



'''simple docstring'''


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
						from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
						lowercase_      :				List[Any]   					=    True
						from torch.cuda.amp import autocast


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,   )
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Whether to log verbose messages or not.'''}			,   )
	__A			  =					field(
	    default=2.0			,   metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}			)
	__A			  =					field(
	    default=0.5			,   metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}			)
	__A			  =					field(
	    default=0.99_99_95			,   metadata={'''help''': '''Decay of gumbel temperature during training.'''}			)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      ModelArguments ,  lowercase_    :      TrainingArguments						):
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,  datefmt="""%m/%d/%Y %H:%M:%S""" ,  handlers=[logging.StreamHandler(sys.stdout						)] ,  )
						lowercase        =							logging.WARNING
						if model_args.verbose_logging:
												lowercase        =							logging.DEBUG
						elif trainer_utils.is_main_process(training_args.local_rank						):
												lowercase        =							logging.INFO
						logger.setLevel(lowercase_						)




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
	__A			  =					field(
	    default='''train'''			,   metadata={
	        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
	    }			,   )
	__A			  =					field(
	    default='''validation'''			,   metadata={
	        '''help''': (
	            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
	        )
	    }			,   )
	__A			  =					field(
	    default='''file'''			,   metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}			,   )
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}			)
	__A			  =					field(
	    default=1			,   metadata={
	        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
	    }			,   )
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}			,   )
	__A			  =					field(
	    default=20.0			,   metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}			)




@dataclass
class      __UpperCamelCase       :
	__A			  =					42
	__A			  =					42
	__A			  =					"longest"
	__A			  =					None
	__A			  =					None







	def __call__(    self ,				_lowerCAmelCase						)    ->    Dict[str, torch.Tensor]:
							'''simple docstring'''






							lowercase        =							self.feature_extractor.pad(
							    _lowerCAmelCase ,				max_length=self.max_length ,				padding=self.padding ,				pad_to_multiple_of=self.pad_to_multiple_of ,				return_tensors="""pt""" ,				)
							lowercase        =							self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]						)

							lowercase        =							batch["""input_values"""].shape[0]

							# make sure that no loss is computed on padded inputs
							if batch["attention_mask"] is not None:
													# compute real output lengths according to convolution formula
													lowercase        =							self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1						)						).to(
													    torch.long						)

													lowercase        =							torch.zeros(
													    (batch_size, mask_indices_seq_length) ,				dtype=torch.long ,				device=batch["""input_values"""].device						)

													# these two operations makes sure that all values
													# before the output lengths indices are attended to
													lowercase        =							1
													lowercase        =							attention_mask.flip([-1]						).cumsum(-1						).flip([-1]						).bool()

							# sample randomly masked indices
							lowercase        =							_compute_mask_indices(
							    (batch_size, mask_indices_seq_length) ,				self.model.config.mask_time_prob ,				self.model.config.mask_time_length ,				attention_mask=_lowerCAmelCase ,				min_masks=2 ,				)

							return batch




class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				*_lowerCAmelCase ,				_lowerCAmelCase=1 ,				_lowerCAmelCase=0 ,				_lowerCAmelCase=1.0 ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							super().__init__(*_lowerCAmelCase ,				**_lowerCAmelCase						)
							lowercase        =							0
							lowercase        =							max_gumbel_temp
							lowercase        =							min_gumbel_temp
							lowercase        =							gumbel_temp_decay







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    torch.Tensor:
							'''simple docstring'''






							model.train()
							lowercase        =							self._prepare_inputs(_lowerCAmelCase						)

							if self.use_amp:
													with autocast():
																			lowercase        =							self.compute_loss(_lowerCAmelCase ,				_lowerCAmelCase						)
							else:
													lowercase        =							self.compute_loss(_lowerCAmelCase ,				_lowerCAmelCase						)

							if self.args.n_gpu > 1 or self.deepspeed:
													if model.module.config.ctc_loss_reduction == "mean":
																			lowercase        =							loss.mean()
													elif model.module.config.ctc_loss_reduction == "sum":
																			lowercase        =							loss.sum() / (inputs["""mask_time_indices"""]).sum()
													else:
																			raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""						)

							if self.args.gradient_accumulation_steps > 1:
													lowercase        =							loss / self.args.gradient_accumulation_steps

							if self.use_amp:
													self.scaler.scale(_lowerCAmelCase						).backward()
							elif self.use_apex:
													with amp.scale_loss(_lowerCAmelCase ,				self.optimizer						) as scaled_loss:
																			scaled_loss.backward()
							elif self.deepspeed:
													self.deepspeed.backward(_lowerCAmelCase						)
							else:
													loss.backward()

							self.num_update_step += 1
							# make sure gumbel softmax temperature is decayed
							if self.args.n_gpu > 1 or self.deepspeed:
													model.module.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,				self.min_gumbel_temp						)						)
							else:
													model.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,				self.min_gumbel_temp						)						)

							return loss.detach()







def      SCREAMING_SNAKE_CASE				(				):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowercase        =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

						lowercase     ,   lowercase     ,   lowercase        =							parser.parse_args_into_dataclasses()
						configure_logger(lowercase_ ,  lowercase_						)

						# Downloading and loading a dataset from the hub.
						lowercase        =							load_dataset(data_args.dataset_name ,  data_args.dataset_config_name ,  cache_dir=model_args.cache_dir						)

						if "validation" not in datasets.keys():
												# make sure only "validation" and "train" keys remain"
												lowercase        =							DatasetDict()
												lowercase        =							load_dataset(
												    data_args.dataset_name ,  data_args.dataset_config_name ,  split=F"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]""" ,  cache_dir=model_args.cache_dir ,  )
												lowercase        =							load_dataset(
												    data_args.dataset_name ,  data_args.dataset_config_name ,  split=F"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]""" ,  cache_dir=model_args.cache_dir ,  )
						else:
												# make sure only "validation" and "train" keys remain"
												lowercase        =							DatasetDict()
												lowercase        =							load_dataset(
												    data_args.dataset_name ,  data_args.dataset_config_name ,  split="""validation""" ,  cache_dir=model_args.cache_dir ,  )
												lowercase        =							load_dataset(
												    data_args.dataset_name ,  data_args.dataset_config_name ,  split=F"""{data_args.train_split_name}""" ,  cache_dir=model_args.cache_dir ,  )

						# only normalized-inputs-training is supported
						lowercase        =							WavaVecaFeatureExtractor.from_pretrained(
						    model_args.model_name_or_path ,  cache_dir=model_args.cache_dir ,  do_normalize=lowercase_						)

						def prepare_dataset(lowercase_    :      List[Any]						):
												# check that all files have the correct sampling rate
												lowercase     ,   lowercase        =							librosa.load(batch[data_args.speech_file_column] ,  sr=feature_extractor.sampling_rate						)
												return batch

						# load audio files into numpy arrays
						lowercase        =							datasets.map(
						    lowercase_ ,  num_proc=data_args.preprocessing_num_workers ,  remove_columns=datasets["""train"""].column_names						)

						# filter audio files that are too long
						lowercase        =							vectorized_datasets.filter(
						    lambda lowercase_						: len(data["""speech"""]						) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate						)						)

						def normalize(lowercase_    :      str						):
												return feature_extractor(batch["""speech"""] ,  sampling_rate=feature_extractor.sampling_rate						)

						# normalize and transform to `BatchFeatures`
						lowercase        =							vectorized_datasets.map(
						    lowercase_ ,  batched=lowercase_ ,  num_proc=data_args.preprocessing_num_workers ,  load_from_cache_file=not data_args.overwrite_cache ,  remove_columns=vectorized_datasets["""train"""].column_names ,  )

						# pretraining is only supported for "newer" stable layer norm architecture
						# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
						lowercase        =							WavaVecaConfig.from_pretrained(
						    model_args.model_name_or_path ,  cache_dir=model_args.cache_dir ,  gradient_checkpointing=training_args.gradient_checkpointing ,  )

						if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
												raise ValueError(
												    """PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"""
												    """ ``config.feat_extract_norm='layer'"""						)

						lowercase        =							WavaVecaForPreTraining(lowercase_						)

						lowercase        =							DataCollatorForWavaVecaPretraining(model=lowercase_ ,  feature_extractor=lowercase_						)

						lowercase        =							WavaVecaPreTrainer(
						    model=lowercase_ ,  data_collator=lowercase_ ,  args=lowercase_ ,  train_dataset=vectorized_datasets["""train"""] ,  eval_dataset=vectorized_datasets["""validation"""] ,  tokenizer=lowercase_ ,  max_gumbel_temp=model_args.max_gumbel_temperature ,  min_gumbel_temp=model_args.min_gumbel_temperature ,  gumbel_temp_decay=model_args.gumbel_temperature_decay ,  )
						trainer.train()


if __name__ == "__main__":
						main()







'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,   )
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Whether tp freeze the encoder.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Whether  to freeze the embeddings.'''}			)




@dataclass
class      __UpperCamelCase       :
	__A			  =					field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
	__A			  =					field(
	    default='''summarization'''			,   metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}			,   )
	__A			  =					field(
	    default=1024			,   metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }			,   )
	__A			  =					field(
	    default=128			,   metadata={
	        '''help''': (
	            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }			,   )
	__A			  =					field(
	    default=142			,   metadata={
	        '''help''': (
	            '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded. '''
	            '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
	            '''during ``evaluate`` and ``predict``.'''
	        )
	    }			,   )
	__A			  =					field(
	    default=142			,   metadata={
	        '''help''': (
	            '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }			,   )
	__A			  =					field(default=-1			,   metadata={'''help''': '''# training examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# validation examples. -1 means use all.'''}			)
	__A			  =					field(default=-1			,   metadata={'''help''': '''# test examples. -1 means use all.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Source language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Target language id for translation.'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''# num_beams to use for evaluation.'''}			)
	__A			  =					field(
	    default=_UpperCAmelCase			,   metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}			,   )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      int ,  lowercase_    :      List[Any]						):
						logger.info(F"""***** {split} metrics *****"""						)
						for key in sorted(metrics.keys()						):
												logger.info(F"""  {key} = {metrics[key]}"""						)
						save_json(lowercase_ ,  os.path.join(lowercase_ ,  F"""{split}_results.json"""						)						)







def      SCREAMING_SNAKE_CASE				(				):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowercase        =							HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)						)

						if len(sys.argv						) == 2 and sys.argv[1].endswith(""".json"""						):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowercase     ,   lowercase     ,   lowercase        =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												lowercase     ,   lowercase     ,   lowercase        =							parser.parse_args_into_dataclasses()

						check_output_dir(lowercase_						)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,  datefmt="""%m/%d/%Y %H:%M:%S""" ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,  )
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" ,  training_args.local_rank ,  training_args.device ,  training_args.n_gpu ,  bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED						) ,  training_args.fpaa ,  )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank						):
												transformers.utils.logging.set_verbosity_info()
						logger.info("""Training/evaluation parameters %s""" ,  lowercase_						)

						# Set seed
						set_seed(training_args.seed						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase        =							AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,  cache_dir=model_args.cache_dir ,  )

						lowercase        =							("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
												if getattr(lowercase_ ,  lowercase_ ,  lowercase_						):
																		assert hasattr(lowercase_ ,  lowercase_						), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																		setattr(lowercase_ ,  lowercase_ ,  getattr(lowercase_ ,  lowercase_						)						)

						lowercase        =							AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,  cache_dir=model_args.cache_dir ,  )
						lowercase        =							AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path ,  from_tf=""".ckpt""" in model_args.model_name_or_path ,  config=lowercase_ ,  cache_dir=model_args.cache_dir ,  )

						# use task specific params
						use_task_specific_params(lowercase_ ,  data_args.task						)

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												lowercase        =							model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(lowercase_ ,  (MBartTokenizer, MBartTokenizerFast)						):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(lowercase_ ,  lowercase_						):
																		lowercase        =							tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowercase        =							tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

						if model_args.freeze_embeds:
												freeze_embeds(lowercase_						)
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()						)
												assert_all_frozen(model.get_encoder()						)

						lowercase        =							SeqaSeqDataset

						# Get datasets
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  type_path="""train""" ,  data_dir=data_args.data_dir ,  n_obs=data_args.n_train ,  max_target_length=data_args.max_target_length ,  max_source_length=data_args.max_source_length ,  prefix=model.config.prefix or """""" ,  )
						    if training_args.do_train
						    else None
						)
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  type_path="""val""" ,  data_dir=data_args.data_dir ,  n_obs=data_args.n_val ,  max_target_length=data_args.val_max_target_length ,  max_source_length=data_args.max_source_length ,  prefix=model.config.prefix or """""" ,  )
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						lowercase        =							(
						    dataset_class(
						        lowercase_ ,  type_path="""test""" ,  data_dir=data_args.data_dir ,  n_obs=data_args.n_test ,  max_target_length=data_args.test_max_target_length ,  max_source_length=data_args.max_source_length ,  prefix=model.config.prefix or """""" ,  )
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						lowercase        =							(
						    build_compute_metrics_fn(data_args.task ,  lowercase_						) if training_args.predict_with_generate else None
						)
						lowercase        =							SeqaSeqTrainer(
						    model=lowercase_ ,  args=lowercase_ ,  data_args=lowercase_ ,  train_dataset=lowercase_ ,  eval_dataset=lowercase_ ,  data_collator=SeqaSeqDataCollator(
						        lowercase_ ,  lowercase_ ,  model.config.decoder_start_token_id ,  training_args.tpu_num_cores						) ,  compute_metrics=lowercase_ ,  tokenizer=lowercase_ ,  )

						lowercase        =							{}
						# Training
						if training_args.do_train:
												logger.info("""*** Train ***"""						)

												lowercase        =							trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												lowercase        =							train_result.metrics
												lowercase        =							data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("""train""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

																		# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir ,  """trainer_state.json"""						)						)

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""						)

												lowercase        =							trainer.evaluate(metric_key_prefix="""val"""						)
												lowercase        =							data_args.n_val
												lowercase        =							round(metrics["""val_loss"""] ,  4						)

												if trainer.is_world_process_zero():
																		handle_metrics("""val""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

						if training_args.do_predict:
												logger.info("""*** Predict ***"""						)

												lowercase        =							trainer.predict(test_dataset=lowercase_ ,  metric_key_prefix="""test"""						)
												lowercase        =							test_output.metrics
												lowercase        =							data_args.n_test

												if trainer.is_world_process_zero():
																		lowercase        =							round(metrics["""test_loss"""] ,  4						)
																		handle_metrics("""test""" ,  lowercase_ ,  training_args.output_dir						)
																		all_metrics.update(lowercase_						)

																		if training_args.predict_with_generate:
																								lowercase        =							tokenizer.batch_decode(
																								    test_output.predictions ,  skip_special_tokens=lowercase_ ,  clean_up_tokenization_spaces=lowercase_						)
																								lowercase        =							lmap(str.strip ,  lowercase_						)
																								write_txt_file(lowercase_ ,  os.path.join(training_args.output_dir ,  """test_generations.txt"""						)						)

						if trainer.is_world_process_zero():
												save_json(lowercase_ ,  os.path.join(training_args.output_dir ,  """all_results.json"""						)						)

						return all_metrics







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


