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  :					Tuple          =  {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__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(lowercase							) for x in itertools.product(*lowercase							)]
				return matrix, entry_names





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__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=lowercase							,      required=lowercase							,      help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''							,      )
				parser.add_argument(
				    '''--bs'''							,      type=lowercase							,      default=8							,      required=lowercase							,      help='''initial batch size (may get reduced if it\'s too big)'''							)
				parser.add_argument('''--task'''							,      type=lowercase							,      help='''used for task_specific_params + metrics'''							)
				parser.add_argument(
				    '''--info'''							,      nargs='''?'''							,      type=lowercase							,      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(lowercase							)							) for col in col_names}
				__lowercase			      =							[]
				for r in matrix:
								__lowercase			      =							dict((x.replace('''--'''							,      ''''''							).split() for x in r)							)
								__lowercase			      =							''' '''.join(lowercase							).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=lowercase							)
								# make sure scores are first in the table
								__lowercase			      =							OrderedDict()
								for score in task_score_names[task]:
												__lowercase			      =							scores[score]
								result.update(lowercase							)
								results.append(lowercase							)

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

				__lowercase			      =							sorted(lowercase							,      key=operator.itemgetter(*task_score_names[task]							)							,      reverse=lowercase							)
				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()
from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


__a  :					Union[str, Any]          =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      lowercase							):
								return _split_by_node_map_style_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)
				else:
								return _split_by_node_iterable_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)