





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,
)


_a						=   logging.getLogger(__name__)






@dataclass
class       __A					:

			'''simple docstring'''







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






@dataclass
class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
			lowerCAmelCase_							  =		field(
			    default="""summarization"""    ,       metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}    ,       )
			lowerCAmelCase_							  =		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."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		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."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		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``."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		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."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		field(default=-1    ,       metadata={"""help""": """# training examples. -1 means use all."""}				)
			lowerCAmelCase_							  =		field(default=-1    ,       metadata={"""help""": """# validation examples. -1 means use all."""}				)
			lowerCAmelCase_							  =		field(default=-1    ,       metadata={"""help""": """# test examples. -1 means use all."""}				)
			lowerCAmelCase_							  =		field(default=lowerCAmelCase    ,       metadata={"""help""": """Source language id for translation."""}				)
			lowerCAmelCase_							  =		field(default=lowerCAmelCase    ,       metadata={"""help""": """Target language id for translation."""}				)
			lowerCAmelCase_							  =		field(default=lowerCAmelCase    ,       metadata={"""help""": """# num_beams to use for evaluation."""}				)
			lowerCAmelCase_							  =		field(
			    default=lowerCAmelCase    ,       metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}    ,       )





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







	'''simple docstring'''







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





def lowerCAmelCase__()       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	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__              =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
	else:
		lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__              =	parser.parse_args_into_dataclasses()

	check_output_dir(__lowerCAmelCase							)

	# 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''' ,__lowerCAmelCase							)

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

	lowerCamelCase__              =	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__              =	AutoModelForSeqaSeqLM.from_pretrained(
	    model_args.model_name_or_path ,from_tf='''.ckpt''' in model_args.model_name_or_path ,config=__lowerCAmelCase ,cache_dir=model_args.cache_dir ,)

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

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

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

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

	lowerCamelCase__              =	SeqaSeqDataset

	# Get datasets
	lowerCamelCase__              =	(
	    dataset_class(
	        __lowerCAmelCase ,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__              =	(
	    dataset_class(
	        __lowerCAmelCase ,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__              =	(
	    dataset_class(
	        __lowerCAmelCase ,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__              =	(
	    build_compute_metrics_fn(data_args.task ,__lowerCAmelCase							) if training_args.predict_with_generate else None
	)
	lowerCamelCase__              =	SeqaSeqTrainer(
	    model=__lowerCAmelCase ,args=__lowerCAmelCase ,data_args=__lowerCAmelCase ,train_dataset=__lowerCAmelCase ,eval_dataset=__lowerCAmelCase ,data_collator=SeqaSeqDataCollator(
	        __lowerCAmelCase ,__lowerCAmelCase ,model.config.decoder_start_token_id ,training_args.tpu_num_cores							) ,compute_metrics=__lowerCAmelCase ,tokenizer=__lowerCAmelCase ,)

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

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

		trainer.save_model()  # this also saves the tokenizer

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

			# 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__              =	trainer.evaluate(metric_key_prefix='''val'''							)
		lowerCamelCase__              =	data_args.n_val
		lowerCamelCase__              =	round(metrics['''val_loss'''] ,4							)

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

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

		lowerCamelCase__              =	trainer.predict(test_dataset=__lowerCAmelCase ,metric_key_prefix='''test'''							)
		lowerCamelCase__              =	test_output.metrics
		lowerCamelCase__              =	data_args.n_test

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

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

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

	return all_metrics





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	main()


if __name__ == "__main__":
							main()










import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def lowerCAmelCase__(__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	def wrapper(*__snake_case ,**__snake_case							):
		lowerCamelCase__              =	timeit.default_timer()
		lowerCamelCase__              =	func(*__snake_case ,**__snake_case							)
		lowerCamelCase__              =	timeit.default_timer() - starttime
		return delta

	lowerCamelCase__              =	func.__name__

	return wrapper





def lowerCAmelCase__(__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	[]
	lowerCamelCase__              =	seq_shapes or {}
	for i in range(__snake_case							):
		lowerCamelCase__              =	{}
		for col_id, (k, v) in enumerate(features.items()							):
			if isinstance(__snake_case ,_ArrayXD							):
				lowerCamelCase__              =	np.random.rand(*v.shape							).astype(v.dtype							)
			elif isinstance(__snake_case ,datasets.Value							):
				if v.dtype == "string":
					lowerCamelCase__              =	'''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCamelCase__              =	np.random.randint(10 ,size=1							).astype(v.dtype							).item()
			elif isinstance(__snake_case ,datasets.Sequence							):
				while isinstance(__snake_case ,datasets.Sequence							):
					lowerCamelCase__              =	v.feature
				lowerCamelCase__              =	seq_shapes[k]
				lowerCamelCase__              =	np.random.rand(*__snake_case							).astype(v.dtype							)
			lowerCamelCase__              =	data

		dummy_data.append((i, example)							)

	return dummy_data





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   str:







	'''simple docstring'''







	lowerCamelCase__              =	generate_examples(__snake_case ,num_examples=__snake_case ,seq_shapes=__snake_case							)

	with ArrowWriter(features=__snake_case ,path=__snake_case							) as writer:
		for key, record in dummy_data:
			lowerCamelCase__              =	features.encode_example(__snake_case							)
			writer.write(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'							)

	lowerCamelCase__              =	datasets.Dataset.from_file(filename=__snake_case ,info=datasets.DatasetInfo(features=__snake_case							)							)

	return dataset



