
"""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,
)


UpperCamelCase_       :    Optional[Any]						      =			logging.getLogger(__name__)



@dataclass
class __lowerCAmelCase					:




			"""simple docstring"""







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





@dataclass
class __lowerCAmelCase					:




			"""simple docstring"""







			snake_case						   =					field(
			    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
			snake_case						   =					field(
			    default="summarization"  ,					metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}  ,					)
			snake_case						   =					field(
			    default=10_24  ,					metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }  ,					)
			snake_case						   =					field(
			    default=1_28  ,					metadata={
			        "help": (
			            "The maximum total sequence length for target text after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }  ,					)
			snake_case						   =					field(
			    default=1_42  ,					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``."
			        )
			    }  ,					)
			snake_case						   =					field(
			    default=1_42  ,					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."
			        )
			    }  ,					)
			snake_case						   =					field(default=-1  ,					metadata={"help": "# training examples. -1 means use all."}   )
			snake_case						   =					field(default=-1  ,					metadata={"help": "# validation examples. -1 means use all."}   )
			snake_case						   =					field(default=-1  ,					metadata={"help": "# test examples. -1 means use all."}   )
			snake_case						   =					field(default=_lowercase  ,					metadata={"help": "Source language id for translation."}   )
			snake_case						   =					field(default=_lowercase  ,					metadata={"help": "Target language id for translation."}   )
			snake_case						   =					field(default=_lowercase  ,					metadata={"help": "# num_beams to use for evaluation."}   )
			snake_case						   =					field(
			    default=_lowercase  ,					metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}  ,					)





def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





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

					'''simple docstring'''





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

					check_output_dir(__a					)

					# 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"					, __a					)

					# 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.

					A_								=    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					, )

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

					A_								=    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					, )
					A_								=    AutoModelForSeqaSeqLM.from_pretrained(
					    model_args.model_name_or_path					, from_tf=".ckpt" in model_args.model_name_or_path					, config=__a					, cache_dir=model_args.cache_dir					, )

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

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

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

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

					A_								=    SeqaSeqDataset

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

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

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

										trainer.save_model()  # this also saves the tokenizer

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

															# 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 ***"					)

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

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

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

										A_								=    trainer.predict(test_dataset=__a					, metric_key_prefix="test"					)
										A_								=    test_output.metrics
										A_								=    data_args.n_test

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

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

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

					return all_metrics
def 				A_  (__a					):

					'''simple docstring'''





					main()


if __name__ == "__main__":
		main()






"""simple docstring"""





from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCamelCase_       :    Dict						      =			{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
		from .config import (
		    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
		    OnnxConfig,
		    OnnxConfigWithPast,
		    OnnxSeqaSeqConfigWithPast,
		    PatchingSpec,
		)
		from .convert import export, validate_model_outputs
		from .features import FeaturesManager
		from .utils import ParameterFormat, compute_serialized_parameters_size

else:
		import sys

		UpperCamelCase_       :    List[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




