


"""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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    '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    lowerCamelCase__						(		UpperCamelCase_    ):

      '''simple docstring'''





      def __init__(		self	:	Union[str, Any]      ,       A_	:	Union[str, Any]=None      ,       A_	:	Optional[int]=None      ,       *A_	:	Dict      ,       **A_	:	str							):






       '''simple docstring'''


       super().__init__(*UpperCamelCase__      ,       **UpperCamelCase__							)

       if config is None:
        assert isinstance(self.model      ,       UpperCamelCase__							), (
            "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      ,       UpperCamelCase__							) 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 SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	int							):






       '''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=UpperCamelCase__      ,       optim=UpperCamelCase__      ,       **UpperCamelCase__      ,       )
        else:
         __lowercase									=	optimizer_cls(UpperCamelCase__      ,       **UpperCamelCase__							)

       if self.lr_scheduler is None:
        __lowercase									=	self._get_lr_scheduler(UpperCamelCase__							)
       else:  # ignoring --lr_scheduler
        logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[Any]							):






       '''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=UpperCamelCase__							)
       return scheduler
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






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






       '''simple docstring'''


       __lowercase									=	inputs.pop("""labels"""							)
       __lowercase									=	self._compute_loss(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__							)
       return loss
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	nn.Module      ,       A_	:	Dict[str, Union[torch.Tensor, Any]]      ,       A_	:	bool      ,       A_	:	Optional[List[str]] = None      ,       ):






       '''simple docstring'''


       __lowercase									=	self._prepare_inputs(UpperCamelCase__							)

       __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"""]      ,       **UpperCamelCase__      ,       )
        # 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(UpperCamelCase__      ,       gen_kwargs["""max_length"""]							)

       __lowercase									=	inputs.pop("""labels"""							)
       with torch.no_grad():
        # compute loss on predict data
        __lowercase									=	self._compute_loss(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__							)

       __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(UpperCamelCase__      ,       gen_kwargs["""max_length"""]							)

       return (loss, logits, labels)






      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]							):






       '''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




"""simple docstring"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
