






"""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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '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_ (__lowercase  ):





 def __init__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				List[Any]=None			,			lowerCAmelCase_      :				Any=None			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				int		)		->    Dict:
    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__}"""
       )
       UpperCAmelCase_  :				Optional[Any]      =      self.model.config
    else:
       UpperCAmelCase_  :				Tuple      =      config

    UpperCAmelCase_  :				int      =      data_args
    UpperCAmelCase_  :				Union[str, Any]      =      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:
       UpperCAmelCase_  :				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

       UpperCAmelCase_  :				List[str]      =      label_smoothed_nll_loss





 def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				int		)		->    Union[str, Any]:
    if self.optimizer is None:
       UpperCAmelCase_  :				str      =      ["bias", "LayerNorm.weight"]
       UpperCAmelCase_  :				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,
           },
       ]
       UpperCAmelCase_  :				Dict      =      Adafactor if self.args.adafactor else AdamW
       if self.args.adafactor:
          UpperCAmelCase_  :				Dict      =      Adafactor
          UpperCAmelCase_  :				Tuple      =      {"scale_parameter": False, "relative_step": False}
       else:
          UpperCAmelCase_  :				Union[str, Any]      =      AdamW
          UpperCAmelCase_  :				List[Any]      =      {
              "betas": (self.args.adam_betaa, self.args.adam_betaa),
              "eps": self.args.adam_epsilon,
          }
       UpperCAmelCase_  :				Any      =      self.args.learning_rate
       if self.sharded_ddp:
          UpperCAmelCase_  :				Dict      =      OSS(
              params=lowerCAmelCase_			,			optim=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
       else:
          UpperCAmelCase_  :				Tuple      =      optimizer_cls(lowerCAmelCase_			,			**lowerCAmelCase_		)

    if self.lr_scheduler is None:
       UpperCAmelCase_  :				int      =      self._get_lr_scheduler(lowerCAmelCase_		)
    else:  # ignoring --lr_scheduler
       logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."		)





 def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				str		)		->    Any:
    UpperCAmelCase_  :				str      =      arg_to_scheduler[self.args.lr_scheduler]
    if self.args.lr_scheduler == "constant":
       UpperCAmelCase_  :				Dict      =      schedule_func(self.optimizer		)
    elif self.args.lr_scheduler == "constant_w_warmup":
       UpperCAmelCase_  :				Union[str, Any]      =      schedule_func(self.optimizer			,			num_warmup_steps=self.args.warmup_steps		)
    else:
       UpperCAmelCase_  :				Tuple      =      schedule_func(
           self.optimizer			,			num_warmup_steps=self.args.warmup_steps			,			num_training_steps=lowerCAmelCase_		)
    return scheduler





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





 def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[Any]		)		->    int:
    UpperCAmelCase_  :				Dict      =      inputs.pop("labels"		)
    UpperCAmelCase_  :				Any      =      self._compute_loss(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
    return loss





 def   _SCREAMING_SNAKE_CASE   (   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]]:
    UpperCAmelCase_  :				Union[str, Any]      =      self._prepare_inputs(lowerCAmelCase_		)

    UpperCAmelCase_  :				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:
       UpperCAmelCase_  :				str      =      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"]:
          UpperCAmelCase_  :				Dict      =      self._pad_tensors_to_max_len(lowerCAmelCase_			,			gen_kwargs["max_length"]		)

    UpperCAmelCase_  :				Any      =      inputs.pop("labels"		)
    with torch.no_grad():
       # compute loss on predict data
       UpperCAmelCase_  :				List[str]      =      self._compute_loss(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

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

    UpperCAmelCase_  :				Optional[int]      =      generated_tokens if self.args.predict_with_generate else logits

    if labels.shape[-1] < gen_kwargs["max_length"]:
       UpperCAmelCase_  :				str      =      self._pad_tensors_to_max_len(lowerCAmelCase_			,			gen_kwargs["max_length"]		)

    return (loss, logits, labels)





 def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				int		)		->    Optional[int]:
    # If PAD token is not defined at least EOS token has to be defined
    UpperCAmelCase_  :				Optional[int]      =      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}"""		)

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











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


if __name__ == "__main__":
			print(f'{solution() = }')



