


"""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__				:Tuple			   =	logging.getLogger(__name__)






@dataclass
class      A:

		"""simple docstring"""





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






@dataclass
class      A:

		"""simple docstring"""





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





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





def 				A_       ( )					->  Any:
			# 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.

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

			check_output_dir(snake_case__		)

			# 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'''       ,					snake_case__		)

			# 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.

			_UpperCamelCase					:List[Any]					     =	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       ,					)

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

			_UpperCamelCase					:Tuple					     =	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       ,					)
			_UpperCamelCase					:str					     =	AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path       ,					from_tf='''.ckpt''' in model_args.model_name_or_path       ,					config=snake_case__       ,					cache_dir=model_args.cache_dir       ,					)

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

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

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(snake_case__       ,					(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(snake_case__       ,					snake_case__		):
									_UpperCamelCase					:Dict					     =	tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									_UpperCamelCase					:Optional[int]					     =	tokenizer.convert_tokens_to_ids(data_args.tgt_lang		)

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

			_UpperCamelCase					:List[str]					     =	SeqaSeqDataset

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

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

						_UpperCamelCase					:Optional[int]					     =	trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
						_UpperCamelCase					:List[Any]					     =	train_result.metrics
						_UpperCamelCase					:Union[str, Any]					     =	data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

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

									# 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 ***'''		)

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

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

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

						_UpperCamelCase					:List[str]					     =	trainer.predict(test_dataset=snake_case__       ,					metric_key_prefix='''test'''		)
						_UpperCamelCase					:Tuple					     =	test_output.metrics
						_UpperCamelCase					:Optional[int]					     =	data_args.n_test

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

									if training_args.predict_with_generate:
												_UpperCamelCase					:Union[str, Any]					     =	tokenizer.batch_decode(
												    test_output.predictions       ,					skip_special_tokens=snake_case__       ,					clean_up_tokenization_spaces=snake_case__		)
												_UpperCamelCase					:Any					     =	lmap(str.strip       ,					snake_case__		)
												write_txt_file(snake_case__       ,					os.path.join(training_args.output_dir       ,					'''test_generations.txt'''		)		)

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

			return all_metrics





def 				A_       ( snake_case__		)					->  List[Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()





"""simple docstring"""






import os





def 				A_       ( )					->  Any:
			with open(os.path.dirname(snake_case__		) + '''/p022_names.txt'''		) as file:
						_UpperCamelCase					:Optional[Any]					     =	str(file.readlines()[0]		)
						_UpperCamelCase					:Dict					     =	names.replace('''"'''       ,					''''''		).split(''','''		)

			names.sort()

			_UpperCamelCase					:str					     =	0
			_UpperCamelCase					:Union[str, Any]					     =	0

			for i, name in enumerate(snake_case__		):
						for letter in name:
									name_score += ord(snake_case__		) - 64

						total_score += (i + 1) * name_score
						_UpperCamelCase					:List[Any]					     =	0
			return total_score


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

