

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



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]				):


     '''simple docstring'''






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



def        _lowerCAmelCase      (						):


     '''simple docstring'''






     UpperCamelCase__		:    Optional[Any]         =sys.argv[0]

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

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

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

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

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

          # find widest entries
          for k, v in result.items():
               UpperCamelCase__		:    Optional[Any]         =len(str(UpperCAmelCase				)				)
               if l > col_widths[k]:
                    UpperCamelCase__		:    Dict         =l

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





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


