





'''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
_SCREAMING_SNAKE_CASE    =			{
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}




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




def       _lowerCAmelCase  ( ):
			__lowercase   =							sys.argv[0]

			__lowercase   =							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.'''
			    )						,				)
			__lowercase  ,						__lowercase   =							parser.parse_known_args()
			# we share some of the args
			args_main.extend(['''--task''', args.task] )
			__lowercase   =							[prog] + args_main

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

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

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

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

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

			__lowercase   =							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] ) )

			__lowercase   =							results_sorted[0]
			for score in task_score_names[task]:
						del best[score]
			__lowercase   =							[f"--{k} {v}" for k, v in best.items()]
			__lowercase   =							['''--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 csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_SCREAMING_SNAKE_CASE    =			'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

# TODO: Add description of the dataset here
# You can copy an official description
_SCREAMING_SNAKE_CASE    =			'''\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
'''

# TODO: Add a link to an official homepage for the dataset here
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_SCREAMING_SNAKE_CASE    =			{
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				datasets.Version("1.1.0"  )

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	a			: List[Any]      =				[
	    datasets.BuilderConfig(name="first_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a first domain"  ),
	    datasets.BuilderConfig(name="second_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a second domain"  ),
	]

	a			: Dict      =				"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option1''': datasets.Value('''string'''       ),
							        '''answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option2''': datasets.Value('''string'''       ),
							        '''second_domain_answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				return datasets.DatasetInfo(
				    # This is the description that will appear on the datasets page.
				    description=_DESCRIPTION   ,features=_lowerCamelCase   ,homepage=_HOMEPAGE   ,license=_LICENSE   ,citation=_CITATION   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							_URLS[self.config.name]
				__lowercase   =							dl_manager.download_and_extract(_lowerCamelCase       )
				return [
				    datasets.SplitGenerator(
				        name=datasets.Split.TRAIN   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''train.jsonl'''       ),
				            '''split''': '''train''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.VALIDATION   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''dev.jsonl'''       ),
				            '''split''': '''dev''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.TEST   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''test.jsonl'''       ),
				            '''split''': '''test'''
				        }   ,),
				]


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as f:
							for key, row in enumerate(_lowerCamelCase       ):
										__lowercase   =							json.loads(_lowerCamelCase       )
										if self.config.name == "first_domain":
													# Yields examples as (key, example) tuples
													yield key, {
													    "sentence": data["sentence"],
													    "option1": data["option1"],
													    "answer": "" if split == "test" else data["answer"],
													}
										else:
													yield key, {
													    "sentence": data["sentence"],
													    "option2": data["option2"],
													    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
													}





