

'''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       (       __a      ):



 def __init__(       self						:       Optional[int]					, a__						:       Union[str, Any]=None					, a__						:       Any=None					, *a__						:       Optional[int]					, **a__						:       Union[str, Any]     ):
      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__}'''
           )
           __magic_name__		  =							self.model.config
      else:
           __magic_name__		  =							config

      __magic_name__		  =							data_args
      __magic_name__		  =							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:
           __magic_name__		  =							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id     )
      else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           __magic_name__		  =							label_smoothed_nll_loss



 def 					snake_case__    (       self						:       Any					, a__						:       Tuple     ):
      if self.optimizer is None:
           __magic_name__		  =							['''bias''', '''LayerNorm.weight''']
           __magic_name__		  =							[
               {
                   '''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,
               },
           ]
           __magic_name__		  =							Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
                __magic_name__		  =							Adafactor
                __magic_name__		  =							{'''scale_parameter''': False, '''relative_step''': False}
           else:
                __magic_name__		  =							AdamW
                __magic_name__		  =							{
                    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                    '''eps''': self.args.adam_epsilon,
                }
           __magic_name__		  =							self.args.learning_rate
           if self.sharded_ddp:
                __magic_name__		  =							OSS(
                    params=lowerCamelCase_					, optim=lowerCamelCase_					, **lowerCamelCase_					, )
           else:
                __magic_name__		  =							optimizer_cls(lowerCamelCase_					, **lowerCamelCase_     )

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



 def 					snake_case__    (       self						:       List[str]					, a__						:       str     ):
      __magic_name__		  =							arg_to_scheduler[self.args.lr_scheduler]
      if self.args.lr_scheduler == "constant":
           __magic_name__		  =							schedule_func(self.optimizer     )
      elif self.args.lr_scheduler == "constant_w_warmup":
           __magic_name__		  =							schedule_func(self.optimizer					, num_warmup_steps=self.args.warmup_steps     )
      else:
           __magic_name__		  =							schedule_func(
               self.optimizer					, num_warmup_steps=self.args.warmup_steps					, num_training_steps=lowerCamelCase_     )
      return scheduler



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



 def 					snake_case__    (       self						:       int					, a__						:       Dict					, a__						:       Any     ):
      __magic_name__		  =							inputs.pop('''labels'''     )
      __magic_name__  ,    __magic_name__		  =							self._compute_loss(lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_     )
      return loss



 def 					snake_case__    (       self						:       Union[str, Any]					, a__						:       int					, a__						:       str					, a__						:       str					, a__						:       Union[str, Any] = None					, ):
      __magic_name__		  =							self._prepare_inputs(lowerCamelCase_     )

      __magic_name__		  =							{
          '''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:
           __magic_name__		  =							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"]:
                __magic_name__		  =							self._pad_tensors_to_max_len(lowerCamelCase_					, gen_kwargs['''max_length''']     )

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

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

      __magic_name__		  =							generated_tokens if self.args.predict_with_generate else logits

      if labels.shape[-1] < gen_kwargs["max_length"]:
           __magic_name__		  =							self._pad_tensors_to_max_len(lowerCamelCase_					, gen_kwargs['''max_length''']     )

      return (loss, logits, labels)



 def 					snake_case__    (       self						:       Optional[Any]					, a__						:       List[str]					, a__						:       Dict     ):
      # If PAD token is not defined at least EOS token has to be defined
      __magic_name__		  =							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}'''     )

      __magic_name__		  =							pad_token_id * torch.ones(
          (tensor.shape[0], max_length)					, dtype=tensor.dtype					, device=tensor.device     )
      __magic_name__		  =							tensor
      return padded_tensor









"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




