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


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)

__UpperCamelCase		:       Union[str, Any]									=     {
    '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 					lowercase__						(   UpperCamelCase_):


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





												'''simple docstring'''





												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__}"""
																		)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.model.config
												else:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							config

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							data_args
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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:
																		SCREAMING_SNAKE_CASE							:    str							=							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
												else:
																		# dynamically import label_smoothed_nll_loss
																		from utils import label_smoothed_nll_loss

																		SCREAMING_SNAKE_CASE							:    Any							=							label_smoothed_nll_loss


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												if self.optimizer is None:
																		SCREAMING_SNAKE_CASE							:    int							=							['''bias''', '''LayerNorm.weight''']
																		SCREAMING_SNAKE_CASE							:    str							=							[
																		    {
																		        '''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,
																		    },
																		]
																		SCREAMING_SNAKE_CASE							:    Any							=							Adafactor if self.args.adafactor else AdamW
																		if self.args.adafactor:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Adafactor
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{'''scale_parameter''': False, '''relative_step''': False}
																		else:
																								SCREAMING_SNAKE_CASE							:    Dict							=							AdamW
																								SCREAMING_SNAKE_CASE							:    int							=							{
																								    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																								    '''eps''': self.args.adam_epsilon,
																								}
																		SCREAMING_SNAKE_CASE							:    str							=							self.args.learning_rate
																		if self.sharded_ddp:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							OSS(
																								    params=UpperCamelCase__    ,		optim=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
																		else:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							optimizer_cls(UpperCamelCase__    ,		**UpperCamelCase__						)

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


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							arg_to_scheduler[self.args.lr_scheduler]
												if self.args.lr_scheduler == "constant":
																		SCREAMING_SNAKE_CASE							:    Any							=							schedule_func(self.optimizer						)
												elif self.args.lr_scheduler == "constant_w_warmup":
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							schedule_func(self.optimizer    ,		num_warmup_steps=self.args.warmup_steps						)
												else:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							schedule_func(
																		    self.optimizer    ,		num_warmup_steps=self.args.warmup_steps    ,		num_training_steps=UpperCamelCase__						)
												return scheduler


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												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        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				List[str]    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												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
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							model(**UpperCamelCase__    ,		use_cache=UpperCamelCase__						)[0]
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.loss_fn(logits.view(-1    ,		logits.shape[-1]						)    ,		labels.view(-1						)						)
																		else:
																								# compute usual loss via models
																								SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							model(**UpperCamelCase__    ,		labels=UpperCamelCase__    ,		use_cache=UpperCamelCase__						)[:2]
												else:
																		# compute label smoothed loss
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							model(**UpperCamelCase__    ,		use_cache=UpperCamelCase__						)[0]
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.nn.functional.log_softmax(UpperCamelCase__    ,		dim=-1						)
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							self.loss_fn(UpperCamelCase__    ,		UpperCamelCase__    ,		self.args.label_smoothing    ,		ignore_index=self.config.pad_token_id						)
												return loss, logits


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							inputs.pop('''labels'''						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							self._compute_loss(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												return loss


						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				nn.Module    ,		UpperCamelCase__	:				Dict[str, Union[torch.Tensor, Any]]    ,		UpperCamelCase__	:				bool    ,		UpperCamelCase__	:				Optional[List[str]] = None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self._prepare_inputs(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{
												    '''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:
																		SCREAMING_SNAKE_CASE							:    Any							=							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"]:
																								SCREAMING_SNAKE_CASE							:    Tuple							=							self._pad_tensors_to_max_len(UpperCamelCase__    ,		gen_kwargs['''max_length''']						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							inputs.pop('''labels'''						)
												with torch.no_grad():
																		# compute loss on predict data
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							self._compute_loss(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							generated_tokens if self.args.predict_with_generate else logits

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

												return (loss, logits, labels)



						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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}"""						)

												SCREAMING_SNAKE_CASE							:    Dict							=							pad_token_id * torch.ones(
												    (tensor.shape[0], max_length)    ,		dtype=tensor.dtype    ,		device=tensor.device						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							tensor
												return padded_tensor






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





