





'''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


_SCREAMING_SNAKE_CASE     :   Any					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   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 							__lowercase       (      __snake_case						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							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__}"
							)
							__lowercase										=							self.model.config
				else:
							__lowercase										=							config

				__lowercase										=							data_args
				__lowercase										=							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:
							__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 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				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=A_     ,optim=A_     ,**A_     ,)
							else:
										__lowercase										=							optimizer_cls(A_     ,**A_ )

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__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=A_ )
				return scheduler


	def 						_UpperCAmelCase     (self )							->							Any:







				'''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     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Optional[Any]:







				'''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
										__lowercase										=							model(**A_     ,use_cache=A_ )[0]
										__lowercase										=							self.loss_fn(logits.view(-1     ,logits.shape[-1] )     ,labels.view(-1 ) )
							else:
										# compute usual loss via models
										__lowercase										=							model(**A_     ,labels=A_     ,use_cache=A_ )[:2]
				else:
							# compute label smoothed loss
							__lowercase										=							model(**A_     ,use_cache=A_ )[0]
							__lowercase										=							torch.nn.functional.log_softmax(A_     ,dim=-1 )
							__lowercase										=							self.loss_fn(A_     ,A_     ,self.args.label_smoothing     ,ignore_index=self.config.pad_token_id )
				return loss, logits


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							inputs.pop('''labels''' )
				__lowercase										=							self._compute_loss(A_     ,A_     ,A_ )
				return loss


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							self._prepare_inputs(A_ )

				__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''']     ,**A_     ,)
							# 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(A_     ,gen_kwargs['''max_length'''] )

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

				__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(A_     ,gen_kwargs['''max_length'''] )

				return (loss, logits, labels)


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				__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












'''simple docstring'''



from __future__ import annotations

from typing import Any






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							num_of_nodes
				__lowercase										=							[]
				__lowercase										=							{}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				self.m_edges.append([u_node, v_node, weight] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				if self.m_component[u_node] == u_node:
							return u_node
				return self.find_component(self.m_component[u_node] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if self.m_component[u_node] != u_node:
							for k in self.m_component:
										__lowercase										=							self.find_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if component_size[u_node] <= component_size[v_node]:
							__lowercase										=							v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(_lowerCamelCase )

				elif component_size[u_node] >= component_size[v_node]:
							__lowercase										=							self.find_component(_lowerCamelCase )
							component_size[u_node] += component_size[v_node]
							self.set_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							None:







				'''simple docstring'''





				__lowercase										=							[]
				__lowercase										=							0

				__lowercase										=							[-1] * self.m_num_of_nodes

				# A list of components (initialized to all of the nodes)
				for node in range(self.m_num_of_nodes ):
							self.m_component.update({node: node} )
							component_size.append(1 )

				__lowercase										=							self.m_num_of_nodes

				while num_of_components > 1:
							for edge in self.m_edges:
										__lowercase						,		__lowercase						,		__lowercase										=							edge

										__lowercase										=							self.m_component[u]
										__lowercase										=							self.m_component[v]

										if u_component != v_component:

													for component in (u_component, v_component):
																if (
																    minimum_weight_edge[component] == -1
																    or minimum_weight_edge[component][2] > w
																):
																			__lowercase										=							[u, v, w]

							for edge in minimum_weight_edge:
										if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
													__lowercase						,		__lowercase						,		__lowercase										=							edge

													__lowercase										=							self.m_component[u]
													__lowercase										=							self.m_component[v]

													if u_component != v_component:
																mst_weight += w
																self.union(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )
																print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
																num_of_components -= 1

							__lowercase										=							[-1] * self.m_num_of_nodes
				print(f"The total weight of the minimal spanning tree is: {mst_weight}" )





def    _lowerCAmelCase						(				):
			pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





