



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  __snake_case      (   __UpperCAmelCase):







							'''simple docstring'''






							def __init__(							self  ,  a_=None  ,  a_=None  ,  *a_  ,  **a_		):
											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__				      =		self.model.config
											else:
															a__				      =		config

											a__				      =		data_args
											a__				      =		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__				      =		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 _a      (							self  ,  a_		):

											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=a_  ,  optim=a_  ,  **a_  ,  )
															else:
																			a__				      =		optimizer_cls(a_  ,  **a_		)

											if self.lr_scheduler is None:
															a__				      =		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_		):
											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=a_		)
											return scheduler


							def _a      (							self		):

											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_		):

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


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		inputs.pop("""labels"""		)
											a__      ,			a__				      =		self._compute_loss(a_  ,  a_  ,  a_		)
											return loss


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_ = None  ,  ):
											a__				      =		self._prepare_inputs(a_		)

											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"""]  ,  **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__				      =		self._pad_tensors_to_max_len(a_  ,  gen_kwargs["""max_length"""]		)

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

											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(a_  ,  gen_kwargs["""max_length"""]		)

											return (loss, logits, labels)







							def _a      (							self  ,  a_  ,  a_		):
											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

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

