
"""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,
)


lowerCamelCase__           =	logging.getLogger(__name__)




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




@dataclass
class 				A__ :
		A_		: str            =			field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		A_		: Optional[str]            =			field(
		    default='summarization' ,  metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} ,  )
		A_		: Optional[int]            =			field(
		    default=1_0_2_4 ,  metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    } ,  )
		A_		: Optional[int]            =			field(
		    default=1_2_8 ,  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_		: Optional[int]            =			field(
		    default=1_4_2 ,  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_		: Optional[int]            =			field(
		    default=1_4_2 ,  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_		: Optional[int]            =			field(default=-1 ,  metadata={'help': '# training examples. -1 means use all.'})
		A_		: Optional[int]            =			field(default=-1 ,  metadata={'help': '# validation examples. -1 means use all.'})
		A_		: Optional[int]            =			field(default=-1 ,  metadata={'help': '# test examples. -1 means use all.'})
		A_		: Optional[str]            =			field(default=_lowerCamelCase ,  metadata={'help': 'Source language id for translation.'})
		A_		: Optional[str]            =			field(default=_lowerCamelCase ,  metadata={'help': 'Target language id for translation.'})
		A_		: Optional[int]            =			field(default=_lowerCamelCase ,  metadata={'help': '# num_beams to use for evaluation.'})
		A_		: bool            =			field(
		    default=_lowerCamelCase ,  metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} ,  )



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	logger.info(F"***** {split} metrics *****"	)
	for key in sorted(metrics.keys()	):
		logger.info(F"  {key} = {metrics[key]}"	)
	save_json(_UpperCamelCase ,						os.path.join(_UpperCamelCase ,						F"{split}_results.json"	)	)
def 					__lowerCAmelCase       ():
	# 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.

	__lowerCAmelCase    :		Union[str, Any]		    =   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.
		__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
	else:
		__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   parser.parse_args_into_dataclasses()

	check_output_dir(_UpperCamelCase	)

	# 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' ,						_UpperCamelCase	)

	# 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.

	__lowerCAmelCase    :		Optional[int]		    =   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 ,						)

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

	__lowerCAmelCase    :		Optional[Any]		    =   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 ,						)
	__lowerCAmelCase    :		Optional[int]		    =   AutoModelForSeqaSeqLM.from_pretrained(
	    model_args.model_name_or_path ,						from_tf='.ckpt' in model_args.model_name_or_path ,						config=_UpperCamelCase ,						cache_dir=model_args.cache_dir ,						)

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

	# set num_beams for evaluation
	if data_args.eval_beams is None:
		__lowerCAmelCase    :		Optional[Any]		    =   model.config.num_beams

	# set decoder_start_token_id for MBart
	if model.config.decoder_start_token_id is None and isinstance(_UpperCamelCase ,						(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(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		str		    =   tokenizer.lang_code_to_id[data_args.tgt_lang]
		else:
			__lowerCAmelCase    :		Union[str, Any]		    =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

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

	__lowerCAmelCase    :		Dict		    =   SeqaSeqDataset

	# Get datasets
	__lowerCAmelCase    :		Tuple		    =   (
	    dataset_class(
	        _UpperCamelCase ,						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
	)
	__lowerCAmelCase    :		Any		    =   (
	    dataset_class(
	        _UpperCamelCase ,						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
	)
	__lowerCAmelCase    :		int		    =   (
	    dataset_class(
	        _UpperCamelCase ,						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
	__lowerCAmelCase    :		str		    =   (
	    build_compute_metrics_fn(data_args.task ,						_UpperCamelCase	) if training_args.predict_with_generate else None
	)
	__lowerCAmelCase    :		List[str]		    =   SeqaSeqTrainer(
	    model=_UpperCamelCase ,						args=_UpperCamelCase ,						data_args=_UpperCamelCase ,						train_dataset=_UpperCamelCase ,						eval_dataset=_UpperCamelCase ,						data_collator=SeqaSeqDataCollator(
	        _UpperCamelCase ,						_UpperCamelCase ,						model.config.decoder_start_token_id ,						training_args.tpu_num_cores	) ,						compute_metrics=_UpperCamelCase ,						tokenizer=_UpperCamelCase ,						)

	__lowerCAmelCase    :		Union[str, Any]		    =   {}
	# Training
	if training_args.do_train:
		logger.info('*** Train ***'	)

		__lowerCAmelCase    :		int		    =   trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
		__lowerCAmelCase    :		Dict		    =   train_result.metrics
		__lowerCAmelCase    :		Optional[int]		    =   data_args.n_train

		trainer.save_model()  # this also saves the tokenizer

		if trainer.is_world_process_zero():
			handle_metrics('train' ,						_UpperCamelCase ,						training_args.output_dir	)
			all_metrics.update(_UpperCamelCase	)

			# 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 ***'	)

		__lowerCAmelCase    :		Optional[Any]		    =   trainer.evaluate(metric_key_prefix='val'	)
		__lowerCAmelCase    :		List[str]		    =   data_args.n_val
		__lowerCAmelCase    :		List[Any]		    =   round(metrics['val_loss'] ,						4	)

		if trainer.is_world_process_zero():
			handle_metrics('val' ,						_UpperCamelCase ,						training_args.output_dir	)
			all_metrics.update(_UpperCamelCase	)

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

		__lowerCAmelCase    :		Union[str, Any]		    =   trainer.predict(test_dataset=_UpperCamelCase ,						metric_key_prefix='test'	)
		__lowerCAmelCase    :		int		    =   test_output.metrics
		__lowerCAmelCase    :		Tuple		    =   data_args.n_test

		if trainer.is_world_process_zero():
			__lowerCAmelCase    :		List[str]		    =   round(metrics['test_loss'] ,						4	)
			handle_metrics('test' ,						_UpperCamelCase ,						training_args.output_dir	)
			all_metrics.update(_UpperCamelCase	)

			if training_args.predict_with_generate:
				__lowerCAmelCase    :		Tuple		    =   tokenizer.batch_decode(
				    test_output.predictions ,						skip_special_tokens=_UpperCamelCase ,						clean_up_tokenization_spaces=_UpperCamelCase	)
				__lowerCAmelCase    :		Dict		    =   lmap(str.strip ,						_UpperCamelCase	)
				write_txt_file(_UpperCamelCase ,						os.path.join(training_args.output_dir ,						'test_generations.txt'	)	)

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

	return all_metrics
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()

"""simple docstring"""







from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip