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


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

SCREAMING_SNAKE_CASE__: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 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)

        if config is None:
               assert isinstance(self.model  ,						lowerCamelCase		), (
                   "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  ,						lowerCamelCase		) 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 		a__ (				self  ,						lowerCamelCase		):
        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=lowerCamelCase  ,						optim=lowerCamelCase  ,						**lowerCamelCase  ,						)
               else:
                      __a     				=  optimizer_cls(lowerCamelCase  ,						**lowerCamelCase		)

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


 def 		a__ (				self  ,						lowerCamelCase		):
        __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=lowerCamelCase		)
        return scheduler


 def 		a__ (				self		):
        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  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        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(**lowerCamelCase  ,						use_cache=lowerCamelCase		)[0]
                      __a     				=  self.loss_fn(logits.view(-1  ,						logits.shape[-1]		)  ,						labels.view(-1		)		)
               else:
                      # compute usual loss via models
                      __a				, __a     				=  model(**lowerCamelCase  ,						labels=lowerCamelCase  ,						use_cache=lowerCamelCase		)[:2]
        else:
               # compute label smoothed loss
               __a     				=  model(**lowerCamelCase  ,						use_cache=lowerCamelCase		)[0]
               __a     				=  torch.nn.functional.log_softmax(lowerCamelCase  ,						dim=-1		)
               __a				, __a     				=  self.loss_fn(lowerCamelCase  ,						lowerCamelCase  ,						self.args.label_smoothing  ,						ignore_index=self.config.pad_token_id		)
        return loss, logits


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  inputs.pop("labels"		)
        __a				, __a     				=  self._compute_loss(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
        return loss


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						):
        __a     				=  self._prepare_inputs(lowerCamelCase		)

        __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"]  ,						**lowerCamelCase  ,						)
               # 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(lowerCamelCase  ,						gen_kwargs["max_length"]		)

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

        __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(lowerCamelCase  ,						gen_kwargs["max_length"]		)

        return (loss, logits, labels)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        # 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"""



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:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """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"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        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(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
