





'''simple docstring'''







def    __UpperCamelCase	(	a    : Dict					,   a    : Dict     ) ->Tuple:
 snake_case				=			len(a     )
 print('''The following activities are selected:'''     )

 # The first activity is always selected
 snake_case				=			0
 print(a					,   end=''','''     )

 # Consider rest of the activities
 for j in range(a     ):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(a					,   end=''','''     )
   snake_case				=			j


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _lowercase       						=			[1, 3, 0, 5, 8, 5]
   _lowercase       						=			[2, 4, 6, 7, 9, 9]
   print_max_activities(start, finish)








'''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


_lowercase       						=			logging.get_logger(__name__)

_lowercase       						=			{
    '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 			_lowercase						(       __a					):







     def __init__( self    ,   A__=None    ,   A__=None    ,   *A__    ,   **A__    ) ->	Union[str, Any]:
      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__}"""
       )
       snake_case				=			self.model.config
      else:
       snake_case				=			config

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

       snake_case				=			label_smoothed_nll_loss







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

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







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
      snake_case				=			arg_to_scheduler[self.args.lr_scheduler]
      if self.args.lr_scheduler == "constant":
       snake_case				=			schedule_func(self.optimizer    )
      elif self.args.lr_scheduler == "constant_w_warmup":
       snake_case				=			schedule_func(self.optimizer    ,   num_warmup_steps=self.args.warmup_steps    )
      else:
       snake_case				=			schedule_func(
           self.optimizer    ,   num_warmup_steps=self.args.warmup_steps    ,   num_training_steps=A__    )
      return scheduler







     def     UpperCamelCase			( self    ) ->	Optional[torch.utils.data.Sampler]:
      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    ,   A__    ,   A__    ,   A__    ) ->	List[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
        snake_case				=			model(**A__    ,   use_cache=A__    )[0]
        snake_case				=			self.loss_fn(logits.view(-1    ,   logits.shape[-1]    )    ,   labels.view(-1    )    )
       else:
        # compute usual loss via models
        snake_case				,		snake_case				=			model(**A__    ,   labels=A__    ,   use_cache=A__    )[:2]
      else:
       # compute label smoothed loss
       snake_case				=			model(**A__    ,   use_cache=A__    )[0]
       snake_case				=			torch.nn.functional.log_softmax(A__    ,   dim=-1    )
       snake_case				,		snake_case				=			self.loss_fn(A__    ,   A__    ,   self.args.label_smoothing    ,   ignore_index=self.config.pad_token_id    )
      return loss, logits







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Any:
      snake_case				=			inputs.pop('''labels'''    )
      snake_case				,		snake_case				=			self._compute_loss(A__    ,   A__    ,   A__    )
      return loss







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__ = None    ,   ) ->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
      snake_case				=			self._prepare_inputs(A__    )

      snake_case				=			{
          '''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:
       snake_case				=			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"]:
        snake_case				=			self._pad_tensors_to_max_len(A__    ,   gen_kwargs['''max_length''']    )

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

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

      snake_case				=			generated_tokens if self.args.predict_with_generate else logits

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

      return (loss, logits, labels)







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[str]:
      # If PAD token is not defined at least EOS token has to be defined
      snake_case				=			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}"""    )

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

