




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def        lowerCAmelCase_			(  snake_case_  ):
      return np.dot(snake_case_,snake_case_  )







class 		lowercase							:

     def __init__(						self	,  *,
         _a = np.inf	,  _a = "linear"	,  _a = 0.0	,  )		->							None:
           _A							:  Tuple    =		regularization
           _A							:  str    =		gamma
           if kernel == "linear":
                 _A							:  str    =		self.__linear
           elif kernel == "rbf":
                 if self.gamma == 0:
                       raise ValueError("""rbf kernel requires gamma"""       )
                 if not isinstance(self.gamma	,  (float, int)       ):
                       raise ValueError("""gamma must be float or int"""       )
                 if not self.gamma > 0:
                       raise ValueError("""gamma must be > 0"""       )
                 _A							:  List[str]    =		self.__rbf
                 # in the future, there could be a default value like in sklearn
                 # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                 # previously it was 1/(n_features)
           else:
                 _A							:  Optional[Any]    =		F'''Unknown kernel: {kernel}'''
                 raise ValueError(_a       )

     def       a__			(						self	,  _a	,  _a       )		->							float:
           return np.dot(_a	,  _a       )

     def       a__			(						self	,  _a	,  _a       )		->							float:
           return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

     def       a__			(						self	,  _a	,  _a       )		->							None:
           _A							:  int    =		observations
           _A							:  int    =		classes

           # using Wolfe's Dual to calculate w.
           # Primal problem: minimize 1/2*norm_squared(w)
           #   constraint: yn(w . xn + b) >= 1
           #
           # With l a vector
           # Dual problem: maximize sum_n(ln) -
           #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
           #   constraint: self.C >= ln >= 0
           #           and sum_n(ln*yn) = 0
           # Then we get w using w = sum_n(ln*yn*xn)
           # At the end we can get b ~= mean(yn - w . xn)
           #
           # Since we use kernels, we only need l_star to calculate b
           # and to classify observations

           ((_A)    ,     )							:  Optional[int]    =		np.shape(_a       )

           def to_minimize(_a       ) -> float:
                 _A							:  Union[str, Any]    =		0
                 ((_A)    ,     )							:  Optional[Any]    =		np.shape(_a       )
                 for i in range(_a       ):
                       for j in range(_a       ):
                             s += (
                                 candidate[i]
                                 * candidate[j]
                                 * classes[i]
                                 * classes[j]
                                 * self.kernel(observations[i]	,  observations[j]       )
                             )
                 return 1 / 2 * s - sum(_a       )

           _A							:  Any    =		LinearConstraint(_a	,  0	,  0       )
           _A							:  Optional[Any]    =		Bounds(0	,  self.regularization       )

           _A							:  Union[str, Any]    =		minimize(
               _a	,  np.ones(_a       )	,  bounds=_a	,  constraints=[ly_contraint]       ).x
           _A							:  Dict    =		l_star

           # calculating mean offset of separation plane to points
           _A							:  Tuple    =		0
           for i in range(_a       ):
                 for j in range(_a       ):
                       s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                           observations[i]	,  observations[j]       )
           _A							:  List[str]    =		s / n



     def       a__			(						self	,  _a       )		->							int:
           _A							:  Union[str, Any]    =		sum(
               self.optimum[n]
               * self.classes[n]
               * self.kernel(self.observations[n]	,  _a       )
               for n in range(len(self.classes       )       )       )
           return 1 if s + self.offset >= 0 else -1


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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    "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__	):

     def __init__(						self	,  _a=None	,  _a=None	,  *_a	,  **_a       )		->							Optional[int]:
           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__}'''
                 )
                 _A							:  Optional[Any]    =		self.model.config
           else:
                 _A							:  int    =		config

           _A							:  Optional[Any]    =		data_args
           _A							:  int    =		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:
                 _A							:  Optional[Any]    =		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							:  Union[str, Any]    =		label_smoothed_nll_loss

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

           if self.lr_scheduler is None:
                 _A							:  Union[str, Any]    =		self._get_lr_scheduler(_a       )
           else:  # ignoring --lr_scheduler
                 logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""       )

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

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

     def       a__			(						self	,  _a	,  _a       )		->							List[Any]:
           _A							:  Optional[int]    =		inputs.pop("""labels"""       )
           _A    ,     _A							:  Dict    =		self._compute_loss(_a	,  _a	,  _a       )
           return loss

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = None	,  )		->							Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
           _A							:  int    =		self._prepare_inputs(_a       )

           _A							:  Dict    =		{
               """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							:  List[str]    =		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"]:
                       _A							:  str    =		self._pad_tensors_to_max_len(_a	,  gen_kwargs["""max_length"""]       )

           _A							:  Any    =		inputs.pop("""labels"""       )
           with torch.no_grad():
                 # compute loss on predict data
                 _A    ,     _A							:  Tuple    =		self._compute_loss(_a	,  _a	,  _a       )

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

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

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

           return (loss, logits, labels)



     def       a__			(						self	,  _a	,  _a       )		->							Union[str, Any]:
           # If PAD token is not defined at least EOS token has to be defined
           _A							:  Optional[Any]    =		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							:  Dict    =		pad_token_id * torch.ones(
               (tensor.shape[0], max_length)	,  dtype=tensor.dtype	,  device=tensor.device       )
           _A							:  Dict    =		tensor
           return padded_tensor

