
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
		from fairscale.optim import OSS


lowercase        =  logging.get_logger(__name__)

lowercase        =  {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}




class       __A(				_snake_case       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     int=None   ,					*__UpperCamelCase					:     int   ,					**__UpperCamelCase					:     List[str]   ):
												super().__init__(*lowerCAmelCase__   ,					**lowerCAmelCase__   )

												if config is None:
																			assert isinstance(self.model   ,					lowerCAmelCase__   ), (
																			    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																			    F''' {self.model.__class__}'''
																			)
																			lowerCamelCase_							  =					self.model.config
												else:
																			lowerCamelCase_							  =					config

												lowerCamelCase_							  =					data_args
												lowerCamelCase_							  =					self.config.tgt_vocab_size if isinstance(self.config   ,					lowerCAmelCase__   ) else self.config.vocab_size

												if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
																			assert self.config.pad_token_id is not None, (
																			    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
																			    " calculation or doing label smoothing."
																			)

												if self.config.pad_token_id is None and self.config.eos_token_id is not None:
																			logger.warning(
																			    F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
																			    """ padding.."""   )

												if self.args.label_smoothing == 0:
																			lowerCamelCase_							  =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id   )
												else:
																			# dynamically import label_smoothed_nll_loss
																			from utils import label_smoothed_nll_loss

																			lowerCamelCase_							  =					label_smoothed_nll_loss

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     int   ):
												if self.optimizer is None:
																			lowerCamelCase_							  =					["""bias""", """LayerNorm.weight"""]
																			lowerCamelCase_							  =					[
																			    {
																			        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay   )],
																			        """weight_decay""": self.args.weight_decay,
																			    },
																			    {
																			        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay   )],
																			        """weight_decay""": 0.0,
																			    },
																			]
																			lowerCamelCase_							  =					Adafactor if self.args.adafactor else AdamW
																			if self.args.adafactor:
																										lowerCamelCase_							  =					Adafactor
																										lowerCamelCase_							  =					{"""scale_parameter""": False, """relative_step""": False}
																			else:
																										lowerCamelCase_							  =					AdamW
																										lowerCamelCase_							  =					{
																										    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
																										    """eps""": self.args.adam_epsilon,
																										}
																			lowerCamelCase_							  =					self.args.learning_rate
																			if self.sharded_ddp:
																										lowerCamelCase_							  =					OSS(
																										    params=lowerCAmelCase__   ,					optim=lowerCAmelCase__   ,					**lowerCAmelCase__   ,					)
																			else:
																										lowerCamelCase_							  =					optimizer_cls(lowerCAmelCase__   ,					**lowerCAmelCase__   )

												if self.lr_scheduler is None:
																			lowerCamelCase_							  =					self._get_lr_scheduler(lowerCAmelCase__   )
												else:  # ignoring --lr_scheduler
																			logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												lowerCamelCase_							  =					arg_to_scheduler[self.args.lr_scheduler]
												if self.args.lr_scheduler == "constant":
																			lowerCamelCase_							  =					schedule_func(self.optimizer   )
												elif self.args.lr_scheduler == "constant_w_warmup":
																			lowerCamelCase_							  =					schedule_func(self.optimizer   ,					num_warmup_steps=self.args.warmup_steps   )
												else:
																			lowerCamelCase_							  =					schedule_func(
																			    self.optimizer   ,					num_warmup_steps=self.args.warmup_steps   ,					num_training_steps=lowerCAmelCase__   )
												return scheduler

					def    lowercase__      (	self					:     Tuple   ):
												if isinstance(self.train_dataset   ,					torch.utils.data.IterableDataset   ):
																			return None
												elif is_torch_tpu_available():
																			return get_tpu_sampler(self.train_dataset   )
												else:
																			if self.args.sortish_sampler:
																										self.train_dataset.make_sortish_sampler(
																										    self.args.per_device_train_batch_size   ,					distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)   ,					)

																			return (
																			    RandomSampler(self.train_dataset   )
																			    if self.args.local_rank == -1
																			    else DistributedSampler(self.train_dataset   )
																			)

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												if self.args.label_smoothing == 0:
																			if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																										# force training to ignore pad token
																										lowerCamelCase_							  =					model(**lowerCAmelCase__   ,					use_cache=lowerCAmelCase__   )[0]
																										lowerCamelCase_							  =					self.loss_fn(logits.view(-1   ,					logits.shape[-1]   )   ,					labels.view(-1   )   )
																			else:
																										# compute usual loss via models
																										lowerCamelCase_  ,   lowerCamelCase_							  =					model(**lowerCAmelCase__   ,					labels=lowerCAmelCase__   ,					use_cache=lowerCAmelCase__   )[:2]
												else:
																			# compute label smoothed loss
																			lowerCamelCase_							  =					model(**lowerCAmelCase__   ,					use_cache=lowerCAmelCase__   )[0]
																			lowerCamelCase_							  =					torch.nn.functional.log_softmax(lowerCAmelCase__   ,					dim=-1   )
																			lowerCamelCase_  ,   lowerCamelCase_							  =					self.loss_fn(lowerCAmelCase__   ,					lowerCAmelCase__   ,					self.args.label_smoothing   ,					ignore_index=self.config.pad_token_id   )
												return loss, logits

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					inputs.pop("""labels"""   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					self._compute_loss(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   )
												return loss

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     nn.Module   ,					__UpperCamelCase					:     Dict[str, Union[torch.Tensor, Any]]   ,					__UpperCamelCase					:     bool   ,					__UpperCamelCase					:     Optional[List[str]] = None   ,					):
												lowerCamelCase_							  =					self._prepare_inputs(lowerCAmelCase__   )

												lowerCamelCase_							  =					{
												    """max_length""": self.data_args.val_max_target_length
												    if self.data_args is not None
												    else self.config.max_length,
												    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
												}

												if self.args.predict_with_generate and not self.args.prediction_loss_only:
																			lowerCamelCase_							  =					self.model.generate(
																			    inputs["""input_ids"""]   ,					attention_mask=inputs["""attention_mask"""]   ,					**lowerCAmelCase__   ,					)
																			# in case the batch is shorter than max length, the output should be padded
																			if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																										lowerCamelCase_							  =					self._pad_tensors_to_max_len(lowerCAmelCase__   ,					gen_kwargs["""max_length"""]   )

												lowerCamelCase_							  =					inputs.pop("""labels"""   )
												with torch.no_grad():
																			# compute loss on predict data
																			lowerCamelCase_  ,   lowerCamelCase_							  =					self._compute_loss(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   )

												lowerCamelCase_							  =					loss.mean().detach()
												if self.args.prediction_loss_only:
																			return (loss, None, None)

												lowerCamelCase_							  =					generated_tokens if self.args.predict_with_generate else logits

												if labels.shape[-1] < gen_kwargs["max_length"]:
																			lowerCamelCase_							  =					self._pad_tensors_to_max_len(lowerCAmelCase__   ,					gen_kwargs["""max_length"""]   )

												return (loss, logits, labels)



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												# If PAD token is not defined at least EOS token has to be defined
												lowerCamelCase_							  =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

												if pad_token_id is None:
																			raise ValueError(
																			    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
																			    F''' padded to `max_length`={max_length}'''   )

												lowerCamelCase_							  =					pad_token_id * torch.ones(
												    (tensor.shape[0], max_length)   ,					dtype=tensor.dtype   ,					device=tensor.device   )
												lowerCamelCase_							  =					tensor
												return padded_tensor







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       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					nn.Sequential(
												    nn.Linear(__UpperCamelCase   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					nn.Linear(d_model * 4   ,					d_model * 4   ,					bias=__UpperCamelCase   )   ,					nn.SiLU()   ,					)

												lowerCamelCase_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

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

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

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

												lowerCamelCase_							  =					self.conditioning_emb(__UpperCamelCase   ).unsqueeze(1   )

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

												lowerCamelCase_							  =					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.
												lowerCamelCase_							  =					torch.broadcast_to(
												    torch.arange(__UpperCamelCase   ,					device=decoder_input_tokens.device   )   ,					(batch, seq_length)   ,					)

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

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

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

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

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

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.ModuleList()

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

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

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



					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

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

																			lowerCamelCase_							  =					self.layer[1](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

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

												return (hidden_states,)





class       __A(				nn.Module       ):

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
												super().__init__()
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )
												lowerCamelCase_							  =					TaFiLMLayer(in_features=d_model * 4   ,					out_features=__UpperCamelCase   )
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

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

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ):
												super().__init__()
												lowerCamelCase_							  =					Attention(query_dim=__UpperCamelCase   ,					heads=__UpperCamelCase   ,					dim_head=__UpperCamelCase   ,					out_bias=__UpperCamelCase   ,					scale_qk=__UpperCamelCase   )
												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   ,					eps=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any   ):
												# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
												# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
												# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
												# half-precision inputs is done in fp32

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

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

												return self.weight * hidden_states





class       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





