


'''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				:Tuple        =      logging.get_logger(__name__)

lowerCAmelCase				: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 _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Tuple  ,					_A		:						Union[str, Any]=None  ,					_A		:						Tuple=None  ,					*_A		:						Dict  ,					**_A		:						Optional[Any]      )  ->			Tuple:
			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__}'
				)
				__magic_name__	:   Tuple					     =					self.model.config
			else:
				__magic_name__	:   Optional[Any]					     =					config

			__magic_name__	:   str					     =					data_args
			__magic_name__	:   Any					     =					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:
				__magic_name__	:   List[str]					     =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id      )
			else:
				# dynamically import label_smoothed_nll_loss
				from utils import label_smoothed_nll_loss

				__magic_name__	:   int					     =					label_smoothed_nll_loss







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

			if self.lr_scheduler is None:
				__magic_name__	:   Tuple					     =					self._get_lr_scheduler(_A      )
			else:  # ignoring --lr_scheduler
				logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Tuple      )  ->			str:
			__magic_name__	:   int					     =					arg_to_scheduler[self.args.lr_scheduler]
			if self.args.lr_scheduler == "constant":
				__magic_name__	:   Dict					     =					schedule_func(self.optimizer      )
			elif self.args.lr_scheduler == "constant_w_warmup":
				__magic_name__	:   List[str]					     =					schedule_func(self.optimizer  ,					num_warmup_steps=self.args.warmup_steps      )
			else:
				__magic_name__	:   Dict					     =					schedule_func(
				    self.optimizer  ,					num_warmup_steps=self.args.warmup_steps  ,					num_training_steps=_A      )
			return scheduler







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







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						Any  ,					_A		:						int      )  ->			Union[str, Any]:
			__magic_name__	:   Tuple					     =					inputs.pop('labels'      )
			__magic_name__	:   Dict					     =					self._compute_loss(_A  ,					_A  ,					_A      )
			return loss







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						nn.Module  ,					_A		:						Dict[str, Union[torch.Tensor, Any]]  ,					_A		:						bool  ,					_A		:						Optional[List[str]] = None  ,					)  ->			Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
			__magic_name__	:   Optional[int]					     =					self._prepare_inputs(_A      )

			__magic_name__	:   Optional[Any]					     =					{
			    '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:
				__magic_name__	:   Tuple					     =					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"]:
					__magic_name__	:   Union[str, Any]					     =					self._pad_tensors_to_max_len(_A  ,					gen_kwargs['max_length']      )

			__magic_name__	:   Union[str, Any]					     =					inputs.pop('labels'      )
			with torch.no_grad():
				# compute loss on predict data
				__magic_name__	:   Any					     =					self._compute_loss(_A  ,					_A  ,					_A      )

			__magic_name__	:   List[str]					     =					loss.mean().detach()
			if self.args.prediction_loss_only:
				return (loss, None, None)

			__magic_name__	:   List[Any]					     =					generated_tokens if self.args.predict_with_generate else logits

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

			return (loss, logits, labels)







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						Dict  ,					_A		:						Tuple      )  ->			Union[str, Any]:
			# If PAD token is not defined at least EOS token has to be defined
			__magic_name__	:   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}'      )

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



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase				:Any        =      TypeVar('''T''')





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (position - 1) // 2





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 1





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 2
class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   list[tuple[T, int]]					     =					[]
			__magic_name__	:   dict[T, int]					     =					{}
			__magic_name__	:   int					     =					0







		def __len__( self		:						Optional[int]      )  ->			int:
			return self.elements







		def __repr__( self		:						Optional[Any]      )  ->			str:
			return str(self.heap      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			bool:
			# Check if the priority queue is empty
			return self.elements == 0







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)      )
			__magic_name__	:   List[str]					     =					self.elements
			self.elements += 1
			self._bubble_up(_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
				self._swap_nodes(0  ,					self.elements - 1      )
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
				__magic_name__     ,    __magic_name__	:   Tuple					     =					self.heap[0]
				self._bubble_down(_A      )
			return elem







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Update the weight of the given key
			__magic_name__	:   Optional[Any]					     =					self.position_map[elem]
			__magic_name__	:   Any					     =					(elem, weight)
			if position > 0:
				__magic_name__	:   Optional[int]					     =					get_parent_position(_A      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[parent_position]
				if parent_weight > weight:
					self._bubble_up(_A      )
				else:
					self._bubble_down(_A      )
			else:
				self._bubble_down(_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			__magic_name__	:   Union[str, Any]					     =					self.position_map[elem]
			if curr_pos == 0:
				return None
			__magic_name__	:   Optional[Any]					     =					get_parent_position(_A      )
			__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[curr_pos]
			__magic_name__     ,    __magic_name__	:   int					     =					self.heap[parent_position]
			if parent_weight > weight:
				self._swap_nodes(_A  ,					_A      )
				return self._bubble_up(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			__magic_name__	:   Dict					     =					self.position_map[elem]
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.heap[curr_pos]
			__magic_name__	:   str					     =					get_child_left_position(_A      )
			__magic_name__	:   List[str]					     =					get_child_right_position(_A      )
			if child_left_position < self.elements and child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_left_position]
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_right_position]
				if child_right_weight < child_left_weight and child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			if child_left_position < self.elements:
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[child_left_position]
				if child_left_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			else:
				return None
			if child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   str					     =					self.heap[child_right_position]
				if child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						int  ,					_A		:						int      )  ->			None:
			# Swap the nodes at the given positions
			__magic_name__	:   List[Any]					     =					self.heap[nodea_pos][0]
			__magic_name__	:   Union[str, Any]					     =					self.heap[nodea_pos][0]
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					(
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__magic_name__	:   Optional[Any]					     =					nodea_pos
			__magic_name__	:   Optional[Any]					     =					nodea_pos







class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   dict[T, dict[T, int]]					     =					{}
			__magic_name__	:   int					     =					0







		def __repr__( self		:						str      )  ->			str:
			return str(self.connections      )







		def __len__( self		:						Union[str, Any]      )  ->			int:
			return self.nodes







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T      )  ->			None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
				__magic_name__	:   str					     =					{}
				self.nodes += 1







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						T  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_A      )
			self.add_node(_A      )
			__magic_name__	:   Any					     =					weight
			__magic_name__	:   Dict					     =					weight







def lowerCamelCase      (      lowerCAmelCase    :     GraphUndirectedWeighted[T]		,		):





	"""simple docstring"""







	__magic_name__	:   dict[T, int]					     =					{node: maxsize for node in graph.connections}
	__magic_name__	:   dict[T, T | None]					     =					{node: None for node in graph.connections}

	__magic_name__	:   MinPriorityQueue[T]					     =					MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowerCAmelCase		,		lowerCAmelCase		)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	__magic_name__	:   Any					     =					priority_queue.extract_min()
	__magic_name__	:   List[str]					     =					0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			__magic_name__	:   Any					     =					dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
			__magic_name__	:   Tuple					     =					node

    # running prim's algorithm
	while not priority_queue.is_empty():
		__magic_name__	:   Tuple					     =					priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				__magic_name__	:   int					     =					dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
				__magic_name__	:   Union[str, Any]					     =					node
	return dist, parent