"""simple docstring"""




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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "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 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,*__UpperCAmelCase						,**__UpperCAmelCase   )    -> str:
													super().__init__(*__UpperCAmelCase						,**__UpperCAmelCase   )

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

													A__		   =							data_args
													A__		   =							self.config.tgt_vocab_size if isinstance(self.config						,__UpperCAmelCase   ) 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:
																				A__		   =							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id   )
													else:
																				# dynamically import label_smoothed_nll_loss
																				from utils import label_smoothed_nll_loss

																				A__		   =							label_smoothed_nll_loss





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													if self.optimizer is None:
																				A__		   =							['bias', 'LayerNorm.weight']
																				A__		   =							[
																				    {
																				        '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,
																				    },
																				]
																				A__		   =							Adafactor if self.args.adafactor else AdamW
																				if self.args.adafactor:
																											A__		   =							Adafactor
																											A__		   =							{'scale_parameter': False, 'relative_step': False}
																				else:
																											A__		   =							AdamW
																											A__		   =							{
																											    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																											    'eps': self.args.adam_epsilon,
																											}
																				A__		   =							self.args.learning_rate
																				if self.sharded_ddp:
																											A__		   =							OSS(
																											    params=__UpperCAmelCase						,optim=__UpperCAmelCase						,**__UpperCAmelCase						,)
																				else:
																											A__		   =							optimizer_cls(__UpperCAmelCase						,**__UpperCAmelCase   )

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





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							arg_to_scheduler[self.args.lr_scheduler]
													if self.args.lr_scheduler == "constant":
																				A__		   =							schedule_func(self.optimizer   )
													elif self.args.lr_scheduler == "constant_w_warmup":
																				A__		   =							schedule_func(self.optimizer						,num_warmup_steps=self.args.warmup_steps   )
													else:
																				A__		   =							schedule_func(
																				    self.optimizer						,num_warmup_steps=self.args.warmup_steps						,num_training_steps=__UpperCAmelCase   )
													return scheduler





						def  snake_case__		(				self   )    -> Optional[torch.utils.data.Sampler]:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[str]:
													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
																											A__		   =							model(**__UpperCAmelCase						,use_cache=__UpperCAmelCase   )[0]
																											A__		   =							self.loss_fn(logits.view(-1						,logits.shape[-1]   )						,labels.view(-1   )   )
																				else:
																											# compute usual loss via models
																											A__   ,     A__		   =							model(**__UpperCAmelCase						,labels=__UpperCAmelCase						,use_cache=__UpperCAmelCase   )[:2]
													else:
																				# compute label smoothed loss
																				A__		   =							model(**__UpperCAmelCase						,use_cache=__UpperCAmelCase   )[0]
																				A__		   =							torch.nn.functional.log_softmax(__UpperCAmelCase						,dim=-1   )
																				A__   ,     A__		   =							self.loss_fn(__UpperCAmelCase						,__UpperCAmelCase						,self.args.label_smoothing						,ignore_index=self.config.pad_token_id   )
													return loss, logits





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Any:
													A__		   =							inputs.pop('labels'   )
													A__   ,     A__		   =							self._compute_loss(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													return loss





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
													A__		   =							self._prepare_inputs(__UpperCAmelCase   )

													A__		   =							{
													    '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:
																				A__		   =							self.model.generate(
																				    inputs['input_ids']						,attention_mask=inputs['attention_mask']						,**__UpperCAmelCase						,)
																				# in case the batch is shorter than max length, the output should be padded
																				if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																											A__		   =							self._pad_tensors_to_max_len(__UpperCAmelCase						,gen_kwargs['max_length']   )

													A__		   =							inputs.pop('labels'   )
													with torch.no_grad():
																				# compute loss on predict data
																				A__   ,     A__		   =							self._compute_loss(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )

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

													A__		   =							generated_tokens if self.args.predict_with_generate else logits

													if labels.shape[-1] < gen_kwargs["max_length"]:
																				A__		   =							self._pad_tensors_to_max_len(__UpperCAmelCase						,gen_kwargs['max_length']   )

													return (loss, logits, labels)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[str]:
													# If PAD token is not defined at least EOS token has to be defined
													A__		   =							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}'''   )

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






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
													return -1





						@max_position_embeddings.setter
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





