





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       :     Optional[int]   =     logging.get_logger(__name__)

__lowerCamelCase       :     int   =     {
    """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 					A__   (       __snake_case			):





      def __init__( self       ,							A_=None       ,							A_=None       ,							*A_       ,							**A_   ):
       '''simple docstring'''




       super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_   )

       if config is None:
        assert isinstance(self.model       ,							SCREAMING_SNAKE_CASE_   ), (
            "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
            F""" {self.model.__class__}"""
        )
        UpperCamelCase	:					Tuple       	=   self.model.config
       else:
        UpperCamelCase	:					str       	=   config

       UpperCamelCase	:					List[str]       	=   data_args
       UpperCamelCase	:					Any       	=   self.config.tgt_vocab_size if isinstance(self.config       ,							SCREAMING_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:
        UpperCamelCase	:					List[str]       	=   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	:					Optional[int]       	=   label_smoothed_nll_loss





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




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

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
        UpperCamelCase	:					str       	=   schedule_func(self.optimizer   )
       elif self.args.lr_scheduler == "constant_w_warmup":
        UpperCamelCase	:					List[str]       	=   schedule_func(self.optimizer       ,							num_warmup_steps=self.args.warmup_steps   )
       else:
        UpperCamelCase	:					Any       	=   schedule_func(
            self.optimizer       ,							num_warmup_steps=self.args.warmup_steps       ,							num_training_steps=SCREAMING_SNAKE_CASE_   )
       return scheduler





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   inputs.pop("labels"   )
       UpperCamelCase	,    UpperCamelCase	:					int       	=   self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_   )
       return loss





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self._prepare_inputs(SCREAMING_SNAKE_CASE_   )

       UpperCamelCase	:					List[str]       	=   {
           "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	:					Optional[int]       	=   self.model.generate(
            inputs["input_ids"]       ,							attention_mask=inputs["attention_mask"]       ,							**SCREAMING_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"]:
         UpperCamelCase	:					Any       	=   self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_       ,							gen_kwargs["max_length"]   )

       UpperCamelCase	:					List[str]       	=   inputs.pop("labels"   )
       with torch.no_grad():
        # compute loss on predict data
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_   )

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

       UpperCamelCase	:					Optional[Any]       	=   generated_tokens if self.args.predict_with_generate else logits

       if labels.shape[-1] < gen_kwargs["max_length"]:
        UpperCamelCase	:					Tuple       	=   self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_       ,							gen_kwargs["max_length"]   )

       return (loss, logits, labels)





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					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}"""   )

       UpperCamelCase	:					Optional[int]       	=   pad_token_id * torch.ones(
           (tensor.shape[0], max_length)       ,							dtype=tensor.dtype       ,							device=tensor.device   )
       UpperCamelCase	:					List[Any]       	=   tensor
       return padded_tensor


from __future__ import annotations

from collections.abc import MutableSequence
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			MutableSequence[float]       )       ->     None:
					if len(SCREAMING_SNAKE_CASE_       ) != degree + 1:
									raise ValueError(
									    '''The number of coefficients should be equal to the degree + 1.'''       )

					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					degree
	def __add__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:

					if self.degree > polynomial_a.degree:
									lowercase_       =					self.coefficients[:]
									for i in range(polynomial_a.degree + 1       ):
													coefficients[i] += polynomial_a.coefficients[i]
									return Polynomial(self.degree       ,							SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					polynomial_a.coefficients[:]
									for i in range(self.degree + 1       ):
													coefficients[i] += self.coefficients[i]
									return Polynomial(polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def __sub__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					return self + polynomial_a * Polynomial(0       ,							[-1]       )
	def __neg__(       self  :			int       )       ->     Polynomial:
					return Polynomial(self.degree       ,							[-c for c in self.coefficients]       )
	def __mul__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + polynomial_a.degree + 1)
					for i in range(self.degree + 1       ):
									for j in range(polynomial_a.degree + 1       ):
													coefficients[i + j] += (
													    self.coefficients[i] * polynomial_a.coefficients[j]
													)

					return Polynomial(self.degree + polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int | float       )       ->     int | float:
					lowercase_       =					0
					for i in range(self.degree + 1       ):
									result += self.coefficients[i] * (substitution**i)
					return result
	def __str__(       self  :			Tuple       )       ->     str:
					lowercase_       =					''''''
					for i in range(self.degree       ,							-1       ,							-1       ):
									if self.coefficients[i] == 0:
													continue
									elif self.coefficients[i] > 0:
													if polynomial:
																	polynomial += " + "
									else:
													polynomial += " - "

									if i == 0:
													polynomial += str(abs(self.coefficients[i]       )       )
									elif i == 1:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x"
									else:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x^" + str(SCREAMING_SNAKE_CASE_       )

					return polynomial
	def __repr__(       self  :			Optional[Any]       )       ->     str:
					return self.__str__()
	def 						_lowercase  (       self  :			int       )       ->     Polynomial:
					lowercase_       =					[0] * self.degree
					for i in range(self.degree       ):
									lowercase_       =					self.coefficients[i + 1] * (i + 1)
					return Polynomial(self.degree - 1       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int | float = 0       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + 2)
					lowercase_       =					constant
					for i in range(self.degree + 1       ):
									lowercase_       =					self.coefficients[i] / (i + 1)
					return Polynomial(self.degree + 1       ,							SCREAMING_SNAKE_CASE_       )
	def __eq__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									return False

					if self.degree != polynomial_a.degree:
									return False

					for i in range(self.degree + 1       ):
									if self.coefficients[i] != polynomial_a.coefficients[i]:
													return False

					return True




	def __ne__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					return not self.__eq__(SCREAMING_SNAKE_CASE_       )




