





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






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






def 				_lowerCAmelCase			(	)						->      str:
     lowerCAmelCase_       :							List[Any]			 =		sys.argv[0]

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

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

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

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

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

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

     lowerCAmelCase_       :							Optional[int]			 =		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]      )      )

     lowerCAmelCase_       :							str			 =		results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     lowerCAmelCase_       :							Tuple			 =		[F'--{k} {v}' for k, v in best.items()]
     lowerCAmelCase_       :							Optional[Any]			 =		["""--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"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

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


