'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__  :						Optional[int]					=      16
UpperCamelCase__  :						Optional[int]					=      32







def __UpperCamelCase( _A       :					Accelerator	,      _A       :					int = 16     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Tuple 					=	AutoTokenizer.from_pretrained('''bert-base-cased'''     )
   UpperCAmelCase__				:					List[Any] 					=	load_dataset('''glue'''	,      '''mrpc'''     )

   def tokenize_function(_A       :					Tuple     ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCAmelCase__				:					Tuple 					=	tokenizer(examples['''sentence1''']	,      examples['''sentence2''']	,      truncation=_A	,      max_length=_A     )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      UpperCAmelCase__				:					str 					=	datasets.map(
          _A	,      batched=_A	,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,      )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   UpperCAmelCase__				:					List[Any] 					=	tokenized_datasets.rename_column('''label'''	,      '''labels'''     )

   def collate_fn(_A       :					Union[str, Any]     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCAmelCase__				:					Optional[int] 					=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         UpperCAmelCase__				:					Dict 					=	16
      elif accelerator.mixed_precision != "no":
         UpperCAmelCase__				:					Optional[int] 					=	8
      else:
         UpperCAmelCase__				:					Optional[Any] 					=	None

      return tokenizer.pad(
          _A	,      padding='''longest'''	,      max_length=_A	,      pad_to_multiple_of=_A	,      return_tensors='''pt'''	,      )

   # Instantiate dataloaders.
   UpperCAmelCase__				:					int 					=	DataLoader(
       tokenized_datasets['''train''']	,      shuffle=_A	,      collate_fn=_A	,      batch_size=_A     )
   UpperCAmelCase__				:					Any 					=	DataLoader(
       tokenized_datasets['''validation''']	,      shuffle=_A	,      collate_fn=_A	,      batch_size=_A     )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  UpperCamelCase__  :						int					=      mocked_dataloaders  # noqa: F811







def __UpperCamelCase( _A       :					List[Any]	,      _A       :					Optional[Any]     ):
   '''simple docstring'''
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,      _A     ) == "1":
      UpperCAmelCase__				:					List[Any] 					=	2
   # Initialize accelerator
   UpperCAmelCase__				:					Optional[int] 					=	Accelerator(cpu=args.cpu	,      mixed_precision=args.mixed_precision     )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase__				:					Optional[Any] 					=	config['''lr''']
   UpperCAmelCase__				:					Union[str, Any] 					=	int(config['''num_epochs''']     )
   UpperCAmelCase__				:					str 					=	int(config['''seed''']     )
   UpperCAmelCase__				:					Optional[Any] 					=	int(config['''batch_size''']     )

   UpperCAmelCase__				:					Optional[Any] 					=	evaluate.load('''glue'''	,      '''mrpc'''     )

   # If the batch size is too big we use gradient accumulation
   UpperCAmelCase__				:					List[str] 					=	1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      UpperCAmelCase__				:					Optional[Any] 					=	batch_size // MAX_GPU_BATCH_SIZE
      UpperCAmelCase__				:					int 					=	MAX_GPU_BATCH_SIZE

   set_seed(_A     )
   UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	get_dataloaders(_A	,      _A     )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCAmelCase__				:					List[str] 					=	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,      return_dict=_A     )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCAmelCase__				:					str 					=	model.to(accelerator.device     )

   # Instantiate optimizer
   UpperCAmelCase__				:					List[Any] 					=	AdamW(params=model.parameters()	,      lr=_A     )

   # Instantiate scheduler
   UpperCAmelCase__				:					int 					=	get_linear_schedule_with_warmup(
       optimizer=_A	,      num_warmup_steps=1_00	,      num_training_steps=(len(_A     ) * num_epochs) // gradient_accumulation_steps	,      )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	accelerator.prepare(
       _A	,      _A	,      _A	,      _A	,      _A     )

   # Now we train the model
   for epoch in range(_A     ):
      model.train()
      for step, batch in enumerate(_A     ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device     )
         UpperCAmelCase__				:					Dict 					=	model(**_A     )
         UpperCAmelCase__				:					Any 					=	outputs.loss
         UpperCAmelCase__				:					List[Any] 					=	loss / gradient_accumulation_steps
         accelerator.backward(_A     )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      UpperCAmelCase__				:					Union[str, Any] 					=	0
      for step, batch in enumerate(_A     ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device     )
         with torch.no_grad():
            UpperCAmelCase__				:					Union[str, Any] 					=	model(**_A     )
         UpperCAmelCase__				:					List[Any] 					=	outputs.logits.argmax(dim=-1     )
         UpperCAmelCase__					,   UpperCAmelCase__				:					int 					=	accelerator.gather((predictions, batch['''labels'''])     )
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(_A     ) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               UpperCAmelCase__				:					Optional[int] 					=	predictions[: len(eval_dataloader.dataset     ) - samples_seen]
               UpperCAmelCase__				:					List[Any] 					=	references[: len(eval_dataloader.dataset     ) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=_A	,      references=_A	,      )

      UpperCAmelCase__				:					List[str] 					=	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''	,      _A     )







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Dict 					=	argparse.ArgumentParser(description='''Simple example of training script.'''     )
   parser.add_argument(
       '''--mixed_precision'''	,      type=_A	,      default=_A	,      choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,      help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''	,      )
   parser.add_argument('''--cpu'''	,      action='''store_true'''	,      help='''If passed, will train on the CPU.'''     )
   UpperCAmelCase__				:					Any 					=	parser.parse_args()
   UpperCAmelCase__				:					List[str] 					=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_A	,      _A     )


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 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase ):
      '''simple docstring'''


      @register_to_config
      def __init__(					self       ,lowerCamelCase_ = 128       ,lowerCamelCase_ = 256       ,lowerCamelCase_ = 2000.0       ,lowerCamelCase_ = 768       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 12       ,lowerCamelCase_ = 64       ,lowerCamelCase_ = 2048       ,lowerCamelCase_ = 0.1       ,)      ->      List[Any]:



         '''simple docstring'''



         super().__init__()

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Sequential(
             nn.Linear(lowerCamelCase_       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,nn.Linear(d_model * 4       ,d_model * 4       ,bias=lowerCamelCase_				)       ,nn.SiLU()       ,)

         UpperCAmelCase__				:					List[str] 					=	nn.Embedding(lowerCamelCase_       ,lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	False

         UpperCAmelCase__				:					List[str] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(p=lowerCamelCase_				)

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

         UpperCAmelCase__				:					Tuple 					=	TaLayerNorm(lowerCamelCase_				)

         UpperCAmelCase__				:					Any 					=	nn.Dropout(p=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         UpperCAmelCase__				:					str 					=	get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time       ,embedding_dim=self.config.d_model       ,max_period=self.config.max_decoder_noise_time       ,).to(dtype=self.dtype				)

         UpperCAmelCase__				:					List[Any] 					=	self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

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

         UpperCAmelCase__				:					List[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.
         UpperCAmelCase__				:					int 					=	torch.broadcast_to(
             torch.arange(lowerCamelCase_       ,device=decoder_input_tokens.device				)       ,(batch, seq_length)       ,)

         UpperCAmelCase__				:					Optional[Any] 					=	self.position_encoding(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.continuous_inputs_projection(lowerCamelCase_				)
         inputs += position_encodings
         UpperCAmelCase__				:					str 					=	self.dropout(lowerCamelCase_				)

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

         # Translate encoding masks to encoder-decoder masks.
         UpperCAmelCase__				:					Dict 					=	[(x, self.encoder_decoder_mask(lowerCamelCase_       ,lowerCamelCase_				)) for x, y in encodings_and_masks]

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

         for lyr in self.decoders:
            UpperCAmelCase__				:					Optional[Any] 					=	lyr(
                lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,encoder_attention_mask=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					List[Any] 					=	self.decoder_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	self.post_dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.spec_out(lowerCamelCase_				)
         return spec_out




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      str:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					int 					=	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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[0](
             lowerCamelCase_       ,conditioning_emb=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,)

         if encoder_hidden_states is not None:
            UpperCAmelCase__				:					Any 					=	torch.where(encoder_attention_mask > 0       ,0       ,-1e10				).to(
                encoder_hidden_states.dtype				)

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

         # Apply Film Conditional Feed Forward layer
         UpperCAmelCase__				:					Optional[Any] 					=	self.layer[-1](lowerCamelCase_       ,lowerCamelCase_				)

         return (hidden_states,)




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	TaLayerNorm(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)

         if conditioning_emb is not None:
            UpperCAmelCase__				:					Any 					=	self.FiLMLayer(lowerCamelCase_       ,lowerCamelCase_				)

         # Self-attention block
         UpperCAmelCase__				:					List[Any] 					=	self.attention(lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	hidden_states + self.dropout(lowerCamelCase_				)

         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Optional[int] 					=	Attention(query_dim=lowerCamelCase_       ,heads=lowerCamelCase_       ,dim_head=lowerCamelCase_       ,out_bias=lowerCamelCase_       ,scale_qk=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.layer_norm(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	self.attention(
             lowerCamelCase_       ,encoder_hidden_states=lowerCamelCase_       ,attention_mask=attention_mask.squeeze(1				)       ,)
         UpperCAmelCase__				:					int 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return layer_output




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	TaDenseGatedActDense(d_model=lowerCamelCase_       ,d_ff=lowerCamelCase_       ,dropout_rate=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	TaFiLMLayer(in_features=d_model * 4       ,out_features=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	TaLayerNorm(lowerCamelCase_       ,eps=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Dropout(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.layer_norm(lowerCamelCase_				)
         if conditioning_emb is not None:
            UpperCAmelCase__				:					Optional[Any] 					=	self.film(lowerCamelCase_       ,lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	self.DenseReluDense(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	hidden_states + self.dropout(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Union[str, Any] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Linear(lowerCamelCase_       ,lowerCamelCase_       ,bias=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	nn.Dropout(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	NewGELUActivation()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.act(self.wi_a(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[str] 					=	self.wi_a(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	hidden_gelu * hidden_linear
         UpperCAmelCase__				:					int 					=	self.dropout(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	self.wo(lowerCamelCase_				)
         return hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=1e-6				)      ->      int:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					Tuple 					=	nn.Parameter(torch.ones(lowerCamelCase_				)				)
         UpperCAmelCase__				:					Dict 					=	eps




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	hidden_states.to(torch.floataa				).pow(2				).mean(-1       ,keepdim=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

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

         return self.weight * hidden_states




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      torch.Tensor:



         '''simple docstring'''



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




class 					_lowercase						(   nn.Module ):
      '''simple docstring'''


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



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__				:					str 					=	nn.Linear(lowerCamelCase_       ,out_features * 2       ,bias=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.scale_bias(lowerCamelCase_				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	torch.chunk(lowerCamelCase_       ,2       ,-1				)
         UpperCAmelCase__				:					List[Any] 					=	x * (1 + scale) + shift
         return x




