





import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor




class   UpperCamelCase_       (     UpperCamelCase__    ):


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_lowerCAmelCase							,				"""hidden_sizes"""		)		)
									self.parent.assertTrue(hasattr(_lowerCAmelCase							,				"""num_attention_heads"""		)		)
									self.parent.assertTrue(hasattr(_lowerCAmelCase							,				"""num_encoder_blocks"""		)		)




class   UpperCamelCase_       :


							def __init__(							self    :int							,				__A    :int							,				__A    :Optional[int]=13							,				__A    :Union[str, Any]=64							,				__A    :Tuple=3							,				__A    :int=4							,				__A    :List[Any]=[2, 2, 2, 2]							,				__A    :List[str]=[8, 4, 2, 1]							,				__A    :str=[16, 32, 64, 128]							,				__A    :Optional[int]=[1, 4, 8, 16]							,				__A    :Optional[Any]=[1, 2, 4, 8]							,				__A    :List[Any]=True							,				__A    :Tuple=True							,				__A    :Optional[int]="gelu"							,				__A    :Tuple=0.1							,				__A    :int=0.1							,				__A    :Any=0.0_2							,				__A    :Optional[int]=3							,				__A    :Dict=None							,				)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      parent
									SCREAMING_SNAKE_CASE__			=      batch_size
									SCREAMING_SNAKE_CASE__			=      image_size
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      num_encoder_blocks
									SCREAMING_SNAKE_CASE__			=      sr_ratios
									SCREAMING_SNAKE_CASE__			=      depths
									SCREAMING_SNAKE_CASE__			=      hidden_sizes
									SCREAMING_SNAKE_CASE__			=      downsampling_rates
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      is_training
									SCREAMING_SNAKE_CASE__			=      use_labels
									SCREAMING_SNAKE_CASE__			=      hidden_act
									SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
									SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__			=      initializer_range
									SCREAMING_SNAKE_CASE__			=      num_labels
									SCREAMING_SNAKE_CASE__			=      scope


							def      _snake_case  (							self    :Any		)   ->   List[Any]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      None
									if self.use_labels:
											SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size, self.image_size, self.image_size]							,				self.num_labels		)

									SCREAMING_SNAKE_CASE__			=      self.get_config()
									return config, pixel_values, labels


							def      _snake_case  (							self    :List[str]		)   ->   int:



									"""simple docstring"""



									return SegformerConfig(
									    image_size=self.image_size							,				num_channels=self.num_channels							,				num_encoder_blocks=self.num_encoder_blocks							,				depths=self.depths							,				hidden_sizes=self.hidden_sizes							,				num_attention_heads=self.num_attention_heads							,				hidden_act=self.hidden_act							,				hidden_dropout_prob=self.hidden_dropout_prob							,				attention_probs_dropout_prob=self.attention_probs_dropout_prob							,				initializer_range=self.initializer_range							,				)


							def      _snake_case  (							self    :Tuple							,				__A    :Optional[int]							,				__A    :Any							,				__A    :str		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      SegformerModel(config=_lowerCAmelCase		)
									model.to(_lowerCAmelCase		)
									model.eval()
									SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase		)
									SCREAMING_SNAKE_CASE__			=      SCREAMING_SNAKE_CASE__			=      self.image_size // (self.downsampling_rates[-1] * 2)
									self.parent.assertEqual(
									    result.last_hidden_state.shape							,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)


							def      _snake_case  (							self    :List[str]							,				__A    :Dict							,				__A    :Union[str, Any]							,				__A    :Any		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.num_labels
									SCREAMING_SNAKE_CASE__			=      SegformerForSemanticSegmentation(_lowerCAmelCase		)
									model.to(_lowerCAmelCase		)
									model.eval()
									SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape							,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
									SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase							,				labels=_lowerCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape							,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
									self.parent.assertGreater(result.loss							,				0.0		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :Optional[Any]							,				__A    :List[Any]							,				__A    :Any		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      SegformerForSemanticSegmentation(config=_lowerCAmelCase		)
									model.to(_lowerCAmelCase		)
									model.eval()
									SCREAMING_SNAKE_CASE__			=      torch.randint(0							,				1							,				(self.batch_size, self.image_size, self.image_size)		).to(_lowerCAmelCase		)
									SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase							,				labels=_lowerCAmelCase		)
									self.parent.assertGreater(result.loss							,				0.0		)


							def      _snake_case  (							self    :Tuple		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      config_and_inputs
									SCREAMING_SNAKE_CASE__			=      {"""pixel_values""": pixel_values}
									return config, inputs_dict




@require_torch
class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =   (
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase_           =   (
							    {
							        "feature-extraction": SegformerModel,
							        "image-classification": SegformerForImageClassification,
							        "image-segmentation": SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCamelCase_           =   True
							lowerCamelCase_           =   False
							lowerCamelCase_           =   False
							lowerCamelCase_           =   False


							def      _snake_case  (							self    :Dict		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      SegformerModelTester(self		)
									SCREAMING_SNAKE_CASE__			=      SegformerConfigTester(self							,				config_class=_lowerCAmelCase		)


							def      _snake_case  (							self    :List[str]		)   ->   str:



									"""simple docstring"""



									self.config_tester.run_common_tests()


							def      _snake_case  (							self    :int		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowerCAmelCase		)


							def      _snake_case  (							self    :int		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_binary_image_segmentation(*_lowerCAmelCase		)


							def      _snake_case  (							self    :List[str]		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*_lowerCAmelCase		)


							@unittest.skip("""SegFormer does not use inputs_embeds"""		)
							def      _snake_case  (							self    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									pass


							@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""		)
							def      _snake_case  (							self    :Optional[Any]		)   ->   int:



									"""simple docstring"""



									pass


							def      _snake_case  (							self    :List[str]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__			=      model_class(_lowerCAmelCase		)
											SCREAMING_SNAKE_CASE__			=      inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											SCREAMING_SNAKE_CASE__			=      [*signature.parameters.keys()]

											SCREAMING_SNAKE_CASE__			=      ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,				_lowerCAmelCase		)


							def      _snake_case  (							self    :List[Any]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__			=      True

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__			=      True
											SCREAMING_SNAKE_CASE__			=      False
											SCREAMING_SNAKE_CASE__			=      True
											SCREAMING_SNAKE_CASE__			=      model_class(_lowerCAmelCase		)
											model.to(_lowerCAmelCase		)
											model.eval()
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__			=      model(**self._prepare_for_class(_lowerCAmelCase							,				_lowerCAmelCase		)		)
											SCREAMING_SNAKE_CASE__			=      outputs.attentions

											SCREAMING_SNAKE_CASE__			=      sum(self.model_tester.depths		)
											self.assertEqual(len(_lowerCAmelCase		)							,				_lowerCAmelCase		)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											SCREAMING_SNAKE_CASE__			=      True
											SCREAMING_SNAKE_CASE__			=      model_class(_lowerCAmelCase		)
											model.to(_lowerCAmelCase		)
											model.eval()
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__			=      model(**self._prepare_for_class(_lowerCAmelCase							,				_lowerCAmelCase		)		)
											SCREAMING_SNAKE_CASE__			=      outputs.attentions

											self.assertEqual(len(_lowerCAmelCase		)							,				_lowerCAmelCase		)

											# verify the first attentions (first block, first layer)
											SCREAMING_SNAKE_CASE__			=      (self.model_tester.image_size // 4) ** 2
											SCREAMING_SNAKE_CASE__			=      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
											self.assertListEqual(
											    list(attentions[0].shape[-3:]		)							,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							,				)

											# verify the last attentions (last block, last layer)
											SCREAMING_SNAKE_CASE__			=      (self.model_tester.image_size // 32) ** 2
											SCREAMING_SNAKE_CASE__			=      (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
											self.assertListEqual(
											    list(attentions[-1].shape[-3:]		)							,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]							,				)
											SCREAMING_SNAKE_CASE__			=      len(_lowerCAmelCase		)

											# Check attention is always last and order is fine
											SCREAMING_SNAKE_CASE__			=      True
											SCREAMING_SNAKE_CASE__			=      True
											SCREAMING_SNAKE_CASE__			=      model_class(_lowerCAmelCase		)
											model.to(_lowerCAmelCase		)
											model.eval()
											with torch.no_grad():
													SCREAMING_SNAKE_CASE__			=      model(**self._prepare_for_class(_lowerCAmelCase							,				_lowerCAmelCase		)		)

											self.assertEqual(out_len + 1							,				len(_lowerCAmelCase		)		)

											SCREAMING_SNAKE_CASE__			=      outputs.attentions

											self.assertEqual(len(_lowerCAmelCase		)							,				_lowerCAmelCase		)
											# verify the first attentions (first block, first layer)
											SCREAMING_SNAKE_CASE__			=      (self.model_tester.image_size // 4) ** 2
											SCREAMING_SNAKE_CASE__			=      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]		)							,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]							,				)


							def      _snake_case  (							self    :int		)   ->   Any:



									"""simple docstring"""



									def check_hidden_states_output(__A    :int							,				__A    :Optional[int]							,				__A    :List[str]		):
											SCREAMING_SNAKE_CASE__			=      model_class(_lowerCAmelCase		)
											model.to(_lowerCAmelCase		)
											model.eval()

											with torch.no_grad():
													SCREAMING_SNAKE_CASE__			=      model(**self._prepare_for_class(_lowerCAmelCase							,				_lowerCAmelCase		)		)

											SCREAMING_SNAKE_CASE__			=      outputs.hidden_states

											SCREAMING_SNAKE_CASE__			=      self.model_tester.num_encoder_blocks
											self.assertEqual(len(_lowerCAmelCase		)							,				_lowerCAmelCase		)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]		)							,				[
											        self.model_tester.hidden_sizes[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]							,				)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__			=      True
											check_hidden_states_output(_lowerCAmelCase							,				_lowerCAmelCase							,				_lowerCAmelCase		)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											SCREAMING_SNAKE_CASE__			=      True

											check_hidden_states_output(_lowerCAmelCase							,				_lowerCAmelCase							,				_lowerCAmelCase		)


							def      _snake_case  (							self    :Any		)   ->   Dict:



									"""simple docstring"""



									if not self.model_tester.is_training:
											return

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE__			=      True

									for model_class in self.all_model_classes:
											if model_class in get_values(_lowerCAmelCase		):
													continue

											SCREAMING_SNAKE_CASE__			=      model_class(_lowerCAmelCase		)
											model.to(_lowerCAmelCase		)
											model.train()
											SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(_lowerCAmelCase							,				_lowerCAmelCase							,				return_labels=_lowerCAmelCase		)
											SCREAMING_SNAKE_CASE__			=      model(**_lowerCAmelCase		).loss
											loss.backward()


							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
							def      _snake_case  (							self    :Any		)   ->   Union[str, Any]:



									"""simple docstring"""



									pass


							@slow
							def      _snake_case  (							self    :int		)   ->   int:



									"""simple docstring"""



									for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__			=      SegformerModel.from_pretrained(_lowerCAmelCase		)
											self.assertIsNotNone(_lowerCAmelCase		)






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
		return image




@require_torch
class   UpperCamelCase_       (     unittest.TestCase    ):


							@slow
							def      _snake_case  (							self    :Any		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      SegformerImageProcessor(
									    image_scale=(512, 512)							,				keep_ratio=_lowerCAmelCase							,				align=_lowerCAmelCase							,				do_random_crop=_lowerCAmelCase		)
									SCREAMING_SNAKE_CASE__			=      SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""		).to(
									    _lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      prepare_img()
									SCREAMING_SNAKE_CASE__			=      image_processor(images=_lowerCAmelCase							,				return_tensors="""pt"""		)
									SCREAMING_SNAKE_CASE__			=      encoded_inputs.pixel_values.to(_lowerCAmelCase		)

									with torch.no_grad():
											SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      torch.Size((1, model.config.num_labels, 128, 128)		)
									self.assertEqual(outputs.logits.shape							,				_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      torch.tensor(
									    [
									        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
									        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
									        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
									    ]		).to(_lowerCAmelCase		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]							,				_lowerCAmelCase							,				atol=1E-4		)		)


							@slow
							def      _snake_case  (							self    :Dict		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      SegformerImageProcessor(
									    image_scale=(512, 512)							,				keep_ratio=_lowerCAmelCase							,				align=_lowerCAmelCase							,				do_random_crop=_lowerCAmelCase		)
									SCREAMING_SNAKE_CASE__			=      SegformerForSemanticSegmentation.from_pretrained(
									    """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""		).to(_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      prepare_img()
									SCREAMING_SNAKE_CASE__			=      image_processor(images=_lowerCAmelCase							,				return_tensors="""pt"""		)
									SCREAMING_SNAKE_CASE__			=      encoded_inputs.pixel_values.to(_lowerCAmelCase		)

									with torch.no_grad():
											SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      torch.Size((1, model.config.num_labels, 128, 128)		)
									self.assertEqual(outputs.logits.shape							,				_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      torch.tensor(
									    [
									        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
									        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
									        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
									    ]		).to(_lowerCAmelCase		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]							,				_lowerCAmelCase							,				atol=1E-1		)		)


							@slow
							def      _snake_case  (							self    :List[Any]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      SegformerImageProcessor(
									    image_scale=(512, 512)							,				keep_ratio=_lowerCAmelCase							,				align=_lowerCAmelCase							,				do_random_crop=_lowerCAmelCase		)
									SCREAMING_SNAKE_CASE__			=      SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""		).to(
									    _lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      prepare_img()
									SCREAMING_SNAKE_CASE__			=      image_processor(images=_lowerCAmelCase							,				return_tensors="""pt"""		)
									SCREAMING_SNAKE_CASE__			=      encoded_inputs.pixel_values.to(_lowerCAmelCase		)

									with torch.no_grad():
											SCREAMING_SNAKE_CASE__			=      model(_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      outputs.logits.detach().cpu()

									SCREAMING_SNAKE_CASE__			=      image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase							,				target_sizes=[(500, 300)]		)
									SCREAMING_SNAKE_CASE__			=      torch.Size((500, 300)		)
									self.assertEqual(segmentation[0].shape							,				_lowerCAmelCase		)

									SCREAMING_SNAKE_CASE__			=      image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase		)
									SCREAMING_SNAKE_CASE__			=      torch.Size((128, 128)		)
									self.assertEqual(segmentation[0].shape							,				_lowerCAmelCase		)






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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for dlist, weight in zip(UpperCamelCase__					,   UpperCamelCase__    ):
				SCREAMING_SNAKE_CASE__			=      min(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      max(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      []
				# 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:
						SCREAMING_SNAKE_CASE__			=      f'''Invalid weight of {weight:f} provided'''
						raise ValueError(UpperCamelCase__    )

				score_lists.append(UpperCamelCase__    )

		return score_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      [0 for i in range(len(score_lists[0]    )    )]

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

		return final_scores






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      get_data(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      calculate_each_score(UpperCamelCase__					,   UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      generate_final_scores(UpperCamelCase__    )

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

		return source_data