


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      =     {
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A      =     ["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
						from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
						from .tokenization_fsmt import FSMTTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
						import sys

						_A      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






'''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 A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = search.split()
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = dict((g.split('''='''       ) for g in groups)       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(entries.keys()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [[f"""--{k} {v}""" for v in vs.split(''':'''       )] for k, vs in entries.items()]
					__SCREAMING_SNAKE_CASE					:							int			   = [list(__SCREAMING_SNAKE_CASE       ) for x in itertools.product(*__SCREAMING_SNAKE_CASE       )]
					return matrix, entry_names


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = 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=__SCREAMING_SNAKE_CASE	,   required=__SCREAMING_SNAKE_CASE	,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''	,   )
					parser.add_argument(
					    '''--bs'''	,   type=__SCREAMING_SNAKE_CASE	,   default=8	,   required=__SCREAMING_SNAKE_CASE	,   help='''initial batch size (may get reduced if it\'s too big)'''       )
					parser.add_argument('''--task'''	,   type=__SCREAMING_SNAKE_CASE	,   help='''used for task_specific_params + metrics'''       )
					parser.add_argument(
					    '''--info'''	,   nargs='''?'''	,   type=__SCREAMING_SNAKE_CASE	,   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.'''
					    )	,   )
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = parser.parse_known_args()
					# we share some of the args
					args_main.extend(['''--task''', args.task]       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = [prog] + args_main

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

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

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

										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = run_generate(verbose=__SCREAMING_SNAKE_CASE       )
										# make sure scores are first in the table
										__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict()
										for score in task_score_names[task]:
															__SCREAMING_SNAKE_CASE					:							int			   = scores[score]
										result.update(__SCREAMING_SNAKE_CASE       )
										results.append(__SCREAMING_SNAKE_CASE       )

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

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					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]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--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()


