


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFRoFormerForCausalLM,
       TFRoFormerForMaskedLM,
       TFRoFormerForMultipleChoice,
       TFRoFormerForQuestionAnswering,
       TFRoFormerForSequenceClassification,
       TFRoFormerForTokenClassification,
       TFRoFormerModel,
   )
   from transformers.models.roformer.modeling_tf_roformer import (
       TFRoFormerSelfAttention,
       TFRoFormerSinusoidalPositionalEmbedding,
   )



class   _a      :
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]=13							,					SCREAMING_SNAKE_CASE__							:					Dict=7							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					Tuple=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					Tuple=99							,					SCREAMING_SNAKE_CASE__							:					str=32							,					SCREAMING_SNAKE_CASE__							:					Tuple=2							,					SCREAMING_SNAKE_CASE__							:					int=4							,					SCREAMING_SNAKE_CASE__							:					Dict=37							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=0.1							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					List[Any]=5_12							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=16							,					SCREAMING_SNAKE_CASE__							:					str=2							,					SCREAMING_SNAKE_CASE__							:					Dict=0.02							,					SCREAMING_SNAKE_CASE__							:					List[str]=3							,					SCREAMING_SNAKE_CASE__							:					int=4							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		13
          lowerCamelCase__           =		7
          lowerCamelCase__           =		True
          lowerCamelCase__           =		True
          lowerCamelCase__           =		True
          lowerCamelCase__           =		True
          lowerCamelCase__           =		99
          lowerCamelCase__           =		32
          lowerCamelCase__           =		2
          lowerCamelCase__           =		4
          lowerCamelCase__           =		37
          lowerCamelCase__           =		'gelu'
          lowerCamelCase__           =		0.1
          lowerCamelCase__           =		0.1
          lowerCamelCase__           =		5_12
          lowerCamelCase__           =		16
          lowerCamelCase__           =		2
          lowerCamelCase__           =		0.02
          lowerCamelCase__           =		3
          lowerCamelCase__           =		4
          lowerCamelCase__           =		None
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size				)

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

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

          lowerCamelCase__           =		None
          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.seq_length]							,					self.num_labels				)
                lowerCamelCase__           =		ids_tensor([self.batch_size]							,					self.num_choices				)

          lowerCamelCase__           =		RoFormerConfig(
              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							,					return_dict=SCREAMING_SNAKE_CASE__							,					)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          lowerCamelCase__           =		TFRoFormerModel(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

          lowerCamelCase__           =		[input_ids, input_mask]
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)

          self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		True
          lowerCamelCase__           =		TFRoFormerForCausalLM(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)['logits']
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape				)							,					[self.batch_size, self.seq_length, self.vocab_size]				)
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          lowerCamelCase__           =		TFRoFormerForMaskedLM(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)				)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          lowerCamelCase__           =		self.num_labels
          lowerCamelCase__           =		TFRoFormerForSequenceClassification(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }

          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)				)
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.num_choices
          lowerCamelCase__           =		TFRoFormerForMultipleChoice(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__							,					1				)							,					(1, self.num_choices, 1)				)
          lowerCamelCase__           =		tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__							,					1				)							,					(1, self.num_choices, 1)				)
          lowerCamelCase__           =		tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__							,					1				)							,					(1, self.num_choices, 1)				)
          lowerCamelCase__           =		{
              'input_ids': multiple_choice_inputs_ids,
              'attention_mask': multiple_choice_input_mask,
              'token_type_ids': multiple_choice_token_type_ids,
          }
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)				)
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		self.num_labels
          lowerCamelCase__           =		TFRoFormerForTokenClassification(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)
          self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)				)
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]				):
          lowerCamelCase__           =		TFRoFormerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }

          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)
          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							:					Union[str, Any]				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  
          )           =		config_and_inputs
          lowerCamelCase__           =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_tf
class   _a      (      SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Tuple             =    (
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    a_    :				Union[str, Any]             =    (
        {
            'feature-extraction': TFRoFormerModel,
            'fill-mask': TFRoFormerForMaskedLM,
            'question-answering': TFRoFormerForQuestionAnswering,
            'text-classification': TFRoFormerForSequenceClassification,
            'text-generation': TFRoFormerForCausalLM,
            'token-classification': TFRoFormerForTokenClassification,
            'zero-shot': TFRoFormerForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    a_    :				Dict             =    False
    a_    :				str             =    False
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

          return False
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		TFRoFormerModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=SCREAMING_SNAKE_CASE__							,					hidden_size=37				)
    def      _UpperCamelCase       ( self							:					Any				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__				)


    @slow
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'				)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)





@require_tf
class   _a      (      unittest.TestCase ):
    @slow
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'				)
          lowerCamelCase__           =		tf.constant([[0, 1, 2, 3, 4, 5]]				)
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__				)[0]

          # TODO Replace vocab size
          lowerCamelCase__           =		5_00_00

          lowerCamelCase__           =		[1, 6, vocab_size]
          self.assertEqual(output.shape							,					SCREAMING_SNAKE_CASE__				)

          print(output[:, :3, :3]				)

          # TODO Replace values below with what was printed above.
          lowerCamelCase__           =		tf.constant(
              [
                  [
                      [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
                      [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
                      [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
                  ]
              ]				)
          tf.debugging.assert_near(output[:, :3, :3]							,					SCREAMING_SNAKE_CASE__							,					atol=1e-4				)





@require_tf
class   _a      (      unittest.TestCase ):
    a_    :				Optional[int]             =    1e-4
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		tf.constant([[4, 10]]				)
          lowerCamelCase__           =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,					embedding_dim=6				)

          lowerCamelCase__           =		emba(input_ids.shape				)
          lowerCamelCase__           =		tf.constant(
              [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]				)

          tf.debugging.assert_near(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					atol=self.tolerance				)


    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		tf.constant(
              [
                  [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
                  [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
                  [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
              ]				)
          lowerCamelCase__           =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12							,					embedding_dim=5_12				)
          emba([2, 16, 5_12]				)
          lowerCamelCase__           =		emba.weight[:3, :5]

          tf.debugging.assert_near(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					atol=self.tolerance				)





@require_tf
class   _a      (      unittest.TestCase ):
    a_    :				Union[str, Any]             =    1e-4
    def      _UpperCamelCase       ( self							:					Any				):
          # 2,12,16,64
          lowerCamelCase__           =		tf.reshape(tf.range(2 * 12 * 16 * 64							,					dtype=tf.floataa				)							,					shape=(2, 12, 16, 64)				) / 1_00

          lowerCamelCase__           =		-tf.reshape(tf.range(2 * 12 * 16 * 64							,					dtype=tf.floataa				)							,					shape=(2, 12, 16, 64)				) / 1_00

          lowerCamelCase__           =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							,					embedding_dim=64				)
          lowerCamelCase__           =		embed_positions([2, 16, 7_68]				)[None, None, :, :]

          lowerCamelCase__    ,  lowerCamelCase__           =		TFRoFormerSelfAttention.apply_rotary_position_embeddings(
              SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		tf.constant(
              [
                  [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
                  [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
                  [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
                  [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
                  [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
                  [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
              ]				)
          lowerCamelCase__           =		tf.constant(
              [
                  [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
                  [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
                  [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
                  [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
                  [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
                  [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
              ]				)

          tf.debugging.assert_near(query_layer[0, 0, :6, :8]							,					SCREAMING_SNAKE_CASE__							,					atol=self.tolerance				)
          tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,					SCREAMING_SNAKE_CASE__							,					atol=self.tolerance				)







"""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



