

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     						=      logging.get_logger(__name__)

_UpperCAmelCase     						=      {
    """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      ):
  '''simple docstring'''







  def __init__(			self						:			Dict		,    UpperCamelCase__						:			Optional[int]=None		,    UpperCamelCase__						:			List[str]=None		,    *UpperCamelCase__						:			Optional[Any]		,    **UpperCamelCase__						:			Tuple			):
        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__}'''
              )
              A 			=					self.model.config
        else:
              A 			=					config

        A 			=					data_args
        A 			=					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:
              A 			=					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id			)
        else:
              # dynamically import label_smoothed_nll_loss
              from utils import label_smoothed_nll_loss

              A 			=					label_smoothed_nll_loss







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

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







  def 						UpperCamelCase							(			self						:			int		,    UpperCamelCase__						:			int			):
        A 			=					arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
              A 			=					schedule_func(self.optimizer			)
        elif self.args.lr_scheduler == "constant_w_warmup":
              A 			=					schedule_func(self.optimizer		,    num_warmup_steps=self.args.warmup_steps			)
        else:
              A 			=					schedule_func(
                  self.optimizer		,    num_warmup_steps=self.args.warmup_steps		,    num_training_steps=__lowerCAmelCase			)
        return scheduler







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







  def 						UpperCamelCase							(			self						:			List[Any]		,    UpperCamelCase__						:			int		,    UpperCamelCase__						:			List[Any]			):
        A 			=					inputs.pop('labels'			)
        A 			=					self._compute_loss(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase			)
        return loss







  def 						UpperCamelCase							(			self						:			Optional[Any]		,    UpperCamelCase__						:			int		,    UpperCamelCase__						:			Optional[Any]		,    UpperCamelCase__						:			Any		,    UpperCamelCase__						:			List[str] = None		,    ):
        A 			=					self._prepare_inputs(__lowerCAmelCase			)

        A 			=					{
            '''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:
              A 			=					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"]:
                    A 			=					self._pad_tensors_to_max_len(__lowerCAmelCase		,    gen_kwargs['max_length']			)

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

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

        A 			=					generated_tokens if self.args.predict_with_generate else logits

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

        return (loss, logits, labels)







  def 						UpperCamelCase							(			self						:			Optional[Any]		,    UpperCamelCase__						:			List[Any]		,    UpperCamelCase__						:			Union[str, Any]			):
        A 			=					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}'''			)

        A 			=					pad_token_id * torch.ones(
            (tensor.shape[0], max_length)		,    dtype=tensor.dtype		,    device=tensor.device			)
        A 			=					tensor
        return padded_tensor











from __future__ import annotations

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''Vertex\tShortest Distance from vertex {src}'''			)
				for i, d in enumerate(snake_case			):
								print(f'''{i}\t\t{d}'''			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				for j in range(snake_case			):
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
								if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
												return True
				return False

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   [float('''inf'''			)] * vertex_count
				__magic_name__		:Tuple   =   0.0

				for _ in range(vertex_count - 1			):
								for j in range(snake_case			):
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
																__magic_name__		:Tuple   =   distance[u] + w

				__magic_name__		:Tuple   =   check_negative_cycle(snake_case,	snake_case,	snake_case			)
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''			)

				return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Tuple				=		int(input("""Enter number of vertices: """).strip())
					SCREAMING_SNAKE_CASE__   :      Any				=		int(input("""Enter number of edges: """).strip())

					SCREAMING_SNAKE_CASE__   :      list[dict[str, int]]				=		[{} for _ in range(E)]

					for i in range(E):
										print("""Edge """, i + 1)
										SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__   :      Dict				=		(
										    int(x)
										    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
										)
										SCREAMING_SNAKE_CASE__   :      Dict				=		{"""src""": src, """dst""": dest, """weight""": weight}

					SCREAMING_SNAKE_CASE__   :      List[Any]				=		int(input("""\nEnter shortest path source:""").strip())
					SCREAMING_SNAKE_CASE__   :      List[str]				=		bellman_ford(graph, V, E, source)
					print_distance(shortest_distance, 0)






