'''simple docstring'''




def _lowercase	(      lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				list[list[float]]       				=		[]
       for data in source_data:
              for i, el in enumerate(_lowerCamelCase						):
                     if len(_lowerCamelCase						) < i + 1:
                            data_lists.append([]						)
                     data_lists[i].append(float(_lowerCamelCase						)						)
       return data_lists

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       __UpperCAmelCase  :				list[list[float]]       				=		[]
       for dlist, weight in zip(_lowerCamelCase							,    _lowerCamelCase						):
              __UpperCAmelCase  :				List[str]       				=		min(_lowerCamelCase						)
              __UpperCAmelCase  :				Optional[int]       				=		max(_lowerCamelCase						)

              __UpperCAmelCase  :				list[float]       				=		[]
              # for weight 0 score is 1 - actual score
              if weight == 0:
                     for item in dlist:
                            try:
                                   score.append(1 - ((item - mind) / (maxd - mind))						)
                            except ZeroDivisionError:
                                   score.append(1						)

              elif weight == 1:
                     for item in dlist:
                            try:
                                   score.append((item - mind) / (maxd - mind)						)
                            except ZeroDivisionError:
                                   score.append(0						)

        # weight not 0 or 1
              else:
                     __UpperCAmelCase  :				Any       				=		f"""Invalid weight of {weight:f} provided"""
                     raise ValueError(_lowerCamelCase						)

              score_lists.append(_lowerCamelCase						)

       return score_lists

def _lowercase	(      lowerCamelCase__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				list[float]       				=		[0 for i in range(len(score_lists[0]						)						)]

       for slist in score_lists:
              for j, ele in enumerate(_lowerCamelCase						):
                     __UpperCAmelCase  :				List[Any]       				=		final_scores[j] + ele

       return final_scores



def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		get_data(_lowerCamelCase						)
       __UpperCAmelCase  :				str       				=		calculate_each_score(_lowerCamelCase							,    _lowerCamelCase						)
       __UpperCAmelCase  :				Any       				=		generate_final_scores(_lowerCamelCase						)

       # append scores to source data
       for i, ele in enumerate(_lowerCamelCase						):
              source_data[i].append(_lowerCamelCase						)

       return source_data







'''simple docstring'''




from typing import TYPE_CHECKING

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


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






