






'''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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[str]=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						*__SCREAMING_SNAKE_CASE      :			int  ,						**__SCREAMING_SNAKE_CASE      :			Any					)			->			Optional[Any]:
							super().__init__(*__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

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

							a_		:  Dict											=      data_args
							a_		:  Optional[Any]											=      self.config.tgt_vocab_size if isinstance(self.config  ,						__SCREAMING_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:
											a_		:  Optional[int]											=      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_		:  Dict											=      label_smoothed_nll_loss






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

							if self.lr_scheduler is None:
											a_		:  Optional[int]											=      self._get_lr_scheduler(__SCREAMING_SNAKE_CASE					)
							else:  # ignoring --lr_scheduler
											logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			List[str]:
							a_		:  str											=      arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
											a_		:  Tuple											=      schedule_func(self.optimizer					)
							elif self.args.lr_scheduler == "constant_w_warmup":
											a_		:  Dict											=      schedule_func(self.optimizer  ,						num_warmup_steps=self.args.warmup_steps					)
							else:
											a_		:  Any											=      schedule_func(
											    self.optimizer  ,						num_warmup_steps=self.args.warmup_steps  ,						num_training_steps=__SCREAMING_SNAKE_CASE					)
							return scheduler






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			int:
							a_		:  Tuple											=      inputs.pop('''labels'''					)
							a_				,			a_		:  Union[str, Any]											=      self._compute_loss(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							return loss






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			nn.Module  ,						__SCREAMING_SNAKE_CASE      :			Dict[str, Union[torch.Tensor, Any]]  ,						__SCREAMING_SNAKE_CASE      :			bool  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[str]] = None  ,						)			->			Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
							a_		:  Optional[int]											=      self._prepare_inputs(__SCREAMING_SNAKE_CASE					)

							a_		:  List[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:
											a_		:  str											=      self.model.generate(
											    inputs['''input_ids''']  ,						attention_mask=inputs['''attention_mask''']  ,						**__SCREAMING_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"]:
															a_		:  Dict											=      self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE  ,						gen_kwargs['''max_length''']					)

							a_		:  Tuple											=      inputs.pop('''labels'''					)
							with torch.no_grad():
											# compute loss on predict data
											a_				,			a_		:  Union[str, Any]											=      self._compute_loss(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

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

							a_		:  Optional[int]											=      generated_tokens if self.args.predict_with_generate else logits

							if labels.shape[-1] < gen_kwargs["max_length"]:
											a_		:  Union[str, Any]											=      self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE  ,						gen_kwargs['''max_length''']					)

							return (loss, logits, labels)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			List[str]:
							# If PAD token is not defined at least EOS token has to be defined
							a_		:  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}'					)

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












'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase       		=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__lowerCAmelCase       		=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__lowerCAmelCase       		=      '▁'





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Dict="<s>"  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Dict="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			int="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Dict[str, Any]] = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			None:
							# Mask token behave like a normal word, i.e. include the space before it
							a_		:  Tuple											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else mask_token

							a_		:  List[str]											=      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__SCREAMING_SNAKE_CASE  ,						eos_token=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**__SCREAMING_SNAKE_CASE  ,						)

							a_		:  Tuple											=      vocab_file
							a_		:  Optional[Any]											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__SCREAMING_SNAKE_CASE					)					)

							a_		:  Optional[Any]											=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							a_		:  Any											=      len(self.sp_model					) - 1
							a_		:  Optional[Any]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  List[str]											=      [self.cls_token_id]
							a_		:  Optional[Any]											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__SCREAMING_SNAKE_CASE  ,						token_ids_a=__SCREAMING_SNAKE_CASE  ,						already_has_special_tokens=__SCREAMING_SNAKE_CASE					)

							if token_ids_a is None:
											return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  Any											=      [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							return len(self.sp_model					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							a_		:  int											=      {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,						out_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							a_		:  Optional[int]											=      self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE					)

							return spm_id if spm_id else self.unk_token_id






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Dict											=      []
							a_		:  List[Any]											=      ''''''
							a_		:  Dict											=      False
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															if not prev_is_special:
																			out_string += " "
															out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					) + token
															a_		:  Dict											=      True
															a_		:  Optional[Any]											=      []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE					)
															a_		:  Tuple											=      False
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE					)
							return out_string.strip()






			def __getstate__( self      :			Dict					)			->			int:
							a_		:  Dict											=      self.__dict__.copy()
							a_		:  List[str]											=      None
							return state






			def __setstate__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Any:
							a_		:  Optional[Any]											=      d

							# for backward compatibility
							if not hasattr(self  ,						'''sp_model_kwargs'''					):
											a_		:  Union[str, Any]											=      {}

							a_		:  int											=      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Union[str, Any]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file  ,						__SCREAMING_SNAKE_CASE					)
							elif not os.path.isfile(self.vocab_file					):
											with open(__SCREAMING_SNAKE_CASE  ,						'''wb'''					) as fi:
															a_		:  Any											=      self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE					)

							return (out_vocab_file,)




