





"""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


__A											=						logging.get_logger(__name__)

__A											=						{
    """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    _lowerCAmelCase  (	a			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''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__}"
           )
           lowerCAmelCase__     :List[str]											=				self.model.config
         else:
           lowerCAmelCase__     :str											=				config

         lowerCAmelCase__     :Union[str, Any]											=				data_args
         lowerCAmelCase__     :str											=				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:
           lowerCAmelCase__     :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

           lowerCAmelCase__     :List[Any]											=				label_smoothed_nll_loss






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

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

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






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				inputs.pop('labels' )
         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				self._compute_loss(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         return loss






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self._prepare_inputs(__UpperCAmelCase )

         lowerCAmelCase__     :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:
           lowerCAmelCase__     :int											=				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"]:
             lowerCAmelCase__     :Optional[Any]											=				self._pad_tensors_to_max_len(__UpperCAmelCase       ,					gen_kwargs['max_length'] )

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

         lowerCAmelCase__     :Optional[int]											=				loss.mean().detach()
         if self.args.prediction_loss_only:
           return (loss, None, None)

         lowerCAmelCase__     :Optional[Any]											=				generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
           lowerCAmelCase__     :Optional[int]											=				self._pad_tensors_to_max_len(__UpperCAmelCase       ,					gen_kwargs['max_length'] )

         return (loss, logits, labels)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, 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}" )

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







"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A											=						logging.get_logger(__name__)

__A											=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__A											=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

__A											=						{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:str						    =   VOCAB_FILES_NAMES
       __magic_name__	:List[Any]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Optional[Any]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__	:str						    =   ["""input_ids""", """attention_mask"""]
       __magic_name__	:Any						    =   RobertaTokenizer






       def __init__(       self       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase="replace"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="<mask>"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					tokenizer_file=__UpperCAmelCase       ,					errors=__UpperCAmelCase       ,					bos_token=__UpperCAmelCase       ,					eos_token=__UpperCAmelCase       ,					sep_token=__UpperCAmelCase       ,					cls_token=__UpperCAmelCase       ,					unk_token=__UpperCAmelCase       ,					pad_token=__UpperCAmelCase       ,					mask_token=__UpperCAmelCase       ,					add_prefix_space=__UpperCAmelCase       ,					trim_offsets=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Dict											=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get('add_prefix_space'       ,					__UpperCAmelCase ) != add_prefix_space:
           lowerCAmelCase__     :Optional[int]											=				getattr(__UpperCAmelCase       ,					pre_tok_state.pop('type' ) )
           lowerCAmelCase__     :List[Any]											=				add_prefix_space
           lowerCAmelCase__     :str											=				pre_tok_class(**__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				add_prefix_space

         lowerCAmelCase__     :str											=				'post_processor'
         lowerCAmelCase__     :Optional[Any]											=				getattr(self.backend_tokenizer       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         if tokenizer_component_instance:
           lowerCAmelCase__     :Optional[Any]											=				json.loads(tokenizer_component_instance.__getstate__() )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             lowerCAmelCase__     :Any											=				tuple(state['sep'] )
           if "cls" in state:
             lowerCAmelCase__     :int											=				tuple(state['cls'] )

           lowerCAmelCase__     :List[Any]											=				False

           if state.get('add_prefix_space'       ,					__UpperCAmelCase ) != add_prefix_space:
             lowerCAmelCase__     :Union[str, Any]											=				add_prefix_space
             lowerCAmelCase__     :Any											=				True

           if state.get('trim_offsets'       ,					__UpperCAmelCase ) != trim_offsets:
             lowerCAmelCase__     :Union[str, Any]											=				trim_offsets
             lowerCAmelCase__     :Optional[int]											=				True

           if changes_to_apply:
             lowerCAmelCase__     :str											=				getattr(__UpperCAmelCase       ,					state.pop('type' ) )
             lowerCAmelCase__     :Any											=				component_class(**__UpperCAmelCase )
             setattr(self.backend_tokenizer       ,					__UpperCAmelCase       ,					__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self._mask_token is None:
           if self.verbose:
             logger.error('Using mask_token, but it is not set yet.' )
           return None
         return str(self._mask_token )






       @mask_token.setter
       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else value
         lowerCAmelCase__     :List[str]											=				value






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('is_split_into_words'       ,					__UpperCAmelCase )
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				kwargs.get('is_split_into_words'       ,					__UpperCAmelCase )

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self._tokenizer.model.save(__UpperCAmelCase       ,					name=__UpperCAmelCase )
         return tuple(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[self.sep_token_id]
         lowerCAmelCase__     :int											=				[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]
