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

_UpperCAmelCase      :				str          = {
    "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      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_=None       ,   A_=None       ,   *A_       ,   **A_ )						->      List[Any]:
				"""simple docstring"""
				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__}'''
						)
						UpperCamelCase						=  self.model.config
				else:
						UpperCamelCase						=  config

				UpperCamelCase						=  data_args
				UpperCamelCase						=  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:
						UpperCamelCase						=  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						=  label_smoothed_nll_loss



		def __UpperCamelCase				(				self       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				if self.optimizer is None:
						UpperCamelCase						=  ['bias', 'LayerNorm.weight']
						UpperCamelCase						=  [
						    {
						        '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						=  Adafactor if self.args.adafactor else AdamW
						if self.args.adafactor:
								UpperCamelCase						=  Adafactor
								UpperCamelCase						=  {'scale_parameter': False, 'relative_step': False}
						else:
								UpperCamelCase						=  AdamW
								UpperCamelCase						=  {
								    'betas': (self.args.adam_betaa, self.args.adam_betaa),
								    'eps': self.args.adam_epsilon,
								}
						UpperCamelCase						=  self.args.learning_rate
						if self.sharded_ddp:
								UpperCamelCase						=  OSS(
								    params=A_       ,   optim=A_       ,   **A_       ,   )
						else:
								UpperCamelCase						=  optimizer_cls(A_       ,   **A_ )

				if self.lr_scheduler is None:
						UpperCamelCase						=  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_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  arg_to_scheduler[self.args.lr_scheduler]
				if self.args.lr_scheduler == "constant":
						UpperCamelCase						=  schedule_func(self.optimizer )
				elif self.args.lr_scheduler == "constant_w_warmup":
						UpperCamelCase						=  schedule_func(self.optimizer       ,   num_warmup_steps=self.args.warmup_steps )
				else:
						UpperCamelCase						=  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]:
				"""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       ,   A_       ,   A_       ,   A_ )						->      Dict:
				"""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
								UpperCamelCase						=  model(**A_       ,   use_cache=A_ )[0]
								UpperCamelCase						=  self.loss_fn(logits.view(-1       ,   logits.shape[-1] )       ,   labels.view(-1 ) )
						else:
								# compute usual loss via models
								UpperCamelCase			,						UpperCamelCase						=  model(**A_       ,   labels=A_       ,   use_cache=A_ )[:2]
				else:
						# compute label smoothed loss
						UpperCamelCase						=  model(**A_       ,   use_cache=A_ )[0]
						UpperCamelCase						=  torch.nn.functional.log_softmax(A_       ,   dim=-1 )
						UpperCamelCase			,						UpperCamelCase						=  self.loss_fn(A_       ,   A_       ,   self.args.label_smoothing       ,   ignore_index=self.config.pad_token_id )
				return loss, logits



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  inputs.pop('labels' )
				UpperCamelCase			,						UpperCamelCase						=  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]]:
				"""simple docstring"""
				UpperCamelCase						=  self._prepare_inputs(A_ )

				UpperCamelCase						=  {
				    '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						=  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"]:
								UpperCamelCase						=  self._pad_tensors_to_max_len(A_       ,   gen_kwargs['max_length'] )

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

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

				UpperCamelCase						=  generated_tokens if self.args.predict_with_generate else logits

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

				return (loss, logits, labels)



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      List[str]:
				"""simple docstring"""
				# If PAD token is not defined at least EOS token has to be defined
				UpperCamelCase						=  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						=  pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)       ,   dtype=tensor.dtype       ,   device=tensor.device )
				UpperCamelCase						=  tensor
				return padded_tensor


import os

_UpperCAmelCase      :				int          = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}


def    A (      lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		UpperCamelCase						=  0
		while index < len(lowercase				) - 1:
				UpperCamelCase						=  SYMBOLS[numerals[index]]
				UpperCamelCase						=  SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''

		UpperCamelCase						=  num // 1_000
		numerals += m_count * "M"
		num %= 1_000

		UpperCamelCase						=  num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		UpperCamelCase						=  num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals


def    A (      lowercase = "/p089_roman.txt"				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		with open(os.path.dirname(lowercase				) + roman_numerals_filename				) as filea:
				UpperCamelCase						=  filea.readlines()

		for line in lines:
				UpperCamelCase						=  line.strip()
				UpperCamelCase						=  parse_roman_numerals(lowercase				)
				UpperCamelCase						=  generate_roman_numerals(lowercase				)
				savings += len(lowercase				) - len(lowercase				)

		return savings


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