code: 
  
  
  
  
  
  '''simple docstring'''
  
  import unittest
  
  from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
        import torch
  
        from transformers import (
            MODEL_FOR_PRETRAINING_MAPPING,
            FunnelBaseModel,
            FunnelForMaskedLM,
            FunnelForMultipleChoice,
            FunnelForPreTraining,
            FunnelForQuestionAnswering,
            FunnelForSequenceClassification,
            FunnelForTokenClassification,
            FunnelModel,
        )
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=13				,	A=7				,	A=True				,	A=True				,	A=True				,	A=True				,	A=99				,	A=[1, 1, 2]				,	A=1				,	A=32				,	A=4				,	A=8				,	A=37				,	A="gelu_new"				,	A=0.1				,	A=0.1				,	A=0.0				,	A=512				,	A=3				,	A=0.02				,	A=3				,	A=4				,	A=None				,	A=False				,	)	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    parent
          _SCREAMING_SNAKE_CASE             =    batch_size
          _SCREAMING_SNAKE_CASE             =    seq_length
          _SCREAMING_SNAKE_CASE             =    is_training
          _SCREAMING_SNAKE_CASE             =    use_input_mask
          _SCREAMING_SNAKE_CASE             =    use_token_type_ids
          _SCREAMING_SNAKE_CASE             =    use_labels
          _SCREAMING_SNAKE_CASE             =    vocab_size
          _SCREAMING_SNAKE_CASE             =    block_sizes
          _SCREAMING_SNAKE_CASE             =    num_decoder_layers
          _SCREAMING_SNAKE_CASE             =    d_model
          _SCREAMING_SNAKE_CASE             =    n_head
          _SCREAMING_SNAKE_CASE             =    d_head
          _SCREAMING_SNAKE_CASE             =    d_inner
          _SCREAMING_SNAKE_CASE             =    hidden_act
          _SCREAMING_SNAKE_CASE             =    hidden_dropout
          _SCREAMING_SNAKE_CASE             =    attention_dropout
          _SCREAMING_SNAKE_CASE             =    activation_dropout
          _SCREAMING_SNAKE_CASE             =    max_position_embeddings
          _SCREAMING_SNAKE_CASE             =    type_vocab_size
          _SCREAMING_SNAKE_CASE             =    2
          _SCREAMING_SNAKE_CASE             =    num_labels
          _SCREAMING_SNAKE_CASE             =    num_choices
          _SCREAMING_SNAKE_CASE             =    scope
          _SCREAMING_SNAKE_CASE             =    initializer_std
  
          # Used in the tests to check the size of the first attention layer
          _SCREAMING_SNAKE_CASE             =    n_head
          # Used in the tests to check the size of the first hidden state
          _SCREAMING_SNAKE_CASE             =    self.d_model
          # Used in the tests to check the number of output hidden states/attentions
          _SCREAMING_SNAKE_CASE             =    sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
          # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
          # the last hidden state of the first block (which is the first hidden state of the decoder).
          if not base:
              _SCREAMING_SNAKE_CASE             =    self.num_hidden_layers + 2
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_input_mask:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	vocab_size=2 )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_token_type_ids:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.type_vocab_size )
  
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_labels:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size]				,	self.type_sequence_label_size )
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels )
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size]				,	self.num_choices )
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.seq_length]				,	1 )
  
          _SCREAMING_SNAKE_CASE             =    self.get_config()
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              fake_token_labels,
          )
  
  
      def     snake_case_(	self )	->     List[str]:
          return FunnelConfig(
              vocab_size=self.vocab_size				,	block_sizes=self.block_sizes				,	num_decoder_layers=self.num_decoder_layers				,	d_model=self.d_model				,	n_head=self.n_head				,	d_head=self.d_head				,	d_inner=self.d_inner				,	hidden_act=self.hidden_act				,	hidden_dropout=self.hidden_dropout				,	attention_dropout=self.attention_dropout				,	activation_dropout=self.activation_dropout				,	max_position_embeddings=self.max_position_embeddings				,	type_vocab_size=self.type_vocab_size				,	initializer_std=self.initializer_std				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    FunnelModel(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A )
          _SCREAMING_SNAKE_CASE             =    model(A				,	token_type_ids=A )
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.d_model) )
  
          _SCREAMING_SNAKE_CASE             =    False
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.d_model) )
  
          _SCREAMING_SNAKE_CASE             =    False
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.d_model) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    FunnelBaseModel(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A )
          _SCREAMING_SNAKE_CASE             =    model(A				,	token_type_ids=A )
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, 2, self.d_model) )
  
          _SCREAMING_SNAKE_CASE             =    False
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, 3, self.d_model) )
  
          _SCREAMING_SNAKE_CASE             =    False
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, 2, self.d_model) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    FunnelForPreTraining(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A				,	labels=A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    FunnelForMaskedLM(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A				,	labels=A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    FunnelForSequenceClassification(A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A				,	labels=A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self.num_choices
          _SCREAMING_SNAKE_CASE             =    FunnelForMultipleChoice(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    input_ids.unsqueeze(1 ).expand(-1				,	self.num_choices				,	-1 ).contiguous()
          _SCREAMING_SNAKE_CASE             =    token_type_ids.unsqueeze(1 ).expand(-1				,	self.num_choices				,	-1 ).contiguous()
          _SCREAMING_SNAKE_CASE             =    input_mask.unsqueeze(1 ).expand(-1				,	self.num_choices				,	-1 ).contiguous()
          _SCREAMING_SNAKE_CASE             =    model(
              A				,	attention_mask=A				,	token_type_ids=A				,	labels=A				,	)
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    FunnelForTokenClassification(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A				,	labels=A )
          self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels) )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	A				,	)	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    FunnelForQuestionAnswering(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(
              A				,	attention_mask=A				,	token_type_ids=A				,	start_positions=A				,	end_positions=A				,	)
          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     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
          (
              (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , (
              _SCREAMING_SNAKE_CASE
          )       , 
          )             =    config_and_inputs
          _SCREAMING_SNAKE_CASE             =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				(
          (
              FunnelModel,
              FunnelForMaskedLM,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase									=				(
          {
              '''feature-extraction''': (FunnelBaseModel, FunnelModel),
              '''fill-mask''': FunnelForMaskedLM,
              '''question-answering''': FunnelForQuestionAnswering,
              '''text-classification''': FunnelForSequenceClassification,
              '''token-classification''': FunnelForTokenClassification,
              '''zero-shot''': FunnelForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
  
  
      def     snake_case_(	self				,	A				,	A				,	A=False )	->     int:
          _SCREAMING_SNAKE_CASE             =    super()._prepare_for_class(A				,	A				,	return_labels=A )
  
          if return_labels:
              if model_class in get_values(A ):
                  _SCREAMING_SNAKE_CASE             =    torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)				,	dtype=torch.long				,	device=A )
          return inputs_dict
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    FunnelModelTester(self )
          _SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=A )
  
  
      def     snake_case_(	self )	->     List[str]:
          self.config_tester.run_common_tests()
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*A )
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A )
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     Tuple:
          if hasattr(A				,	"""weight""" ) and module.weight is not None:
              module.weight.data.fill_(3 )
          if hasattr(A				,	"""bias""" ) and module.bias is not None:
              module.bias.data.fill_(3 )
  
          for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
              if hasattr(A				,	A ) and getattr(A				,	A ) is not None:
                  _SCREAMING_SNAKE_CASE             =    getattr(A				,	A )
                  weight.data.fill_(3 )
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				(
          (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
      )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    FunnelModelTester(self				,	base=A )
          _SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=A )
  
  
      def     snake_case_(	self )	->     str:
          self.config_tester.run_common_tests()
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_base_model(*A )
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A )
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*A )
  
  
      def     snake_case_(	self )	->     Any:
          _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.__name__ == "FunnelBaseModel":
                  continue
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.train()
              _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(A				,	A				,	return_labels=A )
              _SCREAMING_SNAKE_CASE             =    model(**A ).loss
              loss.backward()
  
  
  
  
  
      def     snake_case_(	self				,	A )	->     Dict:
          if hasattr(A				,	"""weight""" ) and module.weight is not None:
              module.weight.data.fill_(3 )
          if hasattr(A				,	"""bias""" ) and module.bias is not None:
              module.bias.data.fill_(3 )
  
          for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
              if hasattr(A				,	A ) and getattr(A				,	A ) is not None:
                  _SCREAMING_SNAKE_CASE             =    getattr(A				,	A )
                  weight.data.fill_(3 )
  
  
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    13
          _SCREAMING_SNAKE_CASE             =    7
          _SCREAMING_SNAKE_CASE             =    torch.arange(0				,	batch_size * sequence_length ).long().reshape(A				,	A )
          _SCREAMING_SNAKE_CASE             =    [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
          _SCREAMING_SNAKE_CASE             =    torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths] )
  
          _SCREAMING_SNAKE_CASE             =    FunnelModel.from_pretrained("""sgugger/funnel-random-tiny""" )
          _SCREAMING_SNAKE_CASE             =    model(A				,	token_type_ids=A )[0].abs()
  
          _SCREAMING_SNAKE_CASE             =    torch.tensor(2344.8352 )
          _SCREAMING_SNAKE_CASE             =    torch.tensor(0.8052 )
          self.assertTrue(torch.allclose(output.sum()				,	A				,	atol=1e-4 ) )
          self.assertTrue(torch.allclose(output.mean()				,	A				,	atol=1e-4 ) )
  
          _SCREAMING_SNAKE_CASE             =    torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]] )
          _SCREAMING_SNAKE_CASE             =    model(A				,	attention_mask=A				,	token_type_ids=A )[0].abs()
  
          _SCREAMING_SNAKE_CASE             =    torch.tensor(2343.8425 )
          _SCREAMING_SNAKE_CASE             =    torch.tensor(0.8049 )
          self.assertTrue(torch.allclose(output.sum()				,	A				,	atol=1e-4 ) )
          self.assertTrue(torch.allclose(output.mean()				,	A				,	atol=1e-4 ) )
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    FunnelTokenizer.from_pretrained("""huggingface/funnel-small""" )
          _SCREAMING_SNAKE_CASE             =    FunnelModel.from_pretrained("""huggingface/funnel-small""" )
          _SCREAMING_SNAKE_CASE             =    tokenizer("""Hello! I am the Funnel Transformer model."""				,	return_tensors="""pt""" )
          _SCREAMING_SNAKE_CASE             =    model(**A )[0]
  
          _SCREAMING_SNAKE_CASE             =    torch.tensor(235.7246 )
          _SCREAMING_SNAKE_CASE             =    torch.tensor(0.0256 )
          self.assertTrue(torch.allclose(output.sum()				,	A				,	atol=1e-4 ) )
          self.assertTrue(torch.allclose(output.mean()				,	A				,	atol=1e-4 ) )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					float      , __lowerCamelCase				:					float      , __lowerCamelCase				:					float					)		->list[int]:
      if hue < 0 or hue > 360:
          raise Exception("""hue should be between 0 and 360"""					)
  
      if saturation < 0 or saturation > 1:
          raise Exception("""saturation should be between 0 and 1"""					)
  
      if value < 0 or value > 1:
          raise Exception("""value should be between 0 and 1"""					)
  
      _SCREAMING_SNAKE_CASE             =    value * saturation
      _SCREAMING_SNAKE_CASE             =    hue / 60
      _SCREAMING_SNAKE_CASE             =    chroma * (1 - abs(hue_section % 2 - 1					))
      _SCREAMING_SNAKE_CASE             =    value - chroma
  
      if hue_section >= 0 and hue_section <= 1:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 1 and hue_section <= 2:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 2 and hue_section <= 3:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
      elif hue_section > 3 and hue_section <= 4:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      elif hue_section > 4 and hue_section <= 5:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      else:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
  
      return [red, green, blue]
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int					)		->list[float]:
      if red < 0 or red > 255:
          raise Exception("""red should be between 0 and 255"""					)
  
      if green < 0 or green > 255:
          raise Exception("""green should be between 0 and 255"""					)
  
      if blue < 0 or blue > 255:
          raise Exception("""blue should be between 0 and 255"""					)
  
      _SCREAMING_SNAKE_CASE             =    red / 255
      _SCREAMING_SNAKE_CASE             =    green / 255
      _SCREAMING_SNAKE_CASE             =    blue / 255
      _SCREAMING_SNAKE_CASE             =    max(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    value - min(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    0 if value == 0 else chroma / value
  
      if chroma == 0:
          _SCREAMING_SNAKE_CASE             =    0.0
      elif value == float_red:
          _SCREAMING_SNAKE_CASE             =    60 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
          _SCREAMING_SNAKE_CASE             =    60 * (2 + (float_blue - float_red) / chroma)
      else:
          _SCREAMING_SNAKE_CASE             =    60 * (4 + (float_red - float_green) / chroma)
  
      _SCREAMING_SNAKE_CASE             =    (hue + 360) % 360
  
      return [hue, saturation, value]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					list[float]      , __lowerCamelCase				:					list[float]					)		->bool:
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[0] - hsv_a[0]					) < 0.2
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[1] - hsv_a[1]					) < 0.002
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[2] - hsv_a[2]					) < 0.002
  
      return check_hue and check_saturation and check_value
  
  
  
  