





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




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




def 			lowerCAmelCase_			(				)  ->		Tuple:
       UpperCamelCase_ 							=	sys.argv[0]

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

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

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

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

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

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

       UpperCamelCase_ 							=	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]	)	)

       UpperCamelCase_ 							=	results_sorted[0]
       for score in task_score_names[task]:
              del best[score]
       UpperCamelCase_ 							=	[F'''--{k} {v}''' for k, v in best.items()]
       UpperCamelCase_ 							=	["--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 os

from ...utils import logging
from .utils import DataProcessor, InputExample


A     =      logging.get_logger(__name__)






class   UpperCAmelCase__		(   UpperCamelCase  ):



		def __init__(			self	:     List[str]   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Tuple:






									'''simple docstring'''

									A    =       language
									A    =       train_language



		def        A_		(			self	:     Optional[int]   ,				snake_case	:     Any					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.language if self.train_language is None else self.train_language
									A    =       self._read_tsv(os.path.join(snake_case   ,				f"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       f"""train-{i}"""
																A    =       line[0]
																A    =       line[1]
																A    =       'contradiction' if line[2] == 'contradictory' else line[2]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     int   ,				snake_case	:     int					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self._read_tsv(os.path.join(snake_case   ,				'XNLI-1.0/xnli.test.tsv'					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       line[0]
																if language != self.language:
																							continue
																A    =       f"""test-{i}"""
																A    =       line[6]
																A    =       line[7]
																A    =       line[1]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     Any					)      ->    List[Any]:






									'''simple docstring'''

									return ["contradiction", "entailment", "neutral"]



A     =      {
    'xnli': XnliProcessor,
}

A     =      {
    'xnli': 'classification',
}

A     =      {
    'xnli': 3,
}



