


"""simple docstring"""


from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       TFLayoutLMvaForQuestionAnswering,
       TFLayoutLMvaForSequenceClassification,
       TFLayoutLMvaForTokenClassification,
       TFLayoutLMvaModel,
   )

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor



class   _a      :

    '''simple docstring'''





    def __init__( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str=2							,					SCREAMING_SNAKE_CASE__							:					str=3							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=4							,					SCREAMING_SNAKE_CASE__							:					int=2							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=7							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=True							,					SCREAMING_SNAKE_CASE__							:					str=99							,					SCREAMING_SNAKE_CASE__							:					List[str]=36							,					SCREAMING_SNAKE_CASE__							:					Dict=2							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4							,					SCREAMING_SNAKE_CASE__							:					List[str]=37							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="gelu"							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.1							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.1							,					SCREAMING_SNAKE_CASE__							:					Dict=5_12							,					SCREAMING_SNAKE_CASE__							:					int=16							,					SCREAMING_SNAKE_CASE__							:					List[Any]=2							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0.02							,					SCREAMING_SNAKE_CASE__							:					Tuple=6							,					SCREAMING_SNAKE_CASE__							:					Any=6							,					SCREAMING_SNAKE_CASE__							:					str=3							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					str=10_00							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_input_mask
          lowerCamelCase__           =		use_token_type_ids
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		type_vocab_size
          lowerCamelCase__           =		type_sequence_label_size
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		coordinate_size
          lowerCamelCase__           =		shape_size
          lowerCamelCase__           =		num_labels
          lowerCamelCase__           =		num_choices
          lowerCamelCase__           =		scope
          lowerCamelCase__           =		range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          lowerCamelCase__           =		text_seq_length
          lowerCamelCase__           =		(image_size // patch_size) ** 2 + 1
          lowerCamelCase__           =		self.text_seq_length + self.image_seq_length
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.text_seq_length]							,					self.vocab_size				)

          lowerCamelCase__           =		ids_tensor([self.batch_size, self.text_seq_length, 4]							,					self.range_bbox				)
          lowerCamelCase__           =		bbox.numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]				):
                for j in range(bbox.shape[1]				):
                      if bbox[i, j, 3] < bbox[i, j, 1]:
                            lowerCamelCase__           =		bbox[i, j, 3]
                            lowerCamelCase__           =		bbox[i, j, 1]
                            lowerCamelCase__           =		tmp_coordinate
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                            lowerCamelCase__           =		bbox[i, j, 2]
                            lowerCamelCase__           =		bbox[i, j, 0]
                            lowerCamelCase__           =		tmp_coordinate
          lowerCamelCase__           =		tf.constant(lowercase__				)

          lowerCamelCase__           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          lowerCamelCase__           =		None
          if self.use_input_mask:
                lowerCamelCase__           =		random_attention_mask([self.batch_size, self.text_seq_length]				)

          lowerCamelCase__           =		None
          if self.use_token_type_ids:
                lowerCamelCase__           =		ids_tensor([self.batch_size, self.text_seq_length]							,					self.type_vocab_size				)

          lowerCamelCase__           =		None
          lowerCamelCase__           =		None
          if self.use_labels:
                lowerCamelCase__           =		ids_tensor([self.batch_size]							,					self.type_sequence_label_size				)
                lowerCamelCase__           =		ids_tensor([self.batch_size, self.text_seq_length]							,					self.num_labels				)

          lowerCamelCase__           =		LayoutLMvaConfig(
              vocab_size=self.vocab_size							,					hidden_size=self.hidden_size							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					hidden_act=self.hidden_act							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					type_vocab_size=self.type_vocab_size							,					initializer_range=self.initializer_range							,					coordinate_size=self.coordinate_size							,					shape_size=self.shape_size							,					input_size=self.image_size							,					patch_size=self.patch_size							,					)

          return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		TFLayoutLMvaModel(config=lowercase__				)

          # text + image
          lowerCamelCase__           =		model(lowercase__							,					pixel_values=lowercase__							,					training=lowercase__				)
          lowerCamelCase__           =		model(
              lowercase__							,					bbox=lowercase__							,					pixel_values=lowercase__							,					attention_mask=lowercase__							,					token_type_ids=lowercase__							,					training=lowercase__							,					)
          lowerCamelCase__           =		model(lowercase__							,					bbox=lowercase__							,					pixel_values=lowercase__							,					training=lowercase__				)

          self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)				)

          # text only
          lowerCamelCase__           =		model(lowercase__							,					training=lowercase__				)
          self.parent.assertEqual(
              result.last_hidden_state.shape							,					(self.batch_size, self.text_seq_length, self.hidden_size)				)

          # image only
          lowerCamelCase__           =		model({'pixel_values': pixel_values}							,					training=lowercase__				)
          self.parent.assertEqual(
              result.last_hidden_state.shape							,					(self.batch_size, self.image_seq_length, self.hidden_size)				)
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          lowerCamelCase__           =		self.num_labels
          lowerCamelCase__           =		TFLayoutLMvaForSequenceClassification(config=lowercase__				)
          lowerCamelCase__           =		model(
              lowercase__							,					bbox=lowercase__							,					pixel_values=lowercase__							,					attention_mask=lowercase__							,					token_type_ids=lowercase__							,					labels=lowercase__							,					training=lowercase__							,					)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)				)
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		self.num_labels
          lowerCamelCase__           =		TFLayoutLMvaForTokenClassification(config=lowercase__				)
          lowerCamelCase__           =		model(
              lowercase__							,					bbox=lowercase__							,					pixel_values=lowercase__							,					attention_mask=lowercase__							,					token_type_ids=lowercase__							,					labels=lowercase__							,					training=lowercase__							,					)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.text_seq_length, self.num_labels)				)
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		2
          lowerCamelCase__           =		TFLayoutLMvaForQuestionAnswering(config=lowercase__				)
          lowerCamelCase__           =		model(
              lowercase__							,					bbox=lowercase__							,					pixel_values=lowercase__							,					attention_mask=lowercase__							,					token_type_ids=lowercase__							,					start_positions=lowercase__							,					end_positions=lowercase__							,					training=lowercase__							,					)
          self.parent.assertEqual(result.start_logits.shape							,					(self.batch_size, self.seq_length)				)
          self.parent.assertEqual(result.end_logits.shape							,					(self.batch_size, self.seq_length)				)


    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          (lowerCamelCase__)           =		config_and_inputs
          lowerCamelCase__           =		{
              "input_ids": input_ids,
              "bbox": bbox,
              "pixel_values": pixel_values,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict





@require_tf
class   _a      (      _UpperCAmelCase		,		_UpperCAmelCase		,		unittest.TestCase ):

    '''simple docstring'''





    a_    :				Any             =    (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    a_    :				str             =    (
        {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    a_    :				Union[str, Any]             =    False
    a_    :				str             =    False
    a_    :				Union[str, Any]             =    False
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          return True
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Any=False				):
          lowerCamelCase__           =		copy.deepcopy(lowercase__				)

          if model_class in get_values(lowercase__				):
                lowerCamelCase__           =		{
                    k: tf.tile(tf.expand_dims(lowercase__							,					1				)							,					(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
                    if isinstance(lowercase__							,					tf.Tensor				) and v.ndim > 0
                    else v
                    for k, v in inputs_dict.items()
                }

          if return_labels:
                if model_class in get_values(lowercase__				):
                      lowerCamelCase__           =		tf.ones(self.model_tester.batch_size							,					dtype=tf.intaa				)
                elif model_class in get_values(lowercase__				):
                      lowerCamelCase__           =		tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa				)
                      lowerCamelCase__           =		tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa				)
                elif model_class in get_values(lowercase__				):
                      lowerCamelCase__           =		tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa				)
                elif model_class in get_values(lowercase__				):
                      lowerCamelCase__           =		tf.zeros(
                          (self.model_tester.batch_size, self.model_tester.text_seq_length)							,					dtype=tf.intaa				)

          return inputs_dict
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		TFLayoutLMvaModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=lowercase__							,					hidden_size=37				)
    def      _UpperCamelCase       ( self							:					int				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                lowerCamelCase__           =		model_class(lowercase__				)
                if getattr(lowercase__							,					'hf_compute_loss'							,					lowercase__				):
                      # The number of elements in the loss should be the same as the number of elements in the label
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					lowercase__							,					return_labels=lowercase__				)
                      lowerCamelCase__           =		prepared_for_class[
                          sorted(prepared_for_class.keys() - inputs_dict.keys()							,					reverse=lowercase__				)[0]
                      ]
                      lowerCamelCase__           =		added_label.shape.as_list()[:1]

                      # Test that model correctly compute the loss with kwargs
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					lowercase__							,					return_labels=lowercase__				)
                      lowerCamelCase__           =		prepared_for_class.pop('input_ids'				)

                      lowerCamelCase__           =		model(lowercase__							,					**lowercase__				)[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

                      # Test that model correctly compute the loss when we mask some positions
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					lowercase__							,					return_labels=lowercase__				)
                      lowerCamelCase__           =		prepared_for_class.pop('input_ids'				)
                      if "labels" in prepared_for_class:
                            lowerCamelCase__           =		prepared_for_class["labels"].numpy()
                            if len(labels.shape				) > 1 and labels.shape[1] != 1:
                                  lowerCamelCase__           =		-1_00
                                  lowerCamelCase__           =		tf.convert_to_tensor(lowercase__				)
                                  lowerCamelCase__           =		model(lowercase__							,					**lowercase__				)[0]
                                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)
                                  self.assertTrue(not np.any(np.isnan(loss.numpy()				)				)				)

                # Test that model correctly compute the loss with a dict
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					lowercase__							,					return_labels=lowercase__				)
                      lowerCamelCase__           =		model(lowercase__				)[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

                      # Test that model correctly compute the loss with a tuple
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					lowercase__							,					return_labels=lowercase__				)

                      # Get keys that were added with the _prepare_for_class function
                      lowerCamelCase__           =		prepared_for_class.keys() - inputs_dict.keys()
                      lowerCamelCase__           =		inspect.signature(model.call				).parameters
                      lowerCamelCase__           =		list(signature.keys()				)

                      # Create a dictionary holding the location of the tensors in the tuple
                      lowerCamelCase__           =		{0: "input_ids"}
                      for label_key in label_keys:
                            lowerCamelCase__           =		signature_names.index(lowercase__				)
                            lowerCamelCase__           =		label_key
                      lowerCamelCase__           =		sorted(tuple_index_mapping.items()				)
                      # Initialize a list with their default values, update the values and convert to a tuple
                      lowerCamelCase__           =		[]

                      for name in signature_names:
                            if name != "kwargs":
                                  list_input.append(signature[name].default				)

                      for index, value in sorted_tuple_index_mapping:
                            lowerCamelCase__           =		prepared_for_class[value]

                      lowerCamelCase__           =		tuple(lowercase__				)

                      # Send to model
                      lowerCamelCase__           =		model(tuple_input[:-1]				)[0]

                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          (
              lowerCamelCase__		
          )           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          (
              lowerCamelCase__		
          )           =		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                lowerCamelCase__           =		type
                self.model_tester.create_and_check_model(lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          (
              lowerCamelCase__		
          )           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(
              lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          (
              lowerCamelCase__		
          )           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(
              lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__				)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          (
              lowerCamelCase__		
          )           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(
              lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__				)


    @slow
    def      _UpperCamelCase       ( self							:					List[str]				):






          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__           =		TFLayoutLMvaModel.from_pretrained(lowercase__				)
                self.assertIsNotNone(lowercase__				)





def   snake_case						(				)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image






@require_tf
class   _a      (      unittest.TestCase ):

    '''simple docstring'''





    @cached_property
    def      _UpperCamelCase       ( self							:					Any				):
          return LayoutLMvaImageProcessor(apply_ocr=lowercase__				) if is_vision_available() else None


    @slow
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'				)

          lowerCamelCase__           =		self.default_image_processor
          lowerCamelCase__           =		prepare_img()
          lowerCamelCase__           =		image_processor(images=lowercase__							,					return_tensors='tf'				).pixel_values

          lowerCamelCase__           =		tf.constant([[1, 2]]				)
          lowerCamelCase__           =		tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]				)							,					axis=0				)

          # forward pass
          lowerCamelCase__           =		model(input_ids=lowercase__							,					bbox=lowercase__							,					pixel_values=lowercase__							,					training=lowercase__				)

          # verify the logits
          lowerCamelCase__           =		(1, 1_99, 7_68)
          self.assertEqual(outputs.last_hidden_state.shape							,					lowercase__				)

          lowerCamelCase__           =		tf.constant(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]				)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]							,					lowercase__							,					atol=1e-4				)				)







"""simple docstring"""


def   snake_case						(				_a:     list[list[float]]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      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   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for dlist, weight in zip(_a		,   _a							):
            lowerCamelCase__           =		min(_a							)
            lowerCamelCase__           =		max(_a							)

            lowerCamelCase__           =		[]
            # 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__           =		F'Invalid weight of {weight:f} provided'
                  raise ValueError(_a							)

            score_lists.append(_a							)

      return score_lists



def   snake_case						(				_a:     list[list[float]]							)->		list[float]:
      '''simple docstring'''


      lowerCamelCase__           =		[0 for i in range(len(score_lists[0]							)							)]

      for slist in score_lists:
            for j, ele in enumerate(_a							):
                  lowerCamelCase__           =		final_scores[j] + ele

      return final_scores






def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		get_data(_a							)
      lowerCamelCase__           =		calculate_each_score(_a		,   _a							)
      lowerCamelCase__           =		generate_final_scores(_a							)

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

      return source_data



