"""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
_snake_case					:		int							=			{
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}






def  A__    (      UpperCamelCase	):
							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(UpperCamelCase	) for x in itertools.product(*UpperCamelCase	)]
							return matrix, entry_names






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

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

							A		=  sorted(UpperCamelCase ,  key=operator.itemgetter(*task_score_names[task]	) ,  reverse=UpperCamelCase	)
							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 os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."		)

												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


