

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__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "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 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: str			,						lowerCAmelCase__		: str=None			,						lowerCAmelCase__		: int=None			,						*lowerCAmelCase__		: int			,						**lowerCAmelCase__		: List[str]    )    ->      str:
   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__}"
     )
     UpperCAmelCase					      =  self.model.config
   else:
     UpperCAmelCase					      =  config

   UpperCAmelCase					      =  data_args
   UpperCAmelCase					      =  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:
     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		: Any			,						lowerCAmelCase__		: int    )    ->      Optional[Any]:
   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=lowerCAmelCase__			,						optim=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
     else:
       UpperCAmelCase					      =  optimizer_cls(lowerCAmelCase__			,						**lowerCAmelCase__    )

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


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: Any    )    ->      Optional[int]:
   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=lowerCAmelCase__    )
   return scheduler


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


 def _UpperCamelCase	(						self		: Tuple			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Union[str, Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  inputs.pop("labels"    )
   UpperCAmelCase      ,	UpperCAmelCase					      =  self._compute_loss(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
   return loss


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: nn.Module			,						lowerCAmelCase__		: Dict[str, Union[torch.Tensor, Any]]			,						lowerCAmelCase__		: bool			,						lowerCAmelCase__		: Optional[List[str]] = None			,						)    ->      Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
   UpperCAmelCase					      =  self._prepare_inputs(lowerCAmelCase__    )

   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"]			,						**lowerCAmelCase__			,						)
     # 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(lowerCAmelCase__			,						gen_kwargs["max_length"]    )

   UpperCAmelCase					      =  inputs.pop("labels"    )
   with torch.no_grad():
     # compute loss on predict data
     UpperCAmelCase      ,	UpperCAmelCase					      =  self._compute_loss(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   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(lowerCAmelCase__			,						gen_kwargs["max_length"]    )

   return (loss, logits, labels)


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: Union[str, Any]    )    ->      Dict:
   # 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






def      _lowerCAmelCase(	__A   ,       __A			):
  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def      _lowerCAmelCase(	__A   ,       __A=0			):
  return sorted(__A   ,       key=lambda __A			: x[column]			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(points_counts - 1			):
    for j in range(i + 1   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(min(6   ,       points_counts - 1			)   ,       __A			):
    for j in range(max(0   ,       i - 6			)   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):

  # base case
  if points_counts <= 3:
    return dis_between_closest_pair(__A   ,       __A			)

  # recursion
  UpperCAmelCase					      =  points_counts // 2
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[:mid]   ,       __A			)
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[mid:]   ,       points_counts - mid			)
  UpperCAmelCase					      =  min(__A   ,       __A			)
  UpperCAmelCase					      =  []
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]			) < closest_pair_dis:
      cross_strip.append(__A			)

  UpperCAmelCase					      =  dis_between_closest_in_strip(
      __A   ,       len(__A			)   ,       __A			)
  return min(__A   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=0			)
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=1			)
  return (
      closest_pair_of_points_sqr(
          __A   ,       __A   ,       __A			)
  ) ** 0.5


if __name__ == "__main__":
      lowerCAmelCase__       = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))



