



"""simple docstring"""







import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
A     =      {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Any:
							A    =       search.split()
							A    =       dict((g.split('='      ) for g in groups)      )
							A    =       list(entries.keys()      )
							A    =       [[f"""--{k} {v}""" for v in vs.split(':'      )] for k, vs in entries.items()]
							A    =       [list(lowerCamelCase__      ) for x in itertools.product(*lowerCamelCase__      )]
							return matrix, entry_names

def 	lowerCAmelCase__      (  )			->				Tuple:
							A    =       sys.argv[0]

							A    =       argparse.ArgumentParser(
							    usage=(
							        '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
							        ' refer to `run_eval.py -h` for the complete list.'
							    )      )
							parser.add_argument(
							    '--search'					,   type=lowerCamelCase__					,   required=lowerCamelCase__					,   help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'					,   )
							parser.add_argument(
							    '--bs'					,   type=lowerCamelCase__					,   default=8					,   required=lowerCamelCase__					,   help='initial batch size (may get reduced if it\'s too big)'      )
							parser.add_argument('--task'					,   type=lowerCamelCase__					,   help='used for task_specific_params + metrics'      )
							parser.add_argument(
							    '--info'					,   nargs='?'					,   type=lowerCamelCase__					,   const=datetime_now()					,   help=(
							        'add custom notes to be printed before the results table. If no value is passed, the current datetime'
							        ' string will be used.'
							    )					,   )
							A   ,	A    =       parser.parse_known_args()
							# we share some of the args
							args_main.extend(['--task', args.task]      )
							A    =       [prog] + args_main

							# to support variations like translation_en_to_de"
							A    =       'translation' if 'translation' in args.task else 'summarization'

							A   ,	A    =       parse_search_arg(args.search      )
							A    =       task_score_names[task]  # score cols first
							A    =       {col: len(str(lowerCamelCase__      )      ) for col in col_names}
							A    =       []
							for r in matrix:
														A    =       dict((x.replace('--'					,   ''      ).split() for x in r)      )
														A    =       ' '.join(lowerCamelCase__      ).split()
														args_exp.extend(['--bs', str(args.bs      )]      )  # in case we need to reduce its size due to CUDA OOM
														A    =       args_normal + args_exp

														# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

														A    =       run_generate(verbose=lowerCamelCase__      )
														# make sure scores are first in the table
														A    =       OrderedDict()
														for score in task_score_names[task]:
																					A    =       scores[score]
														result.update(lowerCamelCase__      )
														results.append(lowerCamelCase__      )

														# find widest entries
														for k, v in result.items():
																					A    =       len(str(lowerCamelCase__      )      )
																					if l > col_widths[k]:
																												A    =       l

							A    =       sorted(lowerCamelCase__					,   key=operator.itemgetter(*task_score_names[task]      )					,   reverse=lowerCamelCase__      )
							print(' | '.join([f"""{col:{col_widths[col]}}""" for col in col_names]      )      )
							print(' | '.join([f"""{'-'*col_widths[col]}""" for col in col_names]      )      )
							for row in results_sorted:
														print(' | '.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]      )      )

							A    =       results_sorted[0]
							for score in task_score_names[task]:
														del best[score]
							A    =       [f"""--{k} {v}""" for k, v in best.items()]
							A    =       ['--bs', str(args.bs      )]
							if args.info:
														print(f"""\nInfo: {args.info}"""      )
							print('\nBest score args:'      )
							print(' '.join(args_main + best_args + dyn_args      )      )

							return results_sorted


if __name__ == "__main__":
							# Usage:
							# [normal-run_eval_search.py cmd plus] \
							# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
							#
							# Example:
							# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
							# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
							# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
							# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
							run_search()








"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


A     =      logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase__		:
		lowerCAmelCase_							:						str							=  field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						bool							=  field(default=UpperCamelCase ,metadata={"""help""": """Set this flag to use fast tokenization."""}  )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)



def 	lowerCAmelCase__      (  )			->				Optional[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.

							A    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

							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()

							if (
							    os.path.exists(training_args.output_dir      )
							    and os.listdir(training_args.output_dir      )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'      )

							# 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.local_rank != -1      )					,   training_args.fpaa					,   )
							# 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()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'					,   lowerCamelCase__      )

							# Prepare Question-Answering task
							# 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    =       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					,   use_fast=lowerCamelCase__					,   )
							A    =       AutoModelForQuestionAnswering.from_pretrained(
							    model_args.model_name_or_path					,   from_tf=bool('.ckpt' in model_args.model_name_or_path      )					,   config=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )

							# Get datasets
							A    =       hasattr(model.config					,   'lang2id'      )
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir      )
							    if training_args.do_train
							    else None
							)
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   mode='dev'					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )
							    if training_args.do_eval
							    else None
							)

							# Data collator
							A    =       DataCollatorWithPadding(lowerCamelCase__					,   pad_to_multiple_of=8      ) if training_args.fpaa else None

							# Initialize our Trainer
							A    =       Trainer(
							    model=lowerCamelCase__					,   args=lowerCamelCase__					,   train_dataset=lowerCamelCase__					,   eval_dataset=lowerCamelCase__					,   data_collator=lowerCamelCase__					,   )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir      )

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()



