



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

    if is_vision_available():
        from transformers import MaskaFormerImageProcessor

if is_vision_available():
    from PIL import Image



class 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=10,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=32 * 8,     SCREAMING_SNAKE_CASE_=32 * 8,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=64,     )	->						Optional[int]:
          UpperCamelCase       :    Dict		      =      parent
          UpperCamelCase       :    Any		      =      batch_size
          UpperCamelCase       :    Tuple		      =      is_training
          UpperCamelCase       :    List[str]		      =      use_auxiliary_loss
          UpperCamelCase       :    Tuple		      =      num_queries
          UpperCamelCase       :    Optional[Any]		      =      num_channels
          UpperCamelCase       :    Union[str, Any]		      =      min_size
          UpperCamelCase       :    Any		      =      max_size
          UpperCamelCase       :    Optional[int]		      =      num_labels
          UpperCamelCase       :    Dict		      =      hidden_dim
          UpperCamelCase       :    Any		      =      hidden_dim
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Optional[Any]		      =      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
              SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Dict		      =      torch.ones([self.batch_size, self.min_size, self.max_size],     device=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[str]		      =      (
              torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],     device=SCREAMING_SNAKE_CASE_			) > 0.5
          ).float()
          UpperCamelCase       :    Optional[int]		      =      (torch.rand((self.batch_size, self.num_labels),     device=SCREAMING_SNAKE_CASE_			) > 0.5).long()

          UpperCamelCase       :    int		      =      self.get_config()
          return config, pixel_values, pixel_mask, mask_labels, class_labels
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    Any		      =      MaskaFormerConfig(
              hidden_size=self.hidden_dim,     )
          UpperCamelCase       :    Union[str, Any]		      =      self.num_queries
          UpperCamelCase       :    Any		      =      self.num_labels

          UpperCamelCase       :    Union[str, Any]		      =      [1, 1, 1, 1]
          UpperCamelCase       :    Optional[int]		      =      self.num_channels

          UpperCamelCase       :    Union[str, Any]		      =      64
          UpperCamelCase       :    Optional[int]		      =      128
          UpperCamelCase       :    Optional[Any]		      =      self.hidden_dim
          UpperCamelCase       :    Union[str, Any]		      =      self.hidden_dim
          UpperCamelCase       :    str		      =      self.hidden_dim
          return config
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    int		      =      self.prepare_config_and_inputs()
          UpperCamelCase       :    Optional[int]		      =      {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
          return config, inputs_dict
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          UpperCamelCase       :    List[str]		      =      output.encoder_hidden_states
          UpperCamelCase       :    int		      =      output.pixel_decoder_hidden_states
          UpperCamelCase       :    Tuple		      =      output.transformer_decoder_hidden_states

          self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			),     len(config.backbone_config.depths			)			)
          self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			),     len(config.backbone_config.depths			)			)
          self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_			),     config.decoder_layers			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=False			)	->						List[Any]:
          with torch.no_grad():
             UpperCamelCase       :    Union[str, Any]		      =      MaskaFormerModel(config=SCREAMING_SNAKE_CASE_			)
             model.to(SCREAMING_SNAKE_CASE_			)
             model.eval()

             UpperCamelCase       :    List[str]		      =      model(pixel_values=SCREAMING_SNAKE_CASE_,     pixel_mask=SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Union[str, Any]		      =      model(SCREAMING_SNAKE_CASE_,     output_hidden_states=SCREAMING_SNAKE_CASE_			)

          self.parent.assertEqual(
              output.transformer_decoder_last_hidden_state.shape,     (self.batch_size, self.num_queries, self.hidden_dim),     )
          # let's ensure the other two hidden state exists
          self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
          self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

          if output_hidden_states:
             self.check_output_hidden_state(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						int:
          UpperCamelCase       :    Any		      =      MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_			)
          model.to(SCREAMING_SNAKE_CASE_			)
          model.eval()

          def comm_check_on_output(SCREAMING_SNAKE_CASE_			):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
             self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4),     )
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape,     (self.batch_size, self.num_queries, self.num_labels + 1)			)

          with torch.no_grad():
             UpperCamelCase       :    Union[str, Any]		      =      model(pixel_values=SCREAMING_SNAKE_CASE_,     pixel_mask=SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Optional[Any]		      =      model(SCREAMING_SNAKE_CASE_			)

             comm_check_on_output(SCREAMING_SNAKE_CASE_			)

             UpperCamelCase       :    str		      =      model(
                 pixel_values=SCREAMING_SNAKE_CASE_,     pixel_mask=SCREAMING_SNAKE_CASE_,     mask_labels=SCREAMING_SNAKE_CASE_,     class_labels=SCREAMING_SNAKE_CASE_			)

          comm_check_on_output(SCREAMING_SNAKE_CASE_			)

          self.parent.assertTrue(result.loss is not None			)
          self.parent.assertEqual(result.loss.shape,     torch.Size([1]			)			)






@require_torch
class 							lowerCAmelCase_						(    a__			,       a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   List[str]  	=    (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       UpperCAmelCase__							:   Optional[Any]  	=    {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

       UpperCAmelCase__							:   List[Any]  	=    False
       UpperCAmelCase__							:   List[Any]  	=    False
       UpperCAmelCase__							:   Optional[Any]  	=    False
       UpperCAmelCase__							:   str  	=    False
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    List[str]		      =      MaskaFormerModelTester(self			)
          UpperCamelCase       :    Tuple		      =      ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     has_text_modality=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Any:
          self.config_tester.run_common_tests()
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     output_hidden_states=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Union[str, Any]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_			)
       @unittest.skip(reason='Mask2Former does not use inputs_embeds'			)
       def    snake_case_			(     self			)	->						Dict:
          pass
       @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          pass
       @unittest.skip(reason='Mask2Former is not a generative model'			)
       def    snake_case_			(     self			)	->						List[str]:
          pass
       @unittest.skip(reason='Mask2Former does not use token embeddings'			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          pass
       @require_torch_multi_gpu
       @unittest.skip(
           reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'			)
       def    snake_case_			(     self			)	->						List[str]:
          pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
       def    snake_case_			(     self			)	->						Tuple:
          pass
       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    Optional[int]		      =      model_class(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Union[str, Any]		      =      inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase       :    List[str]		      =      [*signature.parameters.keys()]

             UpperCamelCase       :    Tuple		      =      ['pixel_values']
             self.assertListEqual(arg_names[:1],     SCREAMING_SNAKE_CASE_			)
       @slow
       def    snake_case_			(     self			)	->						List[str]:
          for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             UpperCamelCase       :    List[str]		      =      MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    List[str]		      =      (self.model_tester.min_size,) * 2
          UpperCamelCase       :    Any		      =      {
              'pixel_values': torch.randn((2, 3, *size),     device=SCREAMING_SNAKE_CASE_			),
              'mask_labels': torch.randn((2, 10, *size),     device=SCREAMING_SNAKE_CASE_			),
              'class_labels': torch.zeros(2,     10,     device=SCREAMING_SNAKE_CASE_			).long(),
          }
          UpperCamelCase       :    List[str]		      =      self.model_tester.get_config()

          UpperCamelCase       :    Tuple		      =      MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Any		      =      model(**SCREAMING_SNAKE_CASE_			)
          self.assertTrue(outputs.loss is not None			)
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     output_hidden_states=SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase       :    Union[str, Any]		      =      model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    str		      =      model(**SCREAMING_SNAKE_CASE_,     output_attentions=SCREAMING_SNAKE_CASE_			)
             self.assertTrue(outputs.attentions is not None			)
       def    snake_case_			(     self			)	->						List[Any]:
          if not self.model_tester.is_training:
             return

          UpperCamelCase       :    Optional[Any]		      =      self.all_model_classes[1]
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    Dict		      =      self.model_tester.prepare_config_and_inputs()

          UpperCamelCase       :    Optional[int]		      =      model_class(SCREAMING_SNAKE_CASE_			)
          model.to(SCREAMING_SNAKE_CASE_			)
          model.train()

          UpperCamelCase       :    Any		      =      model(SCREAMING_SNAKE_CASE_,     mask_labels=SCREAMING_SNAKE_CASE_,     class_labels=SCREAMING_SNAKE_CASE_			).loss
          loss.backward()






       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Union[str, Any]		      =      self.all_model_classes[1]
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    List[Any]		      =      self.model_tester.prepare_config_and_inputs()
          UpperCamelCase       :    Union[str, Any]		      =      True
          UpperCamelCase       :    Optional[Any]		      =      True

          UpperCamelCase       :    Union[str, Any]		      =      model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
          model.train()

          UpperCamelCase       :    Dict		      =      model(SCREAMING_SNAKE_CASE_,     mask_labels=SCREAMING_SNAKE_CASE_,     class_labels=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[str]		      =      outputs.encoder_hidden_states[0]
          encoder_hidden_states.retain_grad()

          UpperCamelCase       :    Any		      =      outputs.pixel_decoder_hidden_states[0]
          pixel_decoder_hidden_states.retain_grad()

          UpperCamelCase       :    Tuple		      =      outputs.transformer_decoder_hidden_states[0]
          transformer_decoder_hidden_states.retain_grad()

          UpperCamelCase       :    Union[str, Any]		      =      outputs.attentions[0]
          attentions.retain_grad()

          outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_			)

          self.assertIsNotNone(encoder_hidden_states.grad			)
          self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
          self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
          self.assertIsNotNone(attentions.grad			)






__UpperCAmelCase			  =			1e-4
def 						UpperCamelCase      (  )      ->	Union[str, Any]:
   UpperCamelCase       :    Optional[int]		      =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image



@require_vision
@slow
class 							lowerCAmelCase_						(    unittest.TestCase				):
       @cached_property
       def    snake_case_			(     self			)	->						int:
          return "facebook/mask2former-swin-small-coco-instance"
       @cached_property
       def    snake_case_			(     self			)	->						Any:
          return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    int		      =      MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      self.default_image_processor
          UpperCamelCase       :    Tuple		      =      prepare_img()
          UpperCamelCase       :    Optional[int]		      =      image_processor(SCREAMING_SNAKE_CASE_,     return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      inputs['pixel_values'].shape
          # check size is divisible by 32
          self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
          # check size
          self.assertEqual(SCREAMING_SNAKE_CASE_,     (1, 3, 384, 384)			)

          with torch.no_grad():
             UpperCamelCase       :    Optional[Any]		      =      model(**SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Dict		      =      torch.tensor(
              [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]			).to(SCREAMING_SNAKE_CASE_			)
          self.assertTrue(
              torch.allclose(
                  outputs.encoder_last_hidden_state[0, 0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=SCREAMING_SNAKE_CASE_			)			)

          UpperCamelCase       :    Optional[int]		      =      torch.tensor(
              [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]			).to(SCREAMING_SNAKE_CASE_			)
          self.assertTrue(
              torch.allclose(
                  outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=SCREAMING_SNAKE_CASE_			)			)

          UpperCamelCase       :    Optional[Any]		      =      torch.tensor(
              [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]			).to(SCREAMING_SNAKE_CASE_			)
          self.assertTrue(
              torch.allclose(
                  outputs.transformer_decoder_last_hidden_state[0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=SCREAMING_SNAKE_CASE_			)			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Dict		      =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
          UpperCamelCase       :    str		      =      self.default_image_processor
          UpperCamelCase       :    Optional[Any]		      =      prepare_img()
          UpperCamelCase       :    str		      =      image_processor(SCREAMING_SNAKE_CASE_,     return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      inputs['pixel_values'].shape
          # check size is divisible by 32
          self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
          # check size
          self.assertEqual(SCREAMING_SNAKE_CASE_,     (1, 3, 384, 384)			)

          with torch.no_grad():
             UpperCamelCase       :    List[Any]		      =      model(**SCREAMING_SNAKE_CASE_			)
          # masks_queries_logits
          UpperCamelCase       :    Dict		      =      outputs.masks_queries_logits
          self.assertEqual(
              masks_queries_logits.shape,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
          UpperCamelCase       :    Optional[int]		      =      [
              [-8.78_39, -9.00_56, -8.81_21],
              [-7.41_04, -7.03_13, -6.54_01],
              [-6.61_05, -6.34_27, -6.46_75],
          ]
          UpperCamelCase       :    List[str]		      =      torch.tensor(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
          self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=SCREAMING_SNAKE_CASE_			)			)
          # class_queries_logits
          UpperCamelCase       :    List[Any]		      =      outputs.class_queries_logits
          self.assertEqual(class_queries_logits.shape,     (1, model.config.num_queries, model.config.num_labels + 1)			)
          UpperCamelCase       :    Tuple		      =      torch.tensor(
              [
                  [1.83_24, -8.08_35, -4.19_22],
                  [0.84_50, -9.00_50, -3.60_53],
                  [0.30_45, -7.72_93, -3.02_75],
              ]			).to(SCREAMING_SNAKE_CASE_			)
          self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=SCREAMING_SNAKE_CASE_			)			)






       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Tuple		      =      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
          UpperCamelCase       :    int		      =      self.default_image_processor

          UpperCamelCase       :    List[str]		      =      image_processor(
              [np.zeros((3, 800, 1333)			), np.zeros((3, 800, 1333)			)],     segmentation_maps=[np.zeros((384, 384)			).astype(np.floataa			), np.zeros((384, 384)			).astype(np.floataa			)],     return_tensors='pt',     )

          UpperCamelCase       :    Tuple		      =      inputs['pixel_values'].to(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    str		      =      [el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['mask_labels']]
          UpperCamelCase       :    str		      =      [el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['class_labels']]

          with torch.no_grad():
             UpperCamelCase       :    Any		      =      model(**SCREAMING_SNAKE_CASE_			)

          self.assertTrue(outputs.loss is not None			)








'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    if len(_snake_case							) == 1:
        return True
    lowercase__									=    series[1] - series[0]
    for index in range(len(_snake_case							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True




def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    lowercase__									=    0
    for val in series:
        answer += val
    return answer / len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




