


'''simple docstring'''






from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
  from transformers import (
      TFBertModel,
      TFCLIPVisionModel,
      TFDeiTModel,
      TFRobertaModel,
      TFVisionTextDualEncoderModel,
      TFViTModel,
      VisionTextDualEncoderConfig,
  )

if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor







def 				UpperCamelCase_			(    A__					:   Union[str, Any]					):
  '''simple docstring'''





  if isinstance(A__							,    collections.abc.Iterable					):
    return x
  return (x, x)






@require_tf
class    __snake_case       :







    """simple docstring"""



    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  int		)   ->  Optional[int]:
      pass

    def __lowercase   (   self    :  Union[str, Any]		)   ->  Union[str, Any]:
      pass

    def __lowercase   (   self    :  Any		)   ->  int:
      pass

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[Any]=None    ,						**lowerCamelCase    :  List[Any]		)   ->  Tuple:
      lowerCAmelCase_			:      int			=   VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      str			=   TFVisionTextDualEncoderModel(lowerCamelCase		)

      lowerCAmelCase_			:      List[Any]			=   model(input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase		)

      self.assertEqual(output["""text_embeds"""].shape    ,						(input_ids.shape[0], config.projection_dim)		)
      self.assertEqual(output["""image_embeds"""].shape    ,						(pixel_values.shape[0], config.projection_dim)		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Optional[Any]    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  Optional[Any]=None    ,						**lowerCamelCase    :  Any		)   ->  List[Any]:
      lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   self.get_vision_text_model(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   TFVisionTextDualEncoderModel(vision_model=lowerCamelCase    ,						text_model=lowerCamelCase		)

      lowerCAmelCase_			:      Tuple			=   model(input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase		)

      self.assertEqual(output["""text_embeds"""].shape    ,						(input_ids.shape[0], model.config.projection_dim)		)
      self.assertEqual(output["""image_embeds"""].shape    ,						(pixel_values.shape[0], model.config.projection_dim)		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  List[Any]    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  Any=None    ,						**lowerCamelCase    :  str		)   ->  Tuple:
      lowerCAmelCase_,							lowerCAmelCase_			:      int			=   self.get_vision_text_model(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Any			=   {"""vision_model""": vision_model, """text_model""": text_model}
      lowerCAmelCase_			:      Optional[Any]			=   TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase		)

      lowerCAmelCase_			:      Optional[Any]			=   model(input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase		)

      self.assertEqual(output["""text_embeds"""].shape    ,						(input_ids.shape[0], model.config.projection_dim)		)
      self.assertEqual(output["""image_embeds"""].shape    ,						(pixel_values.shape[0], model.config.projection_dim)		)

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Dict=None    ,						**lowerCamelCase    :  Dict		)   ->  Any:
      lowerCAmelCase_,							lowerCAmelCase_			:      List[Any]			=   self.get_vision_text_model(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Any			=   TFVisionTextDualEncoderModel(vision_model=lowerCamelCase    ,						text_model=lowerCamelCase		)

      lowerCAmelCase_			:      Tuple			=   model(input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   output[0].numpy()

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(lowerCamelCase		)
        lowerCAmelCase_			:      List[Any]			=   TFVisionTextDualEncoderModel.from_pretrained(lowerCamelCase		)

        lowerCAmelCase_			:      int			=   model(input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   after_output[0].numpy()
        lowerCAmelCase_			:      List[Any]			=   np.amax(np.abs(out_a - out_a		)		)
        self.assertLessEqual(lowerCamelCase    ,						1E-5		)

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  List[Any]    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  Dict=None    ,						**lowerCamelCase    :  List[Any]		)   ->  int:
      lowerCAmelCase_,							lowerCAmelCase_			:      Optional[int]			=   self.get_vision_text_model(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   TFVisionTextDualEncoderModel(vision_model=lowerCamelCase    ,						text_model=lowerCamelCase		)

      lowerCAmelCase_			:      List[Any]			=   model(
          input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase    ,						output_attentions=lowerCamelCase		)

      lowerCAmelCase_			:      str			=   output.vision_model_output.attentions
      self.assertEqual(len(lowerCamelCase		)    ,						vision_config.num_hidden_layers		)

      # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
      lowerCAmelCase_			:      List[str]			=   to_atuple(vision_model.config.image_size		)
      lowerCAmelCase_			:      List[Any]			=   to_atuple(vision_model.config.patch_size		)
      lowerCAmelCase_			:      List[Any]			=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      lowerCAmelCase_			:      int			=   num_patches + 1
      self.assertEqual(vision_attentions[0].shape[-3:]    ,						(vision_config.num_attention_heads, seq_len, seq_len)		)

      lowerCAmelCase_			:      Dict			=   output.text_model_output.attentions
      self.assertEqual(len(lowerCamelCase		)    ,						text_config.num_hidden_layers		)

      self.assertEqual(
          text_attentions[0].shape[-3:]    ,						(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])    ,						)

    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  np.ndarray    ,						lowerCamelCase    :  np.ndarray    ,						lowerCamelCase    :  float		)   ->  Optional[int]:
      lowerCAmelCase_			:      Any			=   np.abs((a - b)		).max()
      self.assertLessEqual(lowerCamelCase    ,						lowerCamelCase    ,						F'Difference between torch and flax is {diff} (>= {tol}).'		)

    def __lowercase   (   self    :  List[Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Dict			=   self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_model(**lowerCamelCase		)

    def __lowercase   (   self    :  List[str]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**lowerCamelCase		)

    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      str			=   self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase		)

    def __lowercase   (   self    :  str		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Optional[Any]			=   self.prepare_config_and_inputs()
      self.check_save_load(**lowerCamelCase		)

    def __lowercase   (   self    :  Tuple		)   ->  Optional[Any]:
      lowerCAmelCase_			:      int			=   self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**lowerCamelCase		)





    @slow
    def __lowercase   (   self    :  Optional[Any]		)   ->  Optional[Any]:
      lowerCAmelCase_,							lowerCAmelCase_			:      List[Any]			=   self.get_pretrained_model_and_inputs()

      lowerCAmelCase_			:      Dict			=   model_a(**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   outputs[0].numpy()

      with tempfile.TemporaryDirectory() as tmp_dirname:
        model_a.save_pretrained(lowerCamelCase		)
        lowerCAmelCase_			:      Any			=   TFVisionTextDualEncoderModel.from_pretrained(lowerCamelCase		)

        lowerCAmelCase_			:      List[Any]			=   model_a(**lowerCamelCase		)
        lowerCAmelCase_			:      Dict			=   after_outputs[0].numpy()
        lowerCAmelCase_			:      int			=   np.amax(np.abs(out_a - out_a		)		)
        self.assertLessEqual(lowerCamelCase    ,						1E-5		)







@require_tf
class    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  Optional[Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      List[str]			=   TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          """hf-internal-testing/tiny-random-vit"""    ,						"""hf-internal-testing/tiny-random-bert"""		)
      lowerCAmelCase_			:      Any			=   13
      lowerCAmelCase_			:      int			=   floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]		)
      lowerCAmelCase_			:      Dict			=   ids_tensor([batch_size, 4]    ,						model.text_model.config.vocab_size		)
      lowerCAmelCase_			:      Union[str, Any]			=   random_attention_mask([batch_size, 4]		)
      lowerCAmelCase_			:      List[Any]			=   {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

      return model, inputs

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[Any]		)   ->  Any:
      lowerCAmelCase_			:      Any			=   TFViTModel(lowerCamelCase    ,						name="""vision_model"""		)
      lowerCAmelCase_			:      Tuple			=   TFBertModel(lowerCamelCase    ,						name="""text_model"""		)
      return vision_model, text_model





    def __lowercase   (   self    :  List[Any]		)   ->  List[Any]:
      lowerCAmelCase_			:      Optional[int]			=   TFViTModelTester(self		)
      lowerCAmelCase_			:      List[Any]			=   TFBertModelTester(self		)
      lowerCAmelCase_			:      str			=   vit_model_tester.prepare_config_and_inputs()
      lowerCAmelCase_			:      List[str]			=   bert_model_tester.prepare_config_and_inputs()

      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   vision_config_and_inputs

      (
          (
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							
      )			:      Tuple			=   text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }







@require_tf
class    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  Dict		)   ->  str:
      # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
      # just reinitialize it.
      lowerCAmelCase_			:      str			=   TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          """Rocketknight1/tiny-random-deit-tf"""    ,						"""hf-internal-testing/tiny-random-roberta"""		)
      lowerCAmelCase_			:      List[str]			=   13
      lowerCAmelCase_			:      str			=   floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]		)
      lowerCAmelCase_			:      Union[str, Any]			=   ids_tensor([batch_size, 4]    ,						model.text_model.config.vocab_size		)
      lowerCAmelCase_			:      Dict			=   random_attention_mask([batch_size, 4]		)
      lowerCAmelCase_			:      Optional[int]			=   {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

      return model, inputs

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  List[Any]=None    ,						**lowerCamelCase    :  Dict		)   ->  Any:
      lowerCAmelCase_,							lowerCAmelCase_			:      Union[str, Any]			=   self.get_vision_text_model(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   TFVisionTextDualEncoderModel(vision_model=lowerCamelCase    ,						text_model=lowerCamelCase		)

      lowerCAmelCase_			:      Union[str, Any]			=   model(
          input_ids=lowerCamelCase    ,						pixel_values=lowerCamelCase    ,						attention_mask=lowerCamelCase    ,						output_attentions=lowerCamelCase		)

      lowerCAmelCase_			:      Union[str, Any]			=   output.vision_model_output.attentions
      self.assertEqual(len(lowerCamelCase		)    ,						vision_config.num_hidden_layers		)

      # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
      lowerCAmelCase_			:      Optional[int]			=   to_atuple(vision_model.config.image_size		)
      lowerCAmelCase_			:      Tuple			=   to_atuple(vision_model.config.patch_size		)
      lowerCAmelCase_			:      Optional[Any]			=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      lowerCAmelCase_			:      List[str]			=   num_patches + 2
      self.assertEqual(vision_attentions[0].shape[-3:]    ,						(vision_config.num_attention_heads, seq_len, seq_len)		)

      lowerCAmelCase_			:      Tuple			=   output.text_model_output.attentions
      self.assertEqual(len(lowerCamelCase		)    ,						text_config.num_hidden_layers		)

      self.assertEqual(
          text_attentions[0].shape[-3:]    ,						(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])    ,						)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  Optional[int]		)   ->  List[str]:
      lowerCAmelCase_			:      Any			=   TFDeiTModel(lowerCamelCase    ,						name="""vision_model"""		)
      lowerCAmelCase_			:      List[Any]			=   TFRobertaModel(lowerCamelCase    ,						name="""text_model"""		)
      return vision_model, text_model





    def __lowercase   (   self    :  Tuple		)   ->  int:
      lowerCAmelCase_			:      str			=   TFDeiTModelTester(self		)
      lowerCAmelCase_			:      Tuple			=   TFRobertaModelTester(self		)
      lowerCAmelCase_			:      Optional[Any]			=   vit_model_tester.prepare_config_and_inputs()
      lowerCAmelCase_			:      Any			=   bert_model_tester.prepare_config_and_inputs()

      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   vision_config_and_inputs

      (
          (
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							
      )			:      int			=   text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }







@require_tf
class    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  List[str]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Union[str, Any]			=   TFVisionTextDualEncoderModel.from_vision_text_pretrained(
          """Rocketknight1/tiny-random-clip-tf"""    ,						"""hf-internal-testing/tiny-random-bert"""		)
      lowerCAmelCase_			:      Tuple			=   13
      lowerCAmelCase_			:      Tuple			=   floats_tensor(
          [
              batch_size,
              model.vision_model.config.num_channels,
              model.vision_model.config.image_size,
              model.vision_model.config.image_size,
          ]		)
      lowerCAmelCase_			:      Optional[Any]			=   ids_tensor([batch_size, 4]    ,						model.text_model.config.vocab_size		)
      lowerCAmelCase_			:      int			=   random_attention_mask([batch_size, 4]		)
      lowerCAmelCase_			:      Dict			=   {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

      return model, inputs

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[Any]			=   TFCLIPVisionModel(lowerCamelCase    ,						name="""vision_model"""		)
      lowerCAmelCase_			:      List[str]			=   TFBertModel(lowerCamelCase    ,						name="""text_model"""		)
      return vision_model, text_model





    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      str			=   TFCLIPVisionModelTester(self		)
      lowerCAmelCase_			:      Union[str, Any]			=   TFBertModelTester(self		)
      lowerCAmelCase_			:      List[Any]			=   clip_model_tester.prepare_config_and_inputs()
      lowerCAmelCase_			:      List[str]			=   bert_model_tester.prepare_config_and_inputs()

      lowerCAmelCase_,							lowerCAmelCase_			:      int			=   vision_config_and_inputs

      (
          (
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							(
          lowerCAmelCase_
      ),							
      )			:      List[str]			=   text_config_and_inputs

      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": input_mask,
          "input_ids": input_ids,
          "text_token_type_ids": token_type_ids,
          "text_sequence_labels": sequence_labels,
          "text_token_labels": token_labels,
          "text_choice_labels": choice_labels,
      }







@require_vision
@require_tf
class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    @slow
    def __lowercase   (   self    :  List[Any]		)   ->  Tuple:
      lowerCAmelCase_			:      Optional[int]			=   TFVisionTextDualEncoderModel.from_pretrained(
          """clip-italian/clip-italian"""    ,						logit_scale_init_value=1.0    ,						from_pt=lowerCamelCase		)
      lowerCAmelCase_			:      str			=   VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""		)

      lowerCAmelCase_			:      int			=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
      lowerCAmelCase_			:      List[str]			=   processor(
          text=["""una foto di un gatto""", """una foto di un cane"""]    ,						images=lowerCamelCase    ,						padding=lowerCamelCase    ,						return_tensors="""np"""		)

      lowerCAmelCase_			:      List[str]			=   model(**lowerCamelCase		)

      # verify the logits
      self.assertEqual(outputs.logits_per_image.shape    ,						(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
      self.assertEqual(
          outputs.logits_per_text.shape    ,						(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])    ,						)

      lowerCAmelCase_			:      List[str]			=   np.array([[1.2_284_727, 0.3_104_122]]		)

      self.assertTrue(np.allclose(outputs.logits_per_image.numpy()    ,						lowerCamelCase    ,						atol=1E-3		)		)







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





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







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      str			=   max(A__					)

    lowerCAmelCase_			:      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:
      lowerCAmelCase_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





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

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

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

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

  return source_data



