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
_SCREAMING_SNAKE_CASE   		=			{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}





def  lowercase(  UpperCamelCase_					)		->  Union[str, Any]:







 '''simple docstring'''
 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(UpperCamelCase_					) for x in itertools.product(*UpperCamelCase_					)]
 return matrix, entry_names





def  lowercase(  )		->  Any:







 '''simple docstring'''
 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=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."""
     )       ,				)
 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(UpperCamelCase_					)					) for col in col_names}
 UpperCamelCase      =			[]
 for r in matrix:
  UpperCamelCase      =			dict((x.replace("""--"""       ,				""""""					).split() for x in r)					)
  UpperCamelCase      =			""" """.join(UpperCamelCase_					).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=UpperCamelCase_					)
  # make sure scores are first in the table
  UpperCamelCase      =			OrderedDict()
  for score in task_score_names[task]:
   UpperCamelCase      =			scores[score]
  result.update(UpperCamelCase_					)
  results.append(UpperCamelCase_					)

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

 UpperCamelCase      =			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]					)					)

 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()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

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