



'''simple docstring'''




import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


UpperCamelCase__ :			List[Any]												=				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm
						import torch_xla.debug.metrics as met






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				int							,  *lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				str=None							,  lowerCAmelCase__  :				int=None							,  **lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	eval_examples
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	post_process_function
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	quant_trainer_args
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	1_2_8  # default number of calibration samples

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





					"""simple docstring"""






					if calib_dataset is None and self.calib_dataset is None:
							raise ValueError("""Trainer: calibration requires an calib_dataset."""		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	calib_dataset if calib_dataset is not None else self.calib_dataset

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self._remove_unused_columns(lowerCAmelCase__							,  description="""Calibration"""		)

					return DataLoader(
					    lowerCAmelCase__							,  batch_size=self.args.eval_batch_size							,  collate_fn=self.data_collator							,  drop_last=self.args.dataloader_drop_last							,  num_workers=self.args.dataloader_num_workers							,  pin_memory=self.args.dataloader_pin_memory							,  shuffle=lowerCAmelCase__							,  )

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.train_dataset if calib_dataset is None else calib_dataset
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_calib_dataloader(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.model
					quant_trainer.configure_model(lowerCAmelCase__							,  self.quant_trainer_args							,  calib=lowerCAmelCase__		)
					model.eval()
					quant_trainer.enable_calibration(lowerCAmelCase__		)

					logger.info("""***** Running calibration *****"""		)
					logger.info(F"  Num examples = {self.calib_num}"		)
					logger.info(F"  Batch size = {calib_dataloader.batch_size}"		)

					for step, inputs in enumerate(lowerCAmelCase__		):
							# Prediction step
							__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	self.prediction_step(lowerCAmelCase__							,  lowerCAmelCase__							,  prediction_loss_only=lowerCAmelCase__		)
							if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
									break

					quant_trainer.finish_calibration(lowerCAmelCase__							,  self.quant_trainer_args		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	model

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.eval_dataset if eval_dataset is None else eval_dataset
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_eval_dataloader(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.compute_metrics
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	None
					__SCREAMING_SNAKE_CASE :	Any					 =	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	eval_loop(
							    lowerCAmelCase__							,  description="""Evaluation"""							,  prediction_loss_only=True if compute_metrics is None else None							,  ignore_keys=lowerCAmelCase__							,  )
					finally:
							__SCREAMING_SNAKE_CASE :	str					 =	compute_metrics

					if self.post_process_function is not None and self.compute_metrics is not None:
							__SCREAMING_SNAKE_CASE :	str					 =	self.post_process_function(lowerCAmelCase__							,  lowerCAmelCase__							,  output.predictions		)
							__SCREAMING_SNAKE_CASE :	Any					 =	self.compute_metrics(lowerCAmelCase__		)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()		):
									if not key.startswith(F"{metric_key_prefix}_"		):
											__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	metrics.pop(lowerCAmelCase__		)

							self.log(lowerCAmelCase__		)
					else:
							__SCREAMING_SNAKE_CASE :	str					 =	{}

					if self.args.tpu_metrics_debug or self.args.debug:
							# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
							xm.master_print(met.metrics_report()		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.callback_handler.on_evaluate(self.args							,  self.state							,  self.control							,  lowerCAmelCase__		)
					return metrics

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.get_test_dataloader(lowerCAmelCase__		)

					# Temporarily disable metric computation, we will do it in the loop here.
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.compute_metrics
					__SCREAMING_SNAKE_CASE :	Any					 =	None
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	eval_loop(
							    lowerCAmelCase__							,  description="""Prediction"""							,  prediction_loss_only=True if compute_metrics is None else None							,  ignore_keys=lowerCAmelCase__							,  )
					finally:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	compute_metrics

					if self.post_process_function is None or self.compute_metrics is None:
							return output

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.post_process_function(lowerCAmelCase__							,  lowerCAmelCase__							,  output.predictions							,  """predict"""		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.compute_metrics(lowerCAmelCase__		)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()		):
							if not key.startswith(F"{metric_key_prefix}_"		):
									__SCREAMING_SNAKE_CASE :	str					 =	metrics.pop(lowerCAmelCase__		)

					return PredictionOutput(predictions=predictions.predictions							,  label_ids=predictions.label_ids							,  metrics=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				Union[str, Any]="./"		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.eval_dataset
					__SCREAMING_SNAKE_CASE :	str					 =	self.get_eval_dataloader(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	next(iter(lowerCAmelCase__		)		)

					# saving device - to make it consistent
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)

					# convert to tuple
					__SCREAMING_SNAKE_CASE :	str					 =	tuple(v.to(lowerCAmelCase__		) for k, v in batch.items()		)

					logger.info("""Converting model to be onnx compatible"""		)
					from pytorch_quantization.nn import TensorQuantizer

					__SCREAMING_SNAKE_CASE :	Dict					 =	True

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.model.to(lowerCAmelCase__		)

					model.eval()
					model.float()

					__SCREAMING_SNAKE_CASE :	int					 =	model.module if hasattr(lowerCAmelCase__							,  """module"""		) else model
					quant_trainer.configure_model(lowerCAmelCase__							,  self.quant_trainer_args		)

					__SCREAMING_SNAKE_CASE :	int					 =	os.path.join(lowerCAmelCase__							,  """model.onnx"""		)
					logger.info(F"exporting model to {output_model_file}"		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{0: """batch_size""", 1: """seq_len"""}

					torch.onnx.export(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  export_params=lowerCAmelCase__							,  opset_version=1_3							,  do_constant_folding=lowerCAmelCase__							,  input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]							,  output_names=["""output_start_logits""", """output_end_logits"""]							,  dynamic_axes={
					        """input_ids""": axes,
					        """attention_mask""": axes,
					        """token_type_ids""": axes,
					        """output_start_logits""": axes,
					        """output_end_logits""": axes,
					    }							,  verbose=lowerCAmelCase__							,  )
					logger.info("""onnx export finished"""		)




'''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