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







def 						UpperCAmelCase							(		)						->						Optional[Any]:
     a	:     str						      =    sys.argv[0]

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

     # to support variations like translation_en_to_de"
     a	:     int						      =    '''translation''' if '''translation''' in args.task else '''summarization'''

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

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

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

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

     a	:     Optional[int]						      =    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]							)							)

     a	:     Dict						      =    results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     a	:     str						      =    [f"""--{k} {v}""" for k, v in best.items()]
     a	:     List[str]						      =    ['''--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 re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     a	:     Optional[Any]						      =    min(re.search('''^ +'''    ,   lowerCAmelCase_							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCAmelCase_							)]							)


UpperCAmelCase				 =   """\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

UpperCAmelCase				 =   """\
---
---
# Begin of markdown

Some cool dataset card
"""


UpperCAmelCase				 =   """\
# Begin of markdown

Some cool dataset card
"""






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def _lowercase(     self     )						->List[str]:
        with tempfile.TemporaryDirectory() as tmp_dir:
             a	:     List[str]						      =    Path(lowerCAmelCase_     ) / '''README.md'''
             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     Dict						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[str]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[Any]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertEqual(lowerCAmelCase_				,  {}     )


   def _lowercase(     self     )						->Optional[Any]:
        a	:     Tuple						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Dict						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''     )
        with self.assertRaises(lowerCAmelCase_     ):
             DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Optional[int]						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )