
"""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    _UpperCAmelCase(   lowerCamelCase     ):






		"""simple docstring"""




		def __init__( self    ,		__a=None    ,		__a=None    ,		*__a    ,		**__a)	->	List[str]:






							'''simple docstring'''
							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__}'''
												)
												_UpperCamelCase										=  self.model.config
							else:
												_UpperCamelCase										=  config

							_UpperCamelCase										=  data_args
							_UpperCamelCase										=  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:
												_UpperCamelCase										=  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										=  label_smoothed_nll_loss



		def 						UpperCAmelCase							( self    ,		__a)	->	Dict:






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

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



		def 						UpperCAmelCase							( self    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  arg_to_scheduler[self.args.lr_scheduler]
							if self.args.lr_scheduler == "constant":
												_UpperCamelCase										=  schedule_func(self.optimizer)
							elif self.args.lr_scheduler == "constant_w_warmup":
												_UpperCamelCase										=  schedule_func(self.optimizer    ,		num_warmup_steps=self.args.warmup_steps)
							else:
												_UpperCamelCase										=  schedule_func(
												    self.optimizer    ,		num_warmup_steps=self.args.warmup_steps    ,		num_training_steps=__a)
							return scheduler



		def 						UpperCAmelCase							( self)	->	Optional[torch.utils.data.Sampler]:






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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  inputs.pop('''labels''')
							_UpperCamelCase			,  _UpperCamelCase										=  self._compute_loss(__a    ,		__a    ,		__a)
							return loss



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		)	->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:






							'''simple docstring'''
							_UpperCamelCase										=  self._prepare_inputs(__a)

							_UpperCamelCase										=  {
							    '''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										=  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"]:
																	_UpperCamelCase										=  self._pad_tensors_to_max_len(__a    ,		gen_kwargs['''max_length'''])

							_UpperCamelCase										=  inputs.pop('''labels''')
							with torch.no_grad():
												# compute loss on predict data
												_UpperCamelCase			,  _UpperCamelCase										=  self._compute_loss(__a    ,		__a    ,		__a)

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

							_UpperCamelCase										=  generated_tokens if self.args.predict_with_generate else logits

							if labels.shape[-1] < gen_kwargs["max_length"]:
												_UpperCamelCase										=  self._pad_tensors_to_max_len(__a    ,		gen_kwargs['''max_length'''])

							return (loss, logits, labels)




		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  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										=  pad_token_id * torch.ones(
							    (tensor.shape[0], max_length)    ,		dtype=tensor.dtype    ,		device=tensor.device)
							_UpperCamelCase										=  tensor
							return padded_tensor








"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a														=		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''     )     ),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']     )     ),
					                    '''langs''': Sequence(Value('''string'''     )     ),
					                    '''spans''': Sequence(Value('''string'''     )     ),
					                }     ),					splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes,					expected[split].num_bytes     )
										else:
															result == expected






