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







@dataclass
class 					lowerCamelCase		:
					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=10_24							,			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=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."
					        )
					    }							,			)
					UpperCAmelCase__    :							Optional[int]									=		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``."
					        )
					    }							,			)
					UpperCAmelCase__    :							Optional[int]									=		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."
					        )
					    }							,			)
					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=A_							,			metadata={"help": "Source language id for translation."}  )
					UpperCAmelCase__    :							Optional[str]									=		field(default=A_							,			metadata={"help": "Target language id for translation."}  )
					UpperCAmelCase__    :							Optional[int]									=		field(default=A_							,			metadata={"help": "# num_beams to use for evaluation."}  )
					UpperCAmelCase__    :							bool									=		field(
					    default=A_							,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}							,			)



def 						lowercase_      (       A__			,		A__			,		A__		)							->							Dict:
				"""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 						lowercase_      (       )							->							Tuple:
				"""simple docstring"""

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

				snake_case								= 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			,		)

				snake_case								= ("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__		)		)

				snake_case								= 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			,		)
				snake_case								= 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:
								snake_case								= 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__		):
												snake_case								= tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												snake_case								= 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()		)

				snake_case								= SeqaSeqDataset

				# Get datasets
				snake_case								= (
				    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
				)
				snake_case								= (
				    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
				)
				snake_case								= (
				    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
				snake_case								= (
				    build_compute_metrics_fn(data_args.task			,		A__		) if training_args.predict_with_generate else None
				)
				snake_case								= 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__			,		)

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

								snake_case								= trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
								snake_case								= train_result.metrics
								snake_case								= 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 ***"		)

								snake_case								= trainer.evaluate(metric_key_prefix="val"		)
								snake_case								= data_args.n_val
								snake_case								= 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 ***"		)

								snake_case								= trainer.predict(test_dataset=A__			,		metric_key_prefix="test"		)
								snake_case								= test_output.metrics
								snake_case								= data_args.n_test

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

												if training_args.predict_with_generate:
																snake_case								= tokenizer.batch_decode(
																    test_output.predictions			,		skip_special_tokens=A__			,		clean_up_tokenization_spaces=A__		)
																snake_case								= 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 						lowercase_      (       A__		)							->							Optional[Any]:
				"""simple docstring"""

				main()


if __name__ == "__main__":
			main()



def 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

