
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_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Optional[int]    =	{
    """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					(       snake_case_	):






			'''simple docstring'''

			def __init__(							self			:							Optional[Any]    ,						snake_case_			:							int=None    ,						snake_case_			:							Any=None    ,						*snake_case_			:							Union[str, Any]    ,						**snake_case_			:							str   ):
							super().__init__(*snake_case_    ,						**snake_case_   )

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

							UpperCamelCase_:				Any						=    data_args
							UpperCamelCase_:				List[str]						=    self.config.tgt_vocab_size if isinstance(self.config    ,						snake_case_   ) 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:
											UpperCamelCase_:				Union[str, Any]						=    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id   )
							else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											UpperCamelCase_:				Tuple						=    label_smoothed_nll_loss

			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							Union[str, Any]   ):
							if self.optimizer is None:
											UpperCamelCase_:				Optional[Any]						=    ["""bias""", """LayerNorm.weight"""]
											UpperCamelCase_:				Optional[Any]						=    [
											    {
											        """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,
											    },
											]
											UpperCamelCase_:				List[str]						=    Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
															UpperCamelCase_:				Tuple						=    Adafactor
															UpperCamelCase_:				Optional[int]						=    {"""scale_parameter""": False, """relative_step""": False}
											else:
															UpperCamelCase_:				Any						=    AdamW
															UpperCamelCase_:				Tuple						=    {
															    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
															    """eps""": self.args.adam_epsilon,
															}
											UpperCamelCase_:				List[Any]						=    self.args.learning_rate
											if self.sharded_ddp:
															UpperCamelCase_:				Optional[int]						=    OSS(
															    params=snake_case_    ,						optim=snake_case_    ,						**snake_case_    ,						)
											else:
															UpperCamelCase_:				Optional[Any]						=    optimizer_cls(snake_case_    ,						**snake_case_   )

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

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							List[str]   ):
							UpperCamelCase_:				Union[str, Any]						=    arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
											UpperCamelCase_:				Optional[int]						=    schedule_func(self.optimizer   )
							elif self.args.lr_scheduler == "constant_w_warmup":
											UpperCamelCase_:				Dict						=    schedule_func(self.optimizer    ,						num_warmup_steps=self.args.warmup_steps   )
							else:
											UpperCamelCase_:				Tuple						=    schedule_func(
											    self.optimizer    ,						num_warmup_steps=self.args.warmup_steps    ,						num_training_steps=snake_case_   )
							return scheduler

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

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							List[str]    ,						snake_case_			:							Tuple   ):
							UpperCamelCase_:				str						=    inputs.pop("""labels"""   )
							UpperCamelCase_, UpperCamelCase_:				str						=    self._compute_loss(snake_case_    ,						snake_case_    ,						snake_case_   )
							return loss

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							List[str]    ,						snake_case_			:							int    ,						snake_case_			:							str = None    ,						):
							UpperCamelCase_:				Optional[int]						=    self._prepare_inputs(snake_case_   )

							UpperCamelCase_:				Union[str, 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:
											UpperCamelCase_:				Tuple						=    self.model.generate(
											    inputs["""input_ids"""]    ,						attention_mask=inputs["""attention_mask"""]    ,						**snake_case_    ,						)
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
															UpperCamelCase_:				Any						=    self._pad_tensors_to_max_len(snake_case_    ,						gen_kwargs["""max_length"""]   )

							UpperCamelCase_:				Any						=    inputs.pop("""labels"""   )
							with torch.no_grad():
											# compute loss on predict data
											UpperCamelCase_, UpperCamelCase_:				List[str]						=    self._compute_loss(snake_case_    ,						snake_case_    ,						snake_case_   )

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

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

							if labels.shape[-1] < gen_kwargs["max_length"]:
											UpperCamelCase_:				str						=    self._pad_tensors_to_max_len(snake_case_    ,						gen_kwargs["""max_length"""]   )

							return (loss, logits, labels)




			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							int    ,						snake_case_			:							Any   ):
							# If PAD token is not defined at least EOS token has to be defined
							UpperCamelCase_:				List[Any]						=    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}'''   )

							UpperCamelCase_:				Optional[int]						=    pad_token_id * torch.ones(
							    (tensor.shape[0], max_length)    ,						dtype=tensor.dtype    ,						device=tensor.device   )
							UpperCamelCase_:				List[Any]						=    tensor
							return padded_tensor








import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


