






"""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,
)


A__	:    Tuple			 = logging.getLogger(__name__)







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







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


def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Any  )    ->     Tuple:
							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 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Optional[int]									=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_	:							Union[str, Any]									=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Dict									=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_	:							Tuple									=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_	:							List[Any]									=("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_	:							List[str]									=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_	:							int									=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_	:							Dict									=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_	:							Optional[Any]									=tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					lowerCamelCase_	:							int									=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_	:							List[str]									=SeqaSeqDataset

							# Get datasets
							lowerCamelCase_	:							str									=(
							    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_	:							Any									=(
							    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_	:							str									=(
							    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_	:							Optional[int]									=(
							    build_compute_metrics_fn(data_args.task    ,						lowerCAmelCase__  ) if training_args.predict_with_generate else None
							)
							lowerCamelCase_	:							Dict									=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_	:							Optional[int]									={}
							# Training
							if training_args.do_train:
														logger.info("*** Train ***"  )

														lowerCamelCase_	:							Tuple									=trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
														lowerCamelCase_	:							Tuple									=train_result.metrics
														lowerCamelCase_	:							List[Any]									=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_	:							Optional[int]									=trainer.evaluate(metric_key_prefix="val"  )
														lowerCamelCase_	:							int									=data_args.n_val
														lowerCamelCase_	:							int									=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_	:							List[Any]									=trainer.predict(test_dataset=lowerCAmelCase__    ,						metric_key_prefix="test"  )
														lowerCamelCase_	:							Any									=test_output.metrics
														lowerCamelCase_	:							Dict									=data_args.n_test

														if trainer.is_world_process_zero():
																					lowerCamelCase_	:							Union[str, Any]									=round(metrics["test_loss"]    ,						4  )
																					handle_metrics("test"    ,						lowerCAmelCase__    ,						training_args.output_dir  )
																					all_metrics.update(lowerCAmelCase__  )

																					if training_args.predict_with_generate:
																												lowerCamelCase_	:							Tuple									=tokenizer.batch_decode(
																												    test_output.predictions    ,						skip_special_tokens=lowerCAmelCase__    ,						clean_up_tokenization_spaces=lowerCAmelCase__  )
																												lowerCamelCase_	:							Dict									=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 			_snake_case    (					lowerCamelCase__     :   int  )    ->     Tuple:
							main()


if __name__ == "__main__":
			main()











"""simple docstring"""



import torch

def 			_snake_case    (					)    ->     Union[str, Any]:
							if torch.cuda.is_available():
														lowerCamelCase_	:							int									=torch.cuda.device_count()
							else:
														lowerCamelCase_	:							List[str]									=0
							print(F"""Successfully ran on {num_gpus} GPUs"""  )


if __name__ == "__main__":
						main()



