



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


__snake_case		:Union[str, Any]   	=  logging.get_logger(__name__)

__snake_case		:str   	=  {
    '''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 (			_UpperCAmelCase					):



       def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=None  ,	*__SCREAMING_SNAKE_CASE							:  Tuple  ,	**__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           super().__init__(*lowercase__  ,	**lowercase__)

           if config is None:
               assert isinstance(self.model  ,	lowercase__), (
                   "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  ,	lowercase__) 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 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           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=lowercase__  ,	optim=lowercase__  ,	**lowercase__  ,	)
               else:
                   __a		 =       optimizer_cls(lowercase__  ,	**lowercase__)

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



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           __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=lowercase__)
           return scheduler



       def 	_lowerCamelCase    (	self							:  Dict):


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



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           __a		 =       inputs.pop('''labels''')
           __a		 =       self._compute_loss(lowercase__  ,	lowercase__  ,	lowercase__)
           return loss



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict = None  ,	):


           '''simple docstring'''

           __a		 =       self._prepare_inputs(lowercase__)

           __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''']  ,	**lowercase__  ,	)
               # 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(lowercase__  ,	gen_kwargs['''max_length'''])

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

           __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(lowercase__  ,	gen_kwargs['''max_length'''])

           return (loss, logits, labels)





       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __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










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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 __snake_case		:List[Any]   	=  None

__snake_case		:Dict   	=  logging.get_logger(__name__)

__snake_case		:Optional[int]   	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case		:Union[str, Any]   	=  {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case		:Optional[Any]   	=  {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__snake_case		:Optional[int]   	=  '''▁'''

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Tuple		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Tuple		      =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					str		      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					str		      =		['''input_ids''', '''attention_mask''']
       UpperCamelCase__						:					Dict		      =		BarthezTokenizer



       def __init__(	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Tuple="<s>"  ,	__SCREAMING_SNAKE_CASE							:  List[str]="</s>"  ,	__SCREAMING_SNAKE_CASE							:  Tuple="</s>"  ,	__SCREAMING_SNAKE_CASE							:  int="<s>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<unk>"  ,	__SCREAMING_SNAKE_CASE							:  int="<pad>"  ,	__SCREAMING_SNAKE_CASE							:  Any="<mask>"  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


           '''simple docstring'''

           __a		 =       AddedToken(__SCREAMING_SNAKE_CASE  ,	lstrip=__SCREAMING_SNAKE_CASE  ,	rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE) else mask_token

           super().__init__(
               __SCREAMING_SNAKE_CASE  ,	tokenizer_file=__SCREAMING_SNAKE_CASE  ,	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  ,	**__SCREAMING_SNAKE_CASE  ,	)

           __a		 =       vocab_file
           __a		 =       False if not self.vocab_file else True



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           __a		 =       [self.cls_token_id]
           __a		 =       [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[List[int]] = None):


           '''simple docstring'''

           __a		 =       [self.sep_token_id]
           __a		 =       [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]





       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = None):


           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
               raise ValueError(
                   '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                   '''tokenizer.''')

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a		 =       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):
               copyfile(self.vocab_file  ,	__SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)





