
'''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__   :		Optional[Any] 		=logging.get_logger(__name__)

A__   :		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				( __a			):
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    List[Any]=None				,   lowerCamelCase			:    int=None				,   *lowerCamelCase			:    Optional[Any]				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   super().__init__(*A__				,   **A__       )

   if config is None:
     assert isinstance(self.model				,   A__       ), (
         "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
         f" {self.model.__class__}"
     )
     __A		:     Optional[Any]												=		self.model.config
   else:
     __A		:     Union[str, Any]												=		config

   __A		:     int												=		data_args
   __A		:     List[str]												=		self.config.tgt_vocab_size if isinstance(self.config				,   A__       ) 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		:     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

     __A		:     Tuple												=		label_smoothed_nll_loss
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





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

   if self.lr_scheduler is None:
     __A		:     Optional[int]												=		self._get_lr_scheduler(A__       )
   else:  # ignoring --lr_scheduler
     logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





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



   """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			:    Tuple				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    str       ):



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



   """simple docstring"""





   __A		:     str												=		inputs.pop("""labels"""       )
   __A							,					__A		:     List[str]												=		self._compute_loss(A__				,   A__				,   A__       )
   return loss
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    List[Any] = None				,   ):



   """simple docstring"""





   __A		:     Tuple												=		self._prepare_inputs(A__       )

   __A		:     List[str]												=		{
       """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		:     List[str]												=		self.model.generate(
         inputs["""input_ids"""]				,   attention_mask=inputs["""attention_mask"""]				,   **A__				,   )
     # in case the batch is shorter than max length, the output should be padded
     if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
       __A		:     Optional[Any]												=		self._pad_tensors_to_max_len(A__				,   gen_kwargs["""max_length"""]       )

   __A		:     Dict												=		inputs.pop("""labels"""       )
   with torch.no_grad():
     # compute loss on predict data
     __A							,					__A		:     int												=		self._compute_loss(A__				,   A__				,   A__       )

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

   __A		:     Tuple												=		generated_tokens if self.args.predict_with_generate else logits

   if labels.shape[-1] < gen_kwargs["max_length"]:
     __A		:     Any												=		self._pad_tensors_to_max_len(A__				,   gen_kwargs["""max_length"""]       )

   return (loss, logits, labels)






 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     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}"       )

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

'''simple docstring'''
A__   :		List[Any] 		=[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



