



"""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


__a      =				logging.get_logger(__name__)

__a      =				{
    "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 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							List[str]						,     snake_case:							int=None						,     snake_case:							str=None						,     *snake_case:							Any						,     **snake_case:							List[Any]						)   ->	Optional[int]:
											super().__init__(*snake_case						,     **snake_case						)

											if config is None:
																	assert isinstance(self.model						,     snake_case						), (
																	    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																	    f""" {self.model.__class__}"""
																	)
																	snake_case_ :Optional[Any]						=			self.model.config
											else:
																	snake_case_ :int						=			config

											snake_case_ :List[Any]						=			data_args
											snake_case_ :str						=			self.config.tgt_vocab_size if isinstance(self.config						,     snake_case						) 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:
																	snake_case_ :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

																	snake_case_ :Tuple						=			label_smoothed_nll_loss






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

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






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Optional[Any]						)   ->	str:
											snake_case_ :int						=			arg_to_scheduler[self.args.lr_scheduler]
											if self.args.lr_scheduler == "constant":
																	snake_case_ :List[str]						=			schedule_func(self.optimizer						)
											elif self.args.lr_scheduler == "constant_w_warmup":
																	snake_case_ :Any						=			schedule_func(self.optimizer						,     num_warmup_steps=self.args.warmup_steps						)
											else:
																	snake_case_ :int						=			schedule_func(
																	    self.optimizer						,     num_warmup_steps=self.args.warmup_steps						,     num_training_steps=snake_case						)
											return scheduler






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






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							List[Any]						,     snake_case:							List[Any]						)   ->	List[Any]:
											snake_case_ :int						=			inputs.pop("""labels"""						)
											snake_case_,       snake_case_ :Any						=			self._compute_loss(snake_case						,     snake_case						,     snake_case						)
											return loss






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							nn.Module						,     snake_case:							Dict[str, Union[torch.Tensor, Any]]						,     snake_case:							bool						,     snake_case:							Optional[List[str]] = None						,     )   ->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
											snake_case_ :Optional[int]						=			self._prepare_inputs(snake_case						)

											snake_case_ :Optional[int]						=			{
											    """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:
																	snake_case_ :Union[str, Any]						=			self.model.generate(
																	    inputs["""input_ids"""]						,     attention_mask=inputs["""attention_mask"""]						,     **snake_case						,     )
																	# in case the batch is shorter than max length, the output should be padded
																	if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																							snake_case_ :Optional[int]						=			self._pad_tensors_to_max_len(snake_case						,     gen_kwargs["""max_length"""]						)

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

											snake_case_ :Optional[int]						=			loss.mean().detach()
											if self.args.prediction_loss_only:
																	return (loss, None, None)

											snake_case_ :Optional[int]						=			generated_tokens if self.args.predict_with_generate else logits

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

											return (loss, logits, labels)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							List[Any]						,     snake_case:							Optional[int]						)   ->	int:
											# If PAD token is not defined at least EOS token has to be defined
											snake_case_ :List[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}"""						)

											snake_case_ :List[str]						=			pad_token_id * torch.ones(
											    (tensor.shape[0], max_length)						,     dtype=tensor.dtype						,     device=tensor.device						)
											snake_case_ :str						=			tensor
											return padded_tensor


from collections.abc import Sequence






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			return sum(c * (x**i) for i, c in enumerate(__lowercase ) )






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			_snake_case		=			0.0
			for coeff in reversed(__lowercase ):
						_snake_case		=			result * x + coeff
			return result


if __name__ == "__main__":
			_lowerCamelCase       :				Optional[Any]       =			(0.0, 0.0, 5.0, 9.3, 7.0)
			_lowerCamelCase       :				Optional[int]       =			1_0.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))