self.tokenizer   ,  cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]		)   ,  cls_token=self.tokenizer.cls_token   ,  cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0   ,  sep_token=self.tokenizer.sep_token   ,  sep_token_extra=_SCREAMING_SNAKE_CASE   ,  pad_on_left=bool(self.config.model_type in ["""xlnet"""]		)   ,  pad_token=self.tokenizer.pad_token_id   ,  pad_token_segment_id=self.tokenizer.pad_token_type_id   ,  pad_token_label_id=self.pad_token_label_id   ,  )
      logger.info("""Saving features into cached file %s"""   ,  _SCREAMING_SNAKE_CASE		)
      torch.save(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = False		)						->   DataLoader:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self._feature_file(_SCREAMING_SNAKE_CASE		)
    logger.info("""Loading features from cached file %s"""   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			torch.load(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			torch.tensor([f.input_ids for f in features]   ,  dtype=torch.long		)
    UpperCAmelCase			:      Tuple			=			torch.tensor([f.attention_mask for f in features]   ,  dtype=torch.long		)
    if features[0].token_type_ids is not None:
     UpperCAmelCase			:      List[str]			=			torch.tensor([f.token_type_ids for f in features]   ,  dtype=torch.long		)
    else:
     UpperCAmelCase			:      Any			=			torch.tensor([0 for f in features]   ,  dtype=torch.long		)
     # HACK(we will not use this anymore soon)
    UpperCAmelCase			:      Any			=			torch.tensor([f.label_ids for f in features]   ,  dtype=torch.long		)
    return DataLoader(
        TensorDataset(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  batch_size=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    """Compute validation""" ""
    UpperCAmelCase			:      Optional[Any]			=			{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     UpperCAmelCase			:      List[Any]			=			(
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids
    UpperCAmelCase			:      int			=			self(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			outputs[:2]
    UpperCAmelCase			:      Dict			=			logits.detach().cpu().numpy()
    UpperCAmelCase			:      Optional[Any]			=			inputs["""labels"""].detach().cpu().numpy()
    return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			torch.stack([x["""val_loss"""] for x in outputs]		).mean()
    UpperCAmelCase			:      int			=			np.concatenate([x["""pred"""] for x in outputs]   ,  axis=0		)
    UpperCAmelCase			:      Optional[int]			=			np.argmax(_SCREAMING_SNAKE_CASE   ,  axis=2		)
    UpperCAmelCase			:      Dict			=			np.concatenate([x["""target"""] for x in outputs]   ,  axis=0		)

    UpperCAmelCase			:      Optional[int]			=			dict(enumerate(self.labels		)		)
    UpperCAmelCase			:      Any			=			[[] for _ in range(out_label_ids.shape[0]		)]
    UpperCAmelCase			:      Dict			=			[[] for _ in range(out_label_ids.shape[0]		)]

    for i in range(out_label_ids.shape[0]		):
     for j in range(out_label_ids.shape[1]		):
      if out_label_ids[i, j] != self.pad_token_label_id:
       out_label_list[i].append(label_map[out_label_ids[i][j]]		)
       preds_list[i].append(label_map[preds[i][j]]		)

    UpperCAmelCase			:      int			=			{
        """val_loss""": val_loss_mean,
        """accuracy_score""": accuracy_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
        """precision""": precision_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
        """recall""": recall_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
        """f1""": fa_score(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		),
    }

    UpperCAmelCase			:      int			=			dict(results.items()		)
    UpperCAmelCase			:      Optional[int]			=			results
    return ret, preds_list, out_label_list

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self._eval_end(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			ret["""log"""]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self._eval_end(_SCREAMING_SNAKE_CASE		)

    # Converting to the dict required by pl
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
    # pytorch_lightning/trainer/logging.py#L139
    UpperCAmelCase			:      Any			=			ret["""log"""]
    # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
    return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




   @staticmethod
   def 			SCREAMING_SNAKE_CASE  (     _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    BaseTransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    parser.add_argument(
        """--task_type"""   ,  default="""NER"""   ,  type=_SCREAMING_SNAKE_CASE   ,  help="""Task type to fine tune in training (e.g. NER, POS, etc)"""		)
    parser.add_argument(
        """--max_seq_length"""   ,  default=128   ,  type=_SCREAMING_SNAKE_CASE   ,  help=(
            """The maximum total input sequence length after tokenization. Sequences longer """
            """than this will be truncated, sequences shorter will be padded."""
        )   ,  )

    parser.add_argument(
        """--labels"""   ,  default=""""""   ,  type=_SCREAMING_SNAKE_CASE   ,  help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""   ,  )
    parser.add_argument(
        """--gpus"""   ,  default=0   ,  type=_SCREAMING_SNAKE_CASE   ,  help="""The number of GPUs allocated for this, it is by default 0 meaning none"""   ,  )

    parser.add_argument(
        """--overwrite_cache"""   ,  action="""store_true"""   ,  help="""Overwrite the cached training and evaluation sets"""		)

    return parser


if __name__ == "__main__":
      A:     List[str]										=	argparse.ArgumentParser()
      add_generic_args(parser, os.getcwd())
      A:     Optional[int]										=	NERTransformer.add_model_specific_args(parser, os.getcwd())
      A:     Tuple										=	parser.parse_args()
      A:     Optional[int]										=	NERTransformer(args)
      A:     Dict										=	generic_train(model, args)

      if args.do_predict:
            # See https://github.com/huggingface/transformers/issues/3159
            # pl use this default format to create a checkpoint:
            # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
            # /pytorch_lightning/callbacks/model_checkpoint.py#L322
            A:     Union[str, Any]										=	sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
            A:     Union[str, Any]										=	model.load_from_checkpoint(checkpoints[-1])
            trainer.test(model)





"""simple docstring"""
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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__				):

   @register_to_config
   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = 128   ,  _SCREAMING_SNAKE_CASE = 256   ,  _SCREAMING_SNAKE_CASE = 2000.0   ,  _SCREAMING_SNAKE_CASE = 768   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 64   ,  _SCREAMING_SNAKE_CASE = 2048   ,  _SCREAMING_SNAKE_CASE = 0.1   ,  )						->   int:





    '''simple docstring'''




    super().__init__()

    UpperCAmelCase			:      Tuple			=			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			:      Tuple			=			nn.Embedding(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			False

    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.ModuleList()
    for lyr_num in range(_SCREAMING_SNAKE_CASE		):
     # FiLM conditional T5 decoder
     UpperCAmelCase			:      List[Any]			=			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			:      str			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCAmelCase			:      Union[str, Any]			=			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			:      Dict			=			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			:      int			=			torch.broadcast_to(
        torch.arange(_SCREAMING_SNAKE_CASE   ,  device=decoder_input_tokens.device		)   ,  (batch, seq_length)   ,  )

    UpperCAmelCase			:      List[str]			=			self.position_encoding(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			self.continuous_inputs_projection(_SCREAMING_SNAKE_CASE		)
    inputs += position_encodings
    UpperCAmelCase			:      List[str]			=			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			:      Optional[Any]			=			torch.cat([x[0] for x in encodings_and_encdec_masks]   ,  dim=1		)
    UpperCAmelCase			:      Union[str, Any]			=			torch.cat([x[1] for x in encodings_and_encdec_masks]   ,  dim=-1		)

    for lyr in self.decoders:
     UpperCAmelCase			:      List[Any]			=			lyr(
         _SCREAMING_SNAKE_CASE   ,  conditioning_emb=_SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  encoder_attention_mask=_SCREAMING_SNAKE_CASE   ,  )[0]

    UpperCAmelCase			:      List[Any]			=			self.decoder_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.post_dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.spec_out(_SCREAMING_SNAKE_CASE		)
    return spec_out



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

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





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[Any]			=			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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      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			:      Tuple			=			torch.where(encoder_attention_mask > 0   ,  0   ,  -1E10		).to(
         encoder_hidden_states.dtype		)

     UpperCAmelCase			:      List[Any]			=			self.layer[1](
         _SCREAMING_SNAKE_CASE   ,  key_value_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

    # Apply Film Conditional Feed Forward layer
    UpperCAmelCase			:      Dict			=			self.layer[-1](_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    return (hidden_states,)



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Union[str, Any]			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			TaFiLMLayer(in_features=d_model * 4   ,  out_features=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[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			:      List[Any]			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)

    if conditioning_emb is not None:
     UpperCAmelCase			:      str			=			self.FiLMLayer(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    # Self-attention block
    UpperCAmelCase			:      List[Any]			=			self.attention(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)

    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[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			:      Dict			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.attention(
        _SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=attention_mask.squeeze(1		)   ,  )
    UpperCAmelCase			:      Dict			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return layer_output



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      int			=			TaDenseGatedActDense(d_model=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			TaFiLMLayer(in_features=d_model * 4   ,  out_features=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    if conditioning_emb is not None:
     UpperCAmelCase			:      Union[str, Any]			=			self.film(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.DenseReluDense(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Any			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			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			:      Tuple			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			NewGELUActivation()




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.act(self.wi_a(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			self.wi_a(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			hidden_gelu * hidden_linear
    UpperCAmelCase			:      int			=			self.dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			self.wo(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

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





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			nn.Parameter(torch.ones(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			eps




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			hidden_states.to(torch.floataa		).pow(2		).mean(-1   ,  keepdim=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

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

    return self.weight * hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   torch.Tensor:





    '''simple docstring'''




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



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Union[str, Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  out_features * 2   ,  bias=_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.scale_bias(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			torch.chunk(_SCREAMING_SNAKE_CASE   ,  2   ,  -1		)
    UpperCAmelCase			:      Optional[int]			=			x * (1 + scale) + shift
    return x
