
'''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


UpperCAmelCase__ :			Optional[Any]											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Tuple											=		{
    "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  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							str   ,				__magic_name__      :							int=None   ,				__magic_name__      :							Optional[int]=None   ,				*__magic_name__      :							Union[str, Any]   ,				**__magic_name__      :							List[Any]				):


				"""simple docstring"""

				super().__init__(*__magic_name__   ,				**__magic_name__				)

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

				lowerCAmelCase__										=				data_args
				lowerCAmelCase__										=				self.config.tgt_vocab_size if isinstance(self.config   ,				__magic_name__				) 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:
							lowerCAmelCase__										=				torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id				)
				else:
							# dynamically import label_smoothed_nll_loss
							from utils import label_smoothed_nll_loss

							lowerCAmelCase__										=				label_smoothed_nll_loss





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							int				):


				"""simple docstring"""

				if self.optimizer is None:
							lowerCAmelCase__										=				["bias", "LayerNorm.weight"]
							lowerCAmelCase__										=				[
							    {
							        "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,
							    },
							]
							lowerCAmelCase__										=				Adafactor if self.args.adafactor else AdamW
							if self.args.adafactor:
										lowerCAmelCase__										=				Adafactor
										lowerCAmelCase__										=				{"scale_parameter": False, "relative_step": False}
							else:
										lowerCAmelCase__										=				AdamW
										lowerCAmelCase__										=				{
										    "betas": (self.args.adam_betaa, self.args.adam_betaa),
										    "eps": self.args.adam_epsilon,
										}
							lowerCAmelCase__										=				self.args.learning_rate
							if self.sharded_ddp:
										lowerCAmelCase__										=				OSS(
										    params=__magic_name__   ,				optim=__magic_name__   ,				**__magic_name__   ,				)
							else:
										lowerCAmelCase__										=				optimizer_cls(__magic_name__   ,				**__magic_name__				)

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				arg_to_scheduler[self.args.lr_scheduler]
				if self.args.lr_scheduler == "constant":
							lowerCAmelCase__										=				schedule_func(self.optimizer				)
				elif self.args.lr_scheduler == "constant_w_warmup":
							lowerCAmelCase__										=				schedule_func(self.optimizer   ,				num_warmup_steps=self.args.warmup_steps				)
				else:
							lowerCAmelCase__										=				schedule_func(
							    self.optimizer   ,				num_warmup_steps=self.args.warmup_steps   ,				num_training_steps=__magic_name__				)
				return scheduler





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""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 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Any   ,				__magic_name__      :							Any				):


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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				inputs.pop("labels"				)
				lowerCAmelCase__						,lowerCAmelCase__										=				self._compute_loss(__magic_name__   ,				__magic_name__   ,				__magic_name__				)
				return loss





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							nn.Module   ,				__magic_name__      :							Dict[str, Union[torch.Tensor, Any]]   ,				__magic_name__      :							bool   ,				__magic_name__      :							Optional[List[str]] = None   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				self._prepare_inputs(__magic_name__				)

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

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

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

				lowerCAmelCase__										=				generated_tokens if self.args.predict_with_generate else logits

				if labels.shape[-1] < gen_kwargs["max_length"]:
							lowerCAmelCase__										=				self._pad_tensors_to_max_len(__magic_name__   ,				gen_kwargs["max_length"]				)

				return (loss, logits, labels)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							int   ,				__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				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}"""				)

				lowerCAmelCase__										=				pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)   ,				dtype=tensor.dtype   ,				device=tensor.device				)
				lowerCAmelCase__										=				tensor
				return padded_tensor









import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					*_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[Any]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							str	) ->			Dict:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       eval_examples
     snake_case_            =       post_process_function



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Dataset] = None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[List[str]] = None     ,					_lowerCAmelCase							:							str = "eval"     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Dict[str, float]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()
     snake_case_            =       (
         gen_kwargs["max_length"] if gen_kwargs.get("max_length"	) is not None else self.args.generation_max_length
     )
     snake_case_            =       (
         gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"	) is not None else self.args.generation_num_beams
     )
     snake_case_            =       gen_kwargs

     snake_case_            =       self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_            =       self.get_eval_dataloader(_lowerCAmelCase	)
     snake_case_            =       self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Evaluation"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics
     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
        snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()	):
           if not key.startswith(F'''{metric_key_prefix}_'''	):
              snake_case_            =       metrics.pop(_lowerCAmelCase	)

        metrics.update(output.metrics	)
     else:
        snake_case_            =       output.metrics

     if self.args.should_log:
        # Only the main node log the results by default
        self.log(_lowerCAmelCase	)

     if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()	)

     snake_case_            =       self.callback_handler.on_evaluate(self.args     ,					self.state     ,					self.control     ,					_lowerCAmelCase	)
     return metrics



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str = "test"     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()

     snake_case_            =       self.get_test_dataloader(_lowerCAmelCase	)

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Prediction"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics

     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)
     if self.post_process_function is None or self.compute_metrics is None:
        return output

     snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					"predict"	)
     snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()	):
        if not key.startswith(F'''{metric_key_prefix}_'''	):
           snake_case_            =       metrics.pop(_lowerCAmelCase	)
     metrics.update(output.metrics	)
     return PredictionOutput(predictions=predictions.predictions     ,					label_ids=predictions.label_ids     ,					metrics=_lowerCAmelCase	)




