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






def SCREAMING_SNAKE_CASE__   (							snake_case    :							str   )					->							List[str]:







     """simple docstring"""
     a :				int				=      search.split()
     a :				Union[str, Any]				=      dict((g.split('='   ) for g in groups)   )
     a :				Union[str, Any]				=      list(entries.keys()   )
     a :				List[str]				=      [[F"""--{k} {v}""" for v in vs.split(':'   )] for k, vs in entries.items()]
     a :				int				=      [list(UpperCAmelCase__   ) for x in itertools.product(*UpperCAmelCase__   )]
     return matrix, entry_names






def SCREAMING_SNAKE_CASE__   (							)					->							Dict:







     """simple docstring"""
     a :				int				=      sys.argv[0]

     a :				int				=      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 :				Dict				=      parser.parse_known_args()
     # we share some of the args
     args_main.extend(['--task', args.task]   )
     a :				Any				=      [prog] + args_main

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

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

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

          a :				Tuple				=      run_generate(verbose=UpperCAmelCase__   )
          # make sure scores are first in the table
          a :				List[str]				=      OrderedDict()
          for score in task_score_names[task]:
               a :				Tuple				=      scores[score]
          result.update(UpperCAmelCase__   )
          results.append(UpperCAmelCase__   )

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

     a :				Union[str, Any]				=      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 :				Optional[int]				=      results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     a :				Dict				=      [F"""--{k} {v}""" for k, v in best.items()]
     a :				Dict				=      ['--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()


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_lowerCamelCase    :      int	 =						10_00_00_00_00_00
_lowerCamelCase    :      Union[str, Any]	 =						1_00_00

_lowerCamelCase ,  _lowerCamelCase    :      List[str]	 =						os.path.split(__file__)
_lowerCamelCase    :      Any	 =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				pa.Table.from_pydict({"col": [0] * chunk_size}     )
					SCREAMING_SNAKE_CASE				=				pa.concat_tables([table] * (num_examples // chunk_size)     )
					return datasets.Dataset(UpperCAmelCase__					,       fingerprint="table_100B"     )


@dataclass
class   lowercase			:
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int




							def 		__snake_case(  self      :    Tuple					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				np.random.default_rng(self.seed					)
												SCREAMING_SNAKE_CASE				=				rng.integers(low=self.low		,							high=self.high		,							size=self.size					).tolist()




							def __iter__(  self      :    Union[str, Any]					)    -> Dict:



												'''simple docstring'''

												return iter(self._sampled_values					)





							def __len__(  self      :    Any					)    -> int:



												'''simple docstring'''

												return self.size





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[0]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[-1]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[range(len(UpperCAmelCase__     ) // 2					,       len(UpperCAmelCase__     ) // 2 + 1_0_2_4     )]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[RandIter(0					,       len(UpperCAmelCase__     )					,       1_0_2_4					,       seed=4_2     )]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				{"num examples": SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset"     )
					SCREAMING_SNAKE_CASE				=				generate_100B_dataset(num_examples=UpperCAmelCase__					,       chunk_size=UpperCAmelCase__     )
					print("Functions"     )
					for func in functions:
										print(func.__name__     )
										SCREAMING_SNAKE_CASE				=				func(UpperCAmelCase__     )

					with open(UpperCAmelCase__					,       "wb"     ) as f:
										f.write(json.dumps(UpperCAmelCase__     ).encode("utf-8"     )     )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()




