



'''simple docstring'''




import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple=32 ,   _lowerCamelCase:     Optional[Any]=10 ,   _lowerCamelCase:     int=1_00 ,   _lowerCamelCase:     str=10_26 ,   _lowerCamelCase:     int=True ,   _lowerCamelCase:     List[Any]="data/tokenized_stories_train_wikitext103.jbl" ,   _lowerCamelCase:     int="igf_context_pairs.jbl" ,   ):
		set_seed(3      )
		# generate train_data and objective_set
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	generate_datasets(
		    _lowerCamelCase ,   _lowerCamelCase ,   number=_lowerCamelCase ,   min_len=10_26 ,   trim=_lowerCamelCase      )
		# keeps model same across runs
		set_seed(4      )
		# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
		# can we train on GPU?
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )

		# load pretrained model
		__SCREAMING_SNAKE_CASE :	int					 =	load_gpta("""gpt2"""      ).to(_lowerCamelCase      )
		print("""computing perplexity on objective set"""      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	compute_perplexity(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      ).item()
		print("""perplexity on objective set:""" ,   _lowerCamelCase      )

		# collect igf pairs and save to file demo.jbl
		collect_objective_set(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )

		# clean up, delete model and data we don't need anymore
		del model, train_data, objective_set
		torch.cuda.empty_cache()


def        lowerCAmelCase_     (			_lowerCamelCase:     Dict ,   _lowerCamelCase:     List[str]=15 ,   _lowerCamelCase:     Union[str, Any]=1_28 ,   _lowerCamelCase:     List[str]=1_00 ,   _lowerCamelCase:     List[Any]="igf_model.pt" ,   ):
		set_seed(42      )

		# Load pre-trained model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	GPTaLMHeadModel.from_pretrained("""gpt2"""      )

		# Initialize secondary learner to use embedding weights of model
		__SCREAMING_SNAKE_CASE :	List[str]					 =	SecondaryLearner(_lowerCamelCase      )

		# Train secondary learner
		__SCREAMING_SNAKE_CASE :	Tuple					 =	train_secondary_learner(
		    _lowerCamelCase ,   _lowerCamelCase ,   max_epochs=_lowerCamelCase ,   batch_size=_lowerCamelCase ,   eval_freq=1_00 ,   igf_model_path=_lowerCamelCase ,   )

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     List[Any] ,   _lowerCamelCase:     Tuple ,   _lowerCamelCase:     List[str]=32 ,   _lowerCamelCase:     Optional[int]=10_00 ,   _lowerCamelCase:     Optional[int]=16 ,   _lowerCamelCase:     List[Any]=1.0 ,   _lowerCamelCase:     List[str]=recopy_gpta ,   _lowerCamelCase:     Optional[Any]=None ,   _lowerCamelCase:     Tuple=10 ,   _lowerCamelCase:     int="gpt2_finetuned.pt" ,   ):
		__SCREAMING_SNAKE_CASE :	Any					 =	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	RandomSampler(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	DataLoader(_lowerCamelCase ,   sampler=_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	int					 =	max_steps // (len(_lowerCamelCase      )) + 1
		__SCREAMING_SNAKE_CASE :	List[str]					 =	0
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.zeros((1, context_len) ,   dtype=torch.long ,   device=_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	recopy_model(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )

		model.train()
		if secondary_learner is not None:
				secondary_learner.to(_lowerCamelCase      )
				secondary_learner.eval()
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	[]
		__SCREAMING_SNAKE_CASE :	Dict					 =	0

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	[]
		__SCREAMING_SNAKE_CASE :	List[str]					 =	[]

		# Compute the performance of the transformer model at the beginning
		__SCREAMING_SNAKE_CASE :	List[str]					 =	compute_perplexity(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )
		test_perps.append(_lowerCamelCase      )
		print("""Test perplexity, step""" ,   _lowerCamelCase ,   """:""" ,   _lowerCamelCase      )
		for epoch in range(int(_lowerCamelCase      )      ):
				for step, example in enumerate(_lowerCamelCase      ):
						torch.cuda.empty_cache()
						__SCREAMING_SNAKE_CASE :	Any					 =	random.randint(0 ,   example.size(2      ) - context_len - 1      )
						__SCREAMING_SNAKE_CASE :	int					 =	example[0, 0, start : start + context_len]
						lm_optimizer.zero_grad()
						__SCREAMING_SNAKE_CASE :	int					 =	model(_lowerCamelCase ,   labels=_lowerCamelCase      )
						__SCREAMING_SNAKE_CASE :	Dict					 =	True

						if secondary_learner is not None:
								__SCREAMING_SNAKE_CASE :	int					 =	secondary_learner.forward(
								    torch.tensor(_lowerCamelCase ,   dtype=torch.long ,   device=_lowerCamelCase      ).unsqueeze(0      )      )[0].item()
								observed_qs.append(float(_lowerCamelCase      )      )

								# Here we implement the simple non-constant threshold for the predicted IG(X) value
								# We will decay the selectivity of our secondary learner filter from
								# 1 standard deviation above average to 1 below average after 10 batches.

								if global_step == 10:
										__SCREAMING_SNAKE_CASE :	Any					 =	-1
								if predicted_q < threshold:
										__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()      )      )
								__SCREAMING_SNAKE_CASE :	str					 =	outputs[0]
								lm_loss.backward()
								examples += 1

						del outputs

						# Once the batch is filled with enough contexts, backprop on the batch.
						if examples == batch_size:
								torch.cuda.empty_cache()
								__SCREAMING_SNAKE_CASE :	Tuple					 =	0
								# Do LM backprop
								torch.nn.utils.clip_grad_norm_(model.parameters() ,   3.0      )
								lm_optimizer.step()
								lm_scheduler.step()  # Update learning rate schedule
								global_step += 1
								# Compute the performance of the transformer model at this batch
								if global_step % eval_interval == 0:
										__SCREAMING_SNAKE_CASE :	List[Any]					 =	compute_perplexity(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )
										test_perps.append(_lowerCamelCase      )

										print("""Test perplexity, step""" ,   _lowerCamelCase ,   """:""" ,   _lowerCamelCase      )
            # Break out of the loop after 60 batches
						if max_steps > 0 and global_step > 60:
								break
				if max_steps > 0 and global_step > 60:
						break

    # save finetuned transformer model
		torch.save(model.state_dict() ,   _lowerCamelCase      )
		torch.cuda.empty_cache()
		# Do some cleaning up so we can reinitialize for the next run of this function
		del lm_optimizer
		del lm_scheduler
		return model


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""      )

		# Required parameters
		parser.add_argument(
		    """--data_dir""" ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help="""The input data dir. Should contain data files for WikiText.""" ,   )
		parser.add_argument(
		    """--model_name_or_path""" ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help="""Path to pretrained model or model identifier from huggingface.co/models""" ,   )
		parser.add_argument(
		    """--data_file""" ,   type=_lowerCamelCase ,   default=_lowerCamelCase ,   help=(
		        """A jbl file containing tokenized data which can be split as objective dataset, """
		        """train_dataset and test_dataset."""
		    ) ,   )

		parser.add_argument(
		    """--igf_data_file""" ,   type=_lowerCamelCase ,   default=_lowerCamelCase ,   help="""A jbl file containing the context and information gain pairs to train secondary learner.""" ,   )

		parser.add_argument(
		    """--output_dir""" ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help="""The output directory where the final fine-tuned model is stored.""" ,   )

		parser.add_argument(
		    """--tokenizer_name""" ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   help="""Pretrained tokenizer name or path if not the same as model_name""" ,   )
		parser.add_argument("""--seed""" ,   type=_lowerCamelCase ,   default=_lowerCamelCase ,   help="""A seed for reproducible training."""      )

		parser.add_argument(
		    """--context_len""" ,   default=32 ,   type=_lowerCamelCase ,   help=(
		        """The maximum total input sequence length after tokenization. Sequences longer """
		        """than this will be truncated, sequences shorter will be padded."""
		    ) ,   )

		parser.add_argument(
		    """--size_objective_set""" ,   default=1_00 ,   type=_lowerCamelCase ,   help="""number of articles that are long enough to be used as our objective set""" ,   )
		parser.add_argument(
		    """--eval_freq""" ,   default=1_00 ,   type=_lowerCamelCase ,   help="""secondary model evaluation is triggered at eval_freq"""      )

		parser.add_argument("""--max_steps""" ,   default=10_00 ,   type=_lowerCamelCase ,   help="""To calculate training epochs"""      )

		parser.add_argument(
		    """--secondary_learner_batch_size""" ,   default=1_28 ,   type=_lowerCamelCase ,   help="""batch size of training data for secondary learner""" ,   )

		parser.add_argument(
		    """--batch_size""" ,   default=16 ,   type=_lowerCamelCase ,   help="""batch size of training data of language model(gpt2) """      )

		parser.add_argument(
		    """--eval_interval""" ,   default=10 ,   type=_lowerCamelCase ,   help=(
		        """decay the selectivity of our secondary learner filter from"""
		        """1 standard deviation above average to 1 below average after 10 batches"""
		    ) ,   )

		parser.add_argument(
		    """--number""" ,   default=1_00 ,   type=_lowerCamelCase ,   help="""The number of examples split to be used as objective_set/test_data"""      )

		parser.add_argument(
		    """--min_len""" ,   default=10_26 ,   type=_lowerCamelCase ,   help="""The minimum length of the article to be used as objective set"""      )

		parser.add_argument(
		    """--secondary_learner_max_epochs""" ,   default=15 ,   type=_lowerCamelCase ,   help="""number of epochs to train secondary learner"""      )

		parser.add_argument("""--trim""" ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   help="""truncate the example if it exceeds context length"""      )

		parser.add_argument(
		    """--threshold""" ,   default=1.0 ,   type=_lowerCamelCase ,   help=(
		        """The threshold value used by secondary learner to filter the train_data and allow only"""
		        """ informative data as input to the model"""
		    ) ,   )

		parser.add_argument("""--finetuned_model_name""" ,   default="""gpt2_finetuned.pt""" ,   type=_lowerCamelCase ,   help="""finetuned_model_name"""      )

		parser.add_argument(
		    """--recopy_model""" ,   default=_lowerCamelCase ,   type=_lowerCamelCase ,   help="""Reset the model to the original pretrained GPT-2 weights after each iteration""" ,   )

		# function calls
		# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
		generate_n_pairs(
		    context_len=32 ,   max_steps=10 ,   size_objective_set=1_00 ,   min_len=10_26 ,   trim=_lowerCamelCase ,   data_file="""data/tokenized_stories_train_wikitext103.jbl""" ,   igf_data_file="""igf_context_pairs.jbl""" ,   )

		# Load train data for secondary learner
		__SCREAMING_SNAKE_CASE :	str					 =	joblib.load("""data/IGF_values.jbl"""      )

		# Train secondary learner
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	training_secondary_learner(
		    _lowerCamelCase ,   secondary_learner_max_epochs=15 ,   secondary_learner_batch_size=1_28 ,   eval_freq=1_00 ,   igf_model_path="""igf_model.pt""" ,   )

		# load pretrained gpt2 model
		__SCREAMING_SNAKE_CASE :	Tuple					 =	GPTaLMHeadModel.from_pretrained("""gpt2"""      )
		set_seed(42      )

		# Generate train and test data to train and evaluate gpt2 model
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	generate_datasets(
		    context_len=32 ,   file="""data/tokenized_stories_train_wikitext103.jbl""" ,   number=1_00 ,   min_len=10_26 ,   trim=_lowerCamelCase      )

		# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
		finetune(
		    _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   context_len=32 ,   max_steps=10_00 ,   batch_size=16 ,   threshold=1.0 ,   recopy_model=_lowerCamelCase ,   secondary_learner=_lowerCamelCase ,   eval_interval=10 ,   finetuned_model_name="""gpt2_finetuned.pt""" ,   )


if __name__ == "__main__":
						main()




'''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 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					):


			'''simple docstring'''

			@register_to_config
			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 1_2_8							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 20_00.0							,  lowerCAmelCase__  :				int = 7_6_8							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 6_4							,  lowerCAmelCase__  :				int = 2_0_4_8							,  lowerCAmelCase__  :				float = 0.1							,  ):





					"""simple docstring"""






					super().__init__()

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Sequential(
					    nn.Linear(lowerCAmelCase__							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  nn.Linear(d_model * 4							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  )

					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Embedding(lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	False

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	nn.Dropout(p=lowerCAmelCase__		)

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

					__SCREAMING_SNAKE_CASE :	List[str]					 =	TaLayerNorm(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	nn.Dropout(p=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






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



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

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

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.conditioning_emb(lowerCAmelCase__		).unsqueeze(1		)

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

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.broadcast_to(
					    torch.arange(lowerCAmelCase__							,  device=decoder_input_tokens.device		)							,  (batch, seq_length)							,  )

					__SCREAMING_SNAKE_CASE :	str					 =	self.position_encoding(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.continuous_inputs_projection(lowerCAmelCase__		)
					inputs += position_encodings
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dropout(lowerCAmelCase__		)

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

					# Translate encoding masks to encoder-decoder masks.
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[(x, self.encoder_decoder_mask(lowerCAmelCase__							,  lowerCAmelCase__		)) for x, y in encodings_and_masks]

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

					for lyr in self.decoders:
							__SCREAMING_SNAKE_CASE :	Dict					 =	lyr(
							    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  encoder_attention_mask=lowerCAmelCase__							,  )[0]

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.decoder_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.post_dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.spec_out(lowerCAmelCase__		)
					return spec_out






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

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





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.ModuleList()

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

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

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



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Optional[int]=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer[0](
					    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					if encoder_hidden_states is not None:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.where(encoder_attention_mask > 0							,  0							,  -1E10		).to(
							    encoder_hidden_states.dtype		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[1](
							    lowerCAmelCase__							,  key_value_states=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					# Apply Film Conditional Feed Forward layer
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[-1](lowerCAmelCase__							,  lowerCAmelCase__		)

					return (hidden_states,)






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

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





					"""simple docstring"""






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



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				int=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.layer_norm(lowerCAmelCase__		)

					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	str					 =	self.FiLMLayer(lowerCAmelCase__							,  lowerCAmelCase__		)

					# Self-attention block
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.attention(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	hidden_states + self.dropout(lowerCAmelCase__		)

					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

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





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Any=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.layer_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.attention(
					    lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  attention_mask=attention_mask.squeeze(1		)							,  )
					__SCREAMING_SNAKE_CASE :	Dict					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return layer_output






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Any					 =	TaDenseGatedActDense(d_model=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Tuple=None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer_norm(lowerCAmelCase__		)
					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.film(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	self.DenseReluDense(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Dropout(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	NewGELUActivation()



			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.act(self.wi_a(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	self.wi_a(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_gelu * hidden_linear
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.wo(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

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





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Parameter(torch.ones(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	str					 =	eps



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	hidden_states.to(torch.floataa		).pow(2		).mean(-1							,  keepdim=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

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

					return self.weight * hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				torch.Tensor		):





					"""simple docstring"""






					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__							,  3.0		))		))






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

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





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  out_features * 2							,  bias=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scale_bias(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.chunk(lowerCAmelCase__							,  2							,  -1		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	x * (1 + scale) + shift
					return x