



'''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[int]   	=			logging.get_logger(__name__)

a__      :				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       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   def __init__(						self  :			Tuple			,				lowerCAmelCase  :			Dict=None			,				lowerCAmelCase  :			List[str]=None			,				*lowerCAmelCase  :			Tuple			,				**lowerCAmelCase  :			Optional[Any])	->				str:




         """simple docstring"""

         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__}'''
               )
               lowercase__     =  self.model.config
         else:
               lowercase__     =  config

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

               lowercase__     =  label_smoothed_nll_loss


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			int)	->				Any:




         """simple docstring"""

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

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


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			List[Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
               lowercase__     =  schedule_func(self.optimizer)
         elif self.args.lr_scheduler == "constant_w_warmup":
               lowercase__     =  schedule_func(self.optimizer			,				num_warmup_steps=self.args.warmup_steps)
         else:
               lowercase__     =  schedule_func(
                   self.optimizer			,				num_warmup_steps=self.args.warmup_steps			,				num_training_steps=lowerCAmelCase)
         return scheduler


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[torch.utils.data.Sampler]:




         """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 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[int])	->				Tuple:




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


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Dict)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  inputs.pop('labels')
         lowercase__,	lowercase__     =  self._compute_loss(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
         return loss


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			nn.Module			,				lowerCAmelCase  :			Dict[str, Union[torch.Tensor, Any]]			,				lowerCAmelCase  :			bool			,				lowerCAmelCase  :			Optional[List[str]] = None			,				)	->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:




         """simple docstring"""

         lowercase__     =  self._prepare_inputs(lowerCAmelCase)

         lowercase__     =  {
             '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:
               lowercase__     =  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"]:
                     lowercase__     =  self._pad_tensors_to_max_len(lowerCAmelCase			,				gen_kwargs['max_length'])

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

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

         lowercase__     =  generated_tokens if self.args.predict_with_generate else logits

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

         return (loss, logits, labels)



   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Tuple)	->				Tuple:




         """simple docstring"""

         lowercase__     =  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}''')

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





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class       UpperCAmelCase__(							unittest.TestCase     ,							lowerCamelCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[str])	->				Any:




         """simple docstring"""

         lowercase__     =  load_tool('text-classification')
         self.tool.setup()
         lowercase__     =  load_tool('text-classification'			,				remote=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.remote_tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')



   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.remote_tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')
