
"""simple docstring"""




def     __magic_name__      ( __snake_case							:			list[list[float]]    )					->							list[list[float]]:
       lowercase       :							list[list[float]]									=				[]
       for data in source_data:
              for i, el in enumerate(__snake_case    ):
                     if len(__snake_case    ) < i + 1:
                            data_lists.append([]    )
                     data_lists[i].append(float(__snake_case    )    )
       return data_lists


def     __magic_name__      ( __snake_case							:			list[list[float]]	,					__snake_case							:			list[int]    )					->							list[list[float]]:
       lowercase       :							list[list[float]]									=				[]
       for dlist, weight in zip(__snake_case	,					__snake_case    ):
              lowercase       :							Optional[Any]									=				min(__snake_case    )
              lowercase       :							Optional[Any]									=				max(__snake_case    )

              lowercase       :							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:
                     lowercase       :							List[Any]									=				f"""Invalid weight of {weight:f} provided"""
                     raise ValueError(__snake_case    )

              score_lists.append(__snake_case    )

       return score_lists


def     __magic_name__      ( __snake_case							:			list[list[float]]    )					->							list[float]:
       lowercase       :							list[float]									=				[0 for i in range(len(score_lists[0]    )    )]

       for slist in score_lists:
              for j, ele in enumerate(__snake_case    ):
                     lowercase       :							str									=				final_scores[j] + ele

       return final_scores


def     __magic_name__      ( __snake_case							:			list[list[float]]	,					__snake_case							:			list[int]    )					->							list[list[float]]:
       lowercase       :							Union[str, Any]									=				get_data(__snake_case    )
       lowercase       :							Optional[int]									=				calculate_each_score(__snake_case	,					__snake_case    )
       lowercase       :							Tuple									=				generate_final_scores(__snake_case    )

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

       return source_data




"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A     :			Optional[int]  						=			{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			[
       """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MgpstrModel""",
       """MgpstrPreTrainedModel""",
       """MgpstrForSceneTextRecognition""",
   ]

if TYPE_CHECKING:
   from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
   from .processing_mgp_str import MgpstrProcessor
   from .tokenization_mgp_str import MgpstrTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mgp_str import (
          MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
          MgpstrForSceneTextRecognition,
          MgpstrModel,
          MgpstrPreTrainedModel,
      )
else:
   import sys

   _A     :			Tuple  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


