


"""simple docstring"""







import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


lowerCAmelCase__      				=   threading.Lock()
lowerCAmelCase__      				=   None

lowerCAmelCase__      				=   {
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCAmelCase__      				=   logging.WARNING

lowerCAmelCase__      				=   True
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							os.getenv("TRANSFORMERS_VERBOSITY"				,      SCREAMING_SNAKE_CASE				)
  if env_level_str:
    if env_level_str in log_levels:
      return log_levels[env_level_str]
    else:
      logging.getLogger().warning(
          f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
          f"""has to be one of: { ", ".join(log_levels.keys()				) }"""				)
  return _default_log_level
def 					a__			(				):


  '''simple docstring'''







  return __name__.split("."				)[0]
def 					a__			(				):


  '''simple docstring'''







  return logging.getLogger(_get_library_name()				)
def 					a__			(				):


  '''simple docstring'''







  global _default_handler

  with _lock:
    if _default_handler:
      # This library has already configured the library root logger.
      return
    lowerCAmelCase     :     int								=							logging.StreamHandler()  # Set sys.stderr as stream.
    lowerCAmelCase     :     Optional[int]								=							sys.stderr.flush

    # Apply our default configuration to the library root logger.
    lowerCAmelCase     :     Optional[Any]								=							_get_library_root_logger()
    library_root_logger.addHandler(_default_handler				)
    library_root_logger.setLevel(_get_default_logging_level()				)
    lowerCAmelCase     :     List[Any]								=							False
def 					a__			(				):


  '''simple docstring'''







  global _default_handler

  with _lock:
    if not _default_handler:
      return

    lowerCAmelCase     :     Tuple								=							_get_library_root_logger()
    library_root_logger.removeHandler(_default_handler				)
    library_root_logger.setLevel(logging.NOTSET				)
    lowerCAmelCase     :     Optional[Any]								=							None
def 					a__			(				):


  '''simple docstring'''







  return log_levels
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[str] = None				):


  '''simple docstring'''







  if name is None:
    lowerCAmelCase     :     List[str]								=							_get_library_name()

  _configure_library_root_logger()
  return logging.getLogger(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  _configure_library_root_logger()
  return _get_library_root_logger().getEffectiveLevel()
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  _configure_library_root_logger()
  _get_library_root_logger().setLevel(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  return set_verbosity(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  return set_verbosity(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  return set_verbosity(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  return set_verbosity(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  _configure_library_root_logger()

  assert _default_handler is not None
  _get_library_root_logger().removeHandler(_default_handler				)
def 					a__			(				):


  '''simple docstring'''







  _configure_library_root_logger()

  assert _default_handler is not None
  _get_library_root_logger().addHandler(_default_handler				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    logging.Handler				):


  '''simple docstring'''







  _configure_library_root_logger()

  assert handler is not None
  _get_library_root_logger().addHandler(SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    logging.Handler				):


  '''simple docstring'''







  _configure_library_root_logger()

  assert handler is not None and handler not in _get_library_root_logger().handlers
  _get_library_root_logger().removeHandler(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  _configure_library_root_logger()
  lowerCAmelCase     :     Optional[Any]								=							False
def 					a__			(				):


  '''simple docstring'''







  _configure_library_root_logger()
  lowerCAmelCase     :     List[Any]								=							True
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Tuple								=							_get_library_root_logger().handlers

  for handler in handlers:
    lowerCAmelCase     :     Optional[Any]								=							logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"				)
    handler.setFormatter(SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							_get_library_root_logger().handlers

  for handler in handlers:
    handler.setFormatter(SCREAMING_SNAKE_CASE				)
def 					a__			(				self    :    int				,      *SCREAMING_SNAKE_CASE    :    str				,      **SCREAMING_SNAKE_CASE    :    str				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"				,      SCREAMING_SNAKE_CASE				)
  if no_advisory_warnings:
    return
  self.warning(*SCREAMING_SNAKE_CASE				,      **SCREAMING_SNAKE_CASE				)


lowerCAmelCase__      				=   warning_advice
@functools.lru_cache(SCREAMING_SNAKE_CASE				)
def 					a__			(				self    :    str				,      *SCREAMING_SNAKE_CASE    :    Dict				,      **SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  self.warning(*SCREAMING_SNAKE_CASE				,      **SCREAMING_SNAKE_CASE				)


lowerCAmelCase__      				=   warning_once


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     *snake_case__		,     **snake_case__						):  # pylint: disable=unused-argument






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							args[0] if args else None

       def __iter__(						self						):






         """simple docstring"""





         return iter(self._iterator						)

       def __getattr__(						self		,     snake_case__						):






         """simple docstring"""





         def empty_fn(*snake_case__		,     **snake_case__						):  # pylint: disable=unused-argument
           return

         return empty_fn

       def __enter__(						self						):






         """simple docstring"""





         return self







       def __exit__(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         return


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __call__(						self		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         if _tqdm_active:
           return tqdm_lib.tqdm(*snake_case__		,     **snake_case__						)
         else:
           return EmptyTqdm(*snake_case__		,     **snake_case__						)

       def        lowercase__							(						self		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							None
         if _tqdm_active:
           return tqdm_lib.tqdm.set_lock(*snake_case__		,     **snake_case__						)







       def        lowercase__							(						self						):






         """simple docstring"""





         if _tqdm_active:
           return tqdm_lib.tqdm.get_lock()

lowerCAmelCase__      				=   _tqdm_cls()
def 					a__			(				):


  '''simple docstring'''







  global _tqdm_active
  return bool(_tqdm_active				)
def 					a__			(				):


  '''simple docstring'''







  global _tqdm_active
  lowerCAmelCase     :     Optional[Any]								=							True
  hf_hub_utils.enable_progress_bars()





def 					a__			(				):


  '''simple docstring'''







  global _tqdm_active
  lowerCAmelCase     :     Tuple								=							False
  hf_hub_utils.disable_progress_bars()









"""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__				(		lowercase				):
       """simple docstring"""


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






         """simple docstring"""





         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__}"""
           )
           lowerCAmelCase     :     Optional[int]								=							self.model.config
         else:
           lowerCAmelCase     :     List[str]								=							config

         lowerCAmelCase     :     Any								=							data_args
         lowerCAmelCase     :     Tuple								=							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:
           lowerCAmelCase     :     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

           lowerCAmelCase     :     Tuple								=							label_smoothed_nll_loss

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





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

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

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						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        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							inputs.pop("labels"						)
         lowerCAmelCase						,  lowerCAmelCase     :     str								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)
         return loss

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self._prepare_inputs(snake_case__						)

         lowerCAmelCase     :     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:
           lowerCAmelCase     :     Dict								=							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"]:
             lowerCAmelCase     :     Dict								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         lowerCAmelCase     :     Optional[Any]								=							inputs.pop("labels"						)
         with torch.no_grad():
           # compute loss on predict data
           lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[str]								=							loss.mean().detach()
         if self.args.prediction_loss_only:
           return (loss, None, None)

         lowerCAmelCase     :     int								=							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(snake_case__		,     gen_kwargs["max_length"]						)

         return (loss, logits, labels)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     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}"""						)

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





