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

_lowerCAmelCase  :Union[str, 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 (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__=None    ,							lowercase__=None    ,							*lowercase__    ,							**lowercase__ )							-> Tuple:
         super().__init__(*lowercase__    ,							**lowercase__ )

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

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       data_args
         SCREAMING_SNAKE_CASE					:			Any    	=       self.config.tgt_vocab_size if isinstance(self.config    ,							lowercase__ ) 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:
              SCREAMING_SNAKE_CASE					:			Dict    	=       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
         else:
              # dynamically import label_smoothed_nll_loss
              from utils import label_smoothed_nll_loss

              SCREAMING_SNAKE_CASE					:			Dict    	=       label_smoothed_nll_loss






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

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






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       schedule_func(self.optimizer )
         elif self.args.lr_scheduler == "constant_w_warmup":
              SCREAMING_SNAKE_CASE					:			Tuple    	=       schedule_func(self.optimizer    ,							num_warmup_steps=self.args.warmup_steps )
         else:
              SCREAMING_SNAKE_CASE					:			int    	=       schedule_func(
                  self.optimizer    ,							num_warmup_steps=self.args.warmup_steps    ,							num_training_steps=lowercase__ )
         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    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> 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
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(**lowercase__    ,							use_cache=lowercase__ )[0]
                   SCREAMING_SNAKE_CASE					:			int    	=       self.loss_fn(logits.view(-1    ,							logits.shape[-1] )    ,							labels.view(-1 ) )
              else:
                   # compute usual loss via models
                   SCREAMING_SNAKE_CASE					:			int    	=       model(**lowercase__    ,							labels=lowercase__    ,							use_cache=lowercase__ )[:2]
         else:
              # compute label smoothed loss
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(**lowercase__    ,							use_cache=lowercase__ )[0]
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.nn.functional.log_softmax(lowercase__    ,							dim=-1 )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.loss_fn(lowercase__    ,							lowercase__    ,							self.args.label_smoothing    ,							ignore_index=self.config.pad_token_id )
         return loss, logits






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       inputs.pop('labels' )
         SCREAMING_SNAKE_CASE					:			str    	=       self._compute_loss(lowercase__    ,							lowercase__    ,							lowercase__ )
         return loss






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							)							-> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         SCREAMING_SNAKE_CASE					:			str    	=       self._prepare_inputs(lowercase__ )

         SCREAMING_SNAKE_CASE					:			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:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model.generate(
                  inputs['input_ids']    ,							attention_mask=inputs['attention_mask']    ,							**lowercase__    ,							)
              # in case the batch is shorter than max length, the output should be padded
              if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._pad_tensors_to_max_len(lowercase__    ,							gen_kwargs['max_length'] )

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

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       loss.mean().detach()
         if self.args.prediction_loss_only:
              return (loss, None, None)

         SCREAMING_SNAKE_CASE					:			int    	=       generated_tokens if self.args.predict_with_generate else logits

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

         return (loss, logits, labels)






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

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




'''simple docstring'''





def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number < 0 or shift_amount < 0:
          raise ValueError('both inputs must be positive integers'		)

     SCREAMING_SNAKE_CASE					:			str    	=       str(bin(a_		)		)
     binary_number += "0" * shift_amount
     return binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number < 0 or shift_amount < 0:
          raise ValueError('both inputs must be positive integers'		)

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       str(bin(a_		)		)[2:]
     if shift_amount >= len(a_		):
          return "0b0"
     SCREAMING_SNAKE_CASE					:			Dict    	=       binary_number[: len(a_		) - shift_amount]
     return "0b" + shifted_binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number >= 0:  # Get binary representation of positive number
          SCREAMING_SNAKE_CASE					:			Tuple    	=       '0' + str(bin(a_		)		).strip('-'		)[2:]
     else:  # Get binary (2's complement) representation of negative number
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(bin(a_		)[3:]		)  # Find 2's complement of number
          SCREAMING_SNAKE_CASE					:			Any    	=       bin(abs(a_		) - (1 << binary_number_length)		)[3:]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
              '1' + '0' * (binary_number_length - len(a_		)) + binary_number
          )

     if shift_amount >= len(a_		):
          return "0b" + binary_number[0] * len(a_		)
     return (
         "0b"
         + binary_number[0] * shift_amount
         + binary_number[: len(a_		) - shift_amount]
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



