
from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Any:
					lowercase_       =					data
					lowercase_       =					None

class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Any       )       ->     List[Any]:
					lowercase_       =					None
					lowercase_       =					None
	def __iter__(       self  :			int       )       ->     Iterator[Any]:
					lowercase_       =					self.head
					while self.head:
									yield node.data
									lowercase_       =					node.next
									if node == self.head:
													break
	def __len__(       self  :			str       )       ->     int:
					return sum(1 for _ in self       )
	def __repr__(       self  :			Optional[Any]       )       ->     Tuple:
					return "->".join(str(SCREAMING_SNAKE_CASE_       ) for item in iter(self       )       )
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     None:
					self.insert_nth(len(self       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     None:
					self.insert_nth(0       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     None:
					if index < 0 or index > len(self       ):
									raise IndexError('''list index out of range.'''       )
					lowercase_       =					Node(SCREAMING_SNAKE_CASE_       )
					if self.head is None:
									lowercase_       =					new_node  # first node points itself
									lowercase_       =					lowercase_       =					new_node
					elif index == 0:  # insert at head
									lowercase_       =					self.head
									lowercase_       =					lowercase_       =					new_node
					else:
									lowercase_       =					self.head
									for _ in range(index - 1       ):
													lowercase_       =					temp.next
									lowercase_       =					temp.next
									lowercase_       =					new_node
									if index == len(self       ) - 1:  # insert at tail
													lowercase_       =					new_node
	def 						_lowercase  (       self  :			int       )       ->     List[Any]:
					return self.delete_nth(0       )
	def 						_lowercase  (       self  :			str       )       ->     Any:
					return self.delete_nth(len(self       ) - 1       )
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int = 0       )       ->     Any:
					if not 0 <= index < len(self       ):
									raise IndexError('''list index out of range.'''       )
					lowercase_       =					self.head
					if self.head == self.tail:  # just one node
									lowercase_       =					lowercase_       =					None
					elif index == 0:  # delete head node
									lowercase_       =					self.tail.next.next
									lowercase_       =					self.head.next
					else:
									lowercase_       =					self.head
									for _ in range(index - 1       ):
													lowercase_       =					temp.next
									lowercase_       =					temp.next
									lowercase_       =					temp.next.next
									if index == len(self       ) - 1:  # delete at tail
													lowercase_       =					temp
					return delete_node.data




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     bool:
					return len(self       ) == 0




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					CircularLinkedList()
				assert len(snake_case__					) == 0
				assert circular_linked_list.is_empty() is True
				assert str(snake_case__					) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1					)
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0					)
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5					):
								assert len(snake_case__					) == i
								circular_linked_list.insert_nth(snake_case__						,   i + 1					)
				assert str(snake_case__					) == "->".join(str(snake_case__					) for i in range(1						,   6					)					)

				circular_linked_list.insert_tail(6					)
				assert str(snake_case__					) == "->".join(str(snake_case__					) for i in range(1						,   7					)					)
				circular_linked_list.insert_head(0					)
				assert str(snake_case__					) == "->".join(str(snake_case__					) for i in range(0						,   7					)					)

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(snake_case__					) == "->".join(str(snake_case__					) for i in range(1						,   6					)					)
				assert circular_linked_list.delete_nth(2					) == 3

				circular_linked_list.insert_nth(2						,   3					)
				assert str(snake_case__					) == "->".join(str(snake_case__					) for i in range(1						,   6					)					)

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
						import doctest

						doctest.testmod()






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


__a  			=					logging.get_logger(__name__)

__a  			=					{
    '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__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=None       ,							*SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     List[str]:
					super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if config is None:
									assert isinstance(self.model       ,							SCREAMING_SNAKE_CASE_       ), (
									    "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       ,							SCREAMING_SNAKE_CASE_       ) 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 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Optional[Any]:
					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=SCREAMING_SNAKE_CASE_       ,							optim=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)
									else:
													lowercase_       =					optimizer_cls(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if self.lr_scheduler is None:
									lowercase_       =					self._get_lr_scheduler(SCREAMING_SNAKE_CASE_       )
					else:  # ignoring --lr_scheduler
									logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''       )
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Dict:
					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=SCREAMING_SNAKE_CASE_       )
					return scheduler
	def 						_lowercase  (       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 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     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
													lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[0]
													lowercase_       =					self.loss_fn(logits.view(-1       ,							logits.shape[-1]       )       ,							labels.view(-1       )       )
									else:
													# compute usual loss via models
													lowercase_  ,	lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[:2]
					else:
									# compute label smoothed loss
									lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[0]
									lowercase_       =					torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,							dim=-1       )
									lowercase_  ,	lowercase_       =					self.loss_fn(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							self.args.label_smoothing       ,							ignore_index=self.config.pad_token_id       )
					return loss, logits
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     List[Any]:
					lowercase_       =					inputs.pop('''labels'''       )
					lowercase_  ,	lowercase_       =					self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					return loss
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			nn.Module       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, Union[torch.Tensor, Any]]       ,							SCREAMING_SNAKE_CASE_  :			bool       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[str]] = None       ,							)       ->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
					lowercase_       =					self._prepare_inputs(SCREAMING_SNAKE_CASE_       )

					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''']       ,							**SCREAMING_SNAKE_CASE_       ,							)
									# 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(SCREAMING_SNAKE_CASE_       ,							gen_kwargs['''max_length''']       )

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

					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(SCREAMING_SNAKE_CASE_       ,							gen_kwargs['''max_length''']       )

					return (loss, logits, labels)




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Tuple:
					# If PAD token is not defined at least EOS token has to be defined
					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




