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__					(     lowercase__							,lowercase__ ):





	'''simple docstring'''



	@register_to_config
	def __init__(  self     :   str      ,  lowercase     :   Any = 128      ,  lowercase     :   Union[str, Any] = 256      ,  lowercase     :   Optional[int] = 2_000.0      ,  lowercase     :   Any = 768      ,  lowercase     :   int = 12      ,  lowercase     :   List[Any] = 12      ,  lowercase     :   str = 64      ,  lowercase     :   List[Any] = 2_048      ,  lowercase     :   List[Any] = 0.1      ,  ):


				'''simple docstring'''


				super().__init__()

				_snake_case          =      nn.Sequential(
				    nn.Linear(lowercase_      ,  d_model * 4      ,  bias=lowercase_     )      ,  nn.SiLU()      ,  nn.Linear(d_model * 4      ,  d_model * 4      ,  bias=lowercase_     )      ,  nn.SiLU()      ,  )

				_snake_case          =      nn.Embedding(lowercase_      ,  lowercase_     )
				_snake_case          =      False

				_snake_case          =      nn.Linear(lowercase_      ,  lowercase_      ,  bias=lowercase_     )

				_snake_case          =      nn.Dropout(p=lowercase_     )

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

				_snake_case          =      TaLayerNorm(lowercase_     )

				_snake_case          =      nn.Dropout(p=lowercase_     )
				_snake_case          =      nn.Linear(lowercase_      ,  lowercase_      ,  bias=lowercase_     )



	def 			A							(  self     :   str      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Any     ):


				'''simple docstring'''


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





	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      decoder_input_tokens.shape
				assert decoder_noise_time.shape == (batch,)

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

				_snake_case          =      self.conditioning_emb(lowercase_     ).unsqueeze(1     )

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

				_snake_case          =      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.
				_snake_case          =      torch.broadcast_to(
				    torch.arange(lowercase_      ,  device=decoder_input_tokens.device     )      ,  (batch, seq_length)      ,  )

				_snake_case          =      self.position_encoding(lowercase_     )

				_snake_case          =      self.continuous_inputs_projection(lowercase_     )
				inputs += position_encodings
				_snake_case          =      self.dropout(lowercase_     )

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

				# Translate encoding masks to encoder-decoder masks.
				_snake_case          =      [(x, self.encoder_decoder_mask(lowercase_      ,  lowercase_     )) for x, y in encodings_and_masks]

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

				for lyr in self.decoders:
							_snake_case          =      lyr(
							    lowercase_      ,  conditioning_emb=lowercase_      ,  encoder_hidden_states=lowercase_      ,  encoder_attention_mask=lowercase_      ,  )[0]

				_snake_case          =      self.decoder_norm(lowercase_     )
				_snake_case          =      self.post_dropout(lowercase_     )

				_snake_case          =      self.spec_out(lowercase_     )
				return spec_out




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



	def __init__(  self     :   Optional[int]      ,  lowercase     :   Dict      ,  lowercase     :   Optional[int]      ,  lowercase     :   List[str]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Tuple      ,  lowercase     :   int=1E-6     ):


				'''simple docstring'''


				super().__init__()
				_snake_case          =      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[str]      ,  lowercase     :   List[Any]      ,  lowercase     :   Dict=None      ,  lowercase     :   List[Any]=None      ,  lowercase     :   int=None      ,  lowercase     :   str=None      ,  lowercase     :   List[str]=None      ,  ):


				'''simple docstring'''


				_snake_case          =      self.layer[0](
				    lowercase_      ,  conditioning_emb=lowercase_      ,  attention_mask=lowercase_      ,  )

				if encoder_hidden_states is not None:
							_snake_case          =      torch.where(encoder_attention_mask > 0      ,  0      ,  -1E10     ).to(
							    encoder_hidden_states.dtype     )

							_snake_case          =      self.layer[1](
							    lowercase_      ,  key_value_states=lowercase_      ,  attention_mask=lowercase_      ,  )

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

				return (hidden_states,)




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



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


				'''simple docstring'''


				super().__init__()
				_snake_case          =      TaLayerNorm(lowercase_     )
				_snake_case          =      TaFiLMLayer(in_features=d_model * 4      ,  out_features=lowercase_     )
				_snake_case          =      Attention(query_dim=lowercase_      ,  heads=lowercase_      ,  dim_head=lowercase_      ,  out_bias=lowercase_      ,  scale_qk=lowercase_     )
				_snake_case          =      nn.Dropout(lowercase_     )





	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   str=None      ,  lowercase     :   List[Any]=None      ,  ):


				'''simple docstring'''


				_snake_case          =      self.layer_norm(lowercase_     )

				if conditioning_emb is not None:
							_snake_case          =      self.FiLMLayer(lowercase_      ,  lowercase_     )

				# Self-attention block
				_snake_case          =      self.attention(lowercase_     )

				_snake_case          =      hidden_states + self.dropout(lowercase_     )

				return hidden_states




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



	def __init__(  self     :   str      ,  lowercase     :   Any      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Dict      ,  lowercase     :   Optional[int]      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				super().__init__()
				_snake_case          =      Attention(query_dim=lowercase_      ,  heads=lowercase_      ,  dim_head=lowercase_      ,  out_bias=lowercase_      ,  scale_qk=lowercase_     )
				_snake_case          =      TaLayerNorm(lowercase_      ,  eps=lowercase_     )
				_snake_case          =      nn.Dropout(lowercase_     )





	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Any      ,  lowercase     :   Dict=None      ,  lowercase     :   Union[str, Any]=None      ,  ):


				'''simple docstring'''


				_snake_case          =      self.layer_norm(lowercase_     )
				_snake_case          =      self.attention(
				    lowercase_      ,  encoder_hidden_states=lowercase_      ,  attention_mask=attention_mask.squeeze(1     )      ,  )
				_snake_case          =      hidden_states + self.dropout(lowercase_     )
				return layer_output




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



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


				'''simple docstring'''


				super().__init__()
				_snake_case          =      TaDenseGatedActDense(d_model=lowercase_      ,  d_ff=lowercase_      ,  dropout_rate=lowercase_     )
				_snake_case          =      TaFiLMLayer(in_features=d_model * 4      ,  out_features=lowercase_     )
				_snake_case          =      TaLayerNorm(lowercase_      ,  eps=lowercase_     )
				_snake_case          =      nn.Dropout(lowercase_     )





	def 			A							(  self     :   Optional[int]      ,  lowercase     :   str      ,  lowercase     :   Optional[int]=None     ):


				'''simple docstring'''


				_snake_case          =      self.layer_norm(lowercase_     )
				if conditioning_emb is not None:
							_snake_case          =      self.film(lowercase_      ,  lowercase_     )

				_snake_case          =      self.DenseReluDense(lowercase_     )
				_snake_case          =      hidden_states + self.dropout(lowercase_     )
				return hidden_states




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



	def __init__(  self     :   Dict      ,  lowercase     :   Dict      ,  lowercase     :   Tuple      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				super().__init__()
				_snake_case          =      nn.Linear(lowercase_      ,  lowercase_      ,  bias=lowercase_     )
				_snake_case          =      nn.Linear(lowercase_      ,  lowercase_      ,  bias=lowercase_     )
				_snake_case          =      nn.Linear(lowercase_      ,  lowercase_      ,  bias=lowercase_     )
				_snake_case          =      nn.Dropout(lowercase_     )
				_snake_case          =      NewGELUActivation()





	def 			A							(  self     :   str      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.act(self.wi_a(lowercase_     )     )
				_snake_case          =      self.wi_a(lowercase_     )
				_snake_case          =      hidden_gelu * hidden_linear
				_snake_case          =      self.dropout(lowercase_     )

				_snake_case          =      self.wo(lowercase_     )
				return hidden_states




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



	def __init__(  self     :   str      ,  lowercase     :   List[Any]      ,  lowercase     :   Tuple=1E-6     ):


				'''simple docstring'''


				super().__init__()
				_snake_case          =      nn.Parameter(torch.ones(lowercase_     )     )
				_snake_case          =      eps





	def 			A							(  self     :   List[str]      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      hidden_states.to(torch.floataa     ).pow(2     ).mean(-1      ,  keepdim=lowercase_     )
				_snake_case          =      hidden_states * torch.rsqrt(variance + self.variance_epsilon     )

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

				return self.weight * hidden_states




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



	def 			A							(  self     :   List[str]      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.044715 * torch.pow(lowercase_      ,  3.0     ))     ))




class        SCREAMING_SNAKE_CASE__					(     nn.Module ):





	'''simple docstring'''



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


				'''simple docstring'''


				super().__init__()
				_snake_case          =      nn.Linear(lowercase_      ,  out_features * 2      ,  bias=lowercase_     )





	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.scale_bias(lowercase_     )
				_snake_case          =      torch.chunk(lowercase_      ,  2      ,  -1     )
				_snake_case          =      x * (1 + scale) + shift
				return x

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


_lowerCamelCase	:    str          =      logging.getLogger(__name__)




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "sequence-classification"



	def __init__(  self     :   Optional[int]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				if type(lowercase     ) == dict:
							_snake_case          =      Namespace(**lowercase     )
				_snake_case          =      glue_output_modes[hparams.task]
				_snake_case          =      glue_tasks_num_labels[hparams.task]

				super().__init__(lowercase      ,  lowercase      ,  self.mode     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				return self.model(**lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

				if self.config.model_type not in ["distilbert", "bart"]:
							_snake_case          =      batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

				_snake_case          =      self(**lowercase     )
				_snake_case          =      outputs[0]

				_snake_case          =      self.trainer.lr_schedulers[0]['scheduler']
				_snake_case          =      {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
				return {"loss": loss, "log": tensorboard_logs}



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.hparams
				_snake_case          =      processors[args.task]()
				_snake_case          =      processor.get_labels()

				for mode in ["train", "dev"]:
							_snake_case          =      self._feature_file(lowercase     )
							if os.path.exists(lowercase     ) and not args.overwrite_cache:
										logger.info('Loading features from cached file %s'      ,  lowercase     )
							else:
										logger.info('Creating features from dataset file at %s'      ,  args.data_dir     )
										_snake_case          =      (
										    processor.get_dev_examples(args.data_dir     )
										    if mode == 'dev'
										    else processor.get_train_examples(args.data_dir     )
										)
										_snake_case          =      convert_examples_to_features(
										    lowercase      ,  self.tokenizer      ,  max_length=args.max_seq_length      ,  label_list=self.labels      ,  output_mode=args.glue_output_mode      ,  )
										logger.info('Saving features into cached file %s'      ,  lowercase     )
										torch.save(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   int      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				_snake_case          =      'dev' if mode == 'test' else mode

				_snake_case          =      self._feature_file(lowercase     )
				logger.info('Loading features from cached file %s'      ,  lowercase     )
				_snake_case          =      torch.load(lowercase     )
				_snake_case          =      torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.token_type_ids for f in features]      ,  dtype=torch.long     )
				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.long     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.float     )

				return DataLoader(
				    TensorDataset(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )      ,  batch_size=lowercase      ,  shuffle=lowercase      ,  )



	def 			A							(  self     :   str      ,  lowercase     :   Dict      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

				if self.config.model_type not in ["distilbert", "bart"]:
							_snake_case          =      batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

				_snake_case          =      self(**lowercase     )
				_snake_case     , _snake_case          =      outputs[:2]
				_snake_case          =      logits.detach().cpu().numpy()
				_snake_case          =      inputs['labels'].detach().cpu().numpy()

				return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      torch.stack([x['val_loss'] for x in outputs]     ).mean().detach().cpu().item()
				_snake_case          =      np.concatenate([x['pred'] for x in outputs]      ,  axis=0     )

				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      np.argmax(lowercase      ,  axis=1     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      np.squeeze(lowercase     )

				_snake_case          =      np.concatenate([x['target'] for x in outputs]      ,  axis=0     )
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]

				_snake_case          =      {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task      ,  lowercase      ,  lowercase     )}

				_snake_case          =      dict(results.items()     )
				_snake_case          =      results
				return ret, preds_list, out_label_list



	def 			A							(  self     :   List[str]      ,  lowercase     :   list     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      ret['log']
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



	def 			A							(  self     :   Tuple      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      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 			A							(  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				BaseTransformer.add_model_specific_args(lowercase      ,  lowercase     )
				parser.add_argument(
				    '--max_seq_length'      ,  default=128      ,  type=lowercase      ,  help=(
				        'The maximum total input sequence length after tokenization. Sequences longer '
				        'than this will be truncated, sequences shorter will be padded.'
				    )      ,  )

				parser.add_argument(
				    '--task'      ,  default=''      ,  type=lowercase      ,  required=lowercase      ,  help='The GLUE task to run'      ,  )
				parser.add_argument(
				    '--gpus'      ,  default=0      ,  type=lowercase      ,  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






def 	a_		(	)			->  List[str]:
			_snake_case          =      argparse.ArgumentParser()
			add_generic_args(__lowercase	, os.getcwd()			)
			_snake_case          =      GLUETransformer.add_model_specific_args(__lowercase	, os.getcwd()			)
			_snake_case          =      parser.parse_args()

			# If output_dir not provided, a folder will be generated in pwd
			if args.output_dir is None:
						_snake_case          =      os.path.join(
						    './results'	, f'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'			)}'''	, )
						os.makedirs(args.output_dir			)

			_snake_case          =      GLUETransformer(__lowercase			)
			_snake_case          =      generic_train(__lowercase	, __lowercase			)

			# Optionally, predict on dev set and write to output_dir
			if args.do_predict:
						_snake_case          =      sorted(glob.glob(os.path.join(args.output_dir	, 'checkpoint-epoch=*.ckpt'			)	, recursive=__lowercase			)			)
						_snake_case          =      model.load_from_checkpoint(checkpoints[-1]			)
						return trainer.test(__lowercase			)


if __name__ == "__main__":
			main()