code: 
  import copy
  import inspect
  import unittest
  
  import numpy as np
  from huggingface_hub import hf_hub_download
  
  from transformers import VivitConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  
  
  if is_torch_available():
  							import torch
  							from torch import nn
  
  							from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
  							from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  							from transformers import VivitImageProcessor
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:Optional[Any]      ,    lowercase			:Tuple=2      ,    lowercase			:List[str]=True      ,    lowercase			:Dict=True      ,    lowercase			:Dict=1_0      ,    lowercase			:List[Any]=1_0      ,    lowercase			:Optional[int]=8      ,    lowercase			:Tuple=[2, 4, 4]      ,    lowercase			:List[Any]=3      ,    lowercase			:Optional[int]=7_6_8      ,    lowercase			:Any=5      ,    lowercase			:str=4      ,    lowercase			:Optional[Any]=3_7      ,    lowercase			:int="gelu_fast"      ,    lowercase			:Tuple=0.0      ,    lowercase			:Union[str, Any]=0.0      ,    lowercase			:Tuple=0.02      ,    lowercase			:Optional[Any]=1e-06      ,    lowercase			:List[str]=True      ,    lowercase			:int=None      ,    )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   parent
  				SCREAMING_SNAKE_CASE    	=   batch_size
  				SCREAMING_SNAKE_CASE    	=   is_training
  				SCREAMING_SNAKE_CASE    	=   use_labels
  				SCREAMING_SNAKE_CASE    	=   num_labels
  				SCREAMING_SNAKE_CASE    	=   image_size
  				SCREAMING_SNAKE_CASE    	=   num_frames
  				SCREAMING_SNAKE_CASE    	=   tubelet_size
  				SCREAMING_SNAKE_CASE    	=   num_channels
  				SCREAMING_SNAKE_CASE    	=   hidden_size
  				SCREAMING_SNAKE_CASE    	=   num_hidden_layers
  				SCREAMING_SNAKE_CASE    	=   num_attention_heads
  				SCREAMING_SNAKE_CASE    	=   intermediate_size
  				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    	=   layer_norm_eps
  				SCREAMING_SNAKE_CASE    	=   qkv_bias
  				SCREAMING_SNAKE_CASE    	=   scope
  
  				SCREAMING_SNAKE_CASE    	=   (
  				    (self.image_size // self.tubelet_size[2])
  				    * (self.image_size // self.tubelet_size[1])
  				    * (self.num_frames // self.tubelet_size[0])
  				) + 1  # CLS token
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   floats_tensor(
  				    [self.batch_size, self.num_frames, 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.num_labels )
  
  				SCREAMING_SNAKE_CASE    	=   self.get_config()
  
  				return config, pixel_values, labels
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   VivitConfig(
  				    num_frames=self.num_frames      ,    image_size=self.image_size      ,    tubelet_size=self.tubelet_size      ,    num_channels=self.num_channels      ,    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      ,    initializer_range=self.initializer_range      ,    layer_norm_eps=self.layer_norm_eps      ,    qkv_bias=self.qkv_bias      ,    )
  				SCREAMING_SNAKE_CASE    	=   self.num_labels
  				return config
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:Dict      ,    lowercase			:Dict      ,    lowercase			:Optional[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   VivitModel(config=lowercase )
  				model.to(lowercase )
  				model.eval()
  				SCREAMING_SNAKE_CASE    	=   model(lowercase )
  				self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:str )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   VivitForVideoClassification(lowercase )
  				model.to(lowercase )
  				model.eval()
  
  				SCREAMING_SNAKE_CASE    	=   model(lowercase )
  
  				# verify the logits shape
  				SCREAMING_SNAKE_CASE    	=   torch.Size((self.batch_size, self.num_labels) )
  				self.parent.assertEqual(result.logits.shape      ,    lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.prepare_config_and_inputs()
  				SCREAMING_SNAKE_CASE    	=   config_and_inputs
  				SCREAMING_SNAKE_CASE    	=   {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 						lowerCamelCase			(					A__   ,		unittest.TestCase				):
  	UpperCamelCase_     :				List[str]								=  (VivitModel, VivitForVideoClassification) if is_torch_available() else ()
  
  	UpperCamelCase_     :				List[Any]								=  False
  	UpperCamelCase_     :				Optional[Any]								=  False
  	UpperCamelCase_     :				Dict								=  False
  	UpperCamelCase_     :				Dict								=  False
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int] )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   VivitModelTester(self )
  				SCREAMING_SNAKE_CASE    	=   ConfigTester(self      ,    config_class=lowercase      ,    has_text_modality=lowercase      ,    hidden_size=3_7 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:Any      ,    lowercase			:Any      ,    lowercase			:Optional[Any]=False )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   copy.deepcopy(lowercase )
  
  				if return_labels:
  							if model_class in get_values(lowercase ):
  										SCREAMING_SNAKE_CASE    	=   torch.zeros(
  										    self.model_tester.batch_size      ,    dtype=torch.long      ,    device=lowercase )
  
  				return inputs_dict
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  	@unittest.skip(reason='''Vivit does not use inputs_embeds''' )
  	def    snake_case__    (    self			:List[Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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(lowercase )
  							self.assertIsInstance(model.get_input_embeddings()      ,    (nn.Module) )
  							SCREAMING_SNAKE_CASE    	=   model.get_output_embeddings()
  							self.assertTrue(x is None or isinstance(lowercase      ,    nn.Linear ) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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(lowercase )
  							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", "head_mask"]
  							self.assertListEqual(arg_names[:2]      ,    lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_video_classification(*lowercase )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:int )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							SCREAMING_SNAKE_CASE    	=   VivitModel.from_pretrained(lowercase )
  							self.assertIsNotNone(lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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    	=   self.model_tester.seq_length
  
  							SCREAMING_SNAKE_CASE    	=   True
  							SCREAMING_SNAKE_CASE    	=   False
  							SCREAMING_SNAKE_CASE    	=   True
  							SCREAMING_SNAKE_CASE    	=   model_class(lowercase )
  							model.to(lowercase )
  							model.eval()
  							with torch.no_grad():
  										SCREAMING_SNAKE_CASE    	=   model(**self._prepare_for_class(lowercase      ,    lowercase ) )
  							SCREAMING_SNAKE_CASE    	=   outputs.attentions
  							self.assertEqual(len(lowercase )      ,    self.model_tester.num_hidden_layers )
  
  							# check that output_attentions also work using config
  							del inputs_dict["output_attentions"]
  							SCREAMING_SNAKE_CASE    	=   True
  							SCREAMING_SNAKE_CASE    	=   model_class(lowercase )
  							model.to(lowercase )
  							model.eval()
  							with torch.no_grad():
  										SCREAMING_SNAKE_CASE    	=   model(**self._prepare_for_class(lowercase      ,    lowercase ) )
  							SCREAMING_SNAKE_CASE    	=   outputs.attentions
  							self.assertEqual(len(lowercase )      ,    self.model_tester.num_hidden_layers )
  
  							self.assertListEqual(
  							    list(attentions[0].shape[-3:] )      ,    [self.model_tester.num_attention_heads, seq_len, seq_len]      ,    )
  							SCREAMING_SNAKE_CASE    	=   len(lowercase )
  
  							# Check attention is always last and order is fine
  							SCREAMING_SNAKE_CASE    	=   True
  							SCREAMING_SNAKE_CASE    	=   True
  							SCREAMING_SNAKE_CASE    	=   model_class(lowercase )
  							model.to(lowercase )
  							model.eval()
  							with torch.no_grad():
  										SCREAMING_SNAKE_CASE    	=   model(**self._prepare_for_class(lowercase      ,    lowercase ) )
  
  							self.assertEqual(out_len + 1      ,    len(lowercase ) )
  
  							SCREAMING_SNAKE_CASE    	=   outputs.attentions
  
  							self.assertEqual(len(lowercase )      ,    self.model_tester.num_hidden_layers )
  
  							self.assertListEqual(
  							    list(self_attentions[0].shape[-3:] )      ,    [self.model_tester.num_attention_heads, seq_len, seq_len]      ,    )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				def check_hidden_states_output(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Optional[int] ):
  							SCREAMING_SNAKE_CASE    	=   model_class(lowercase )
  							model.to(lowercase )
  							model.eval()
  
  							with torch.no_grad():
  										SCREAMING_SNAKE_CASE    	=   model(**self._prepare_for_class(lowercase      ,    lowercase ) )
  
  							SCREAMING_SNAKE_CASE    	=   outputs.hidden_states
  							SCREAMING_SNAKE_CASE    	=   self.model_tester.num_hidden_layers + 1
  							self.assertEqual(len(lowercase )      ,    lowercase )
  
  							SCREAMING_SNAKE_CASE    	=   self.model_tester.seq_length
  
  							self.assertListEqual(
  							    list(hidden_states[0].shape[-2:] )      ,    [seq_length, self.model_tester.hidden_size]      ,    )
  
  				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(lowercase      ,    lowercase      ,    lowercase )
  
  							# check that output_hidden_states also work using config
  							del inputs_dict["output_hidden_states"]
  							SCREAMING_SNAKE_CASE    	=   True
  
  							check_hidden_states_output(lowercase      ,    lowercase      ,    lowercase )
  
  
  
  
  
  
  
  	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
  	def    snake_case__    (    self			:Optional[Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass
  
  
  
  def       a			(   ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   hf_hub_download(
  			    repo_id='''hf-internal-testing/spaghetti-video'''		,	filename='''eating_spaghetti_32_frames.npy'''		,	repo_type='''dataset'''  )
  			SCREAMING_SNAKE_CASE    	=   np.load(__lowerCAmelCase  )
  			return list(__lowerCAmelCase  )
  
  
  
  
  
  @require_torch
  @require_vision
  class 						lowerCamelCase			(					unittest.TestCase				):
  
  
  
  
  
  
  
  	@cached_property
  	def    snake_case__    (    self			:str )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return VivitImageProcessor() if is_vision_available() else None
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:Any )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400''' ).to(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   self.default_image_processor
  				SCREAMING_SNAKE_CASE    	=   prepare_video()
  				SCREAMING_SNAKE_CASE    	=   image_processor(lowercase      ,    return_tensors='''pt''' ).to(lowercase )
  
  				# forward pass
  				with torch.no_grad():
  							SCREAMING_SNAKE_CASE    	=   model(**lowercase )
  
  				# verify the logits
  				SCREAMING_SNAKE_CASE    	=   torch.Size((1, 4_0_0) )
  				self.assertEqual(outputs.logits.shape      ,    lowercase )
  
  				# taken from original model
  				SCREAMING_SNAKE_CASE    	=   torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58] ).to(lowercase )
  
  				self.assertTrue(torch.allclose(outputs.logits[0, :5]      ,    lowercase      ,    atol=1e-4 ) )
style_context: 
  import numpy as np
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Any      ,    lowercase			:List[Any]=5      ,    lowercase			:Optional[Any]=5 )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   depth
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   min_leaf_size
  				SCREAMING_SNAKE_CASE    	=   None
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:int      ,    lowercase			:int )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if labels.ndim != 1:
  							print('''Error: Input labels must be one dimensional''' )
  
  				return np.mean((labels - prediction) ** 2 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:Dict )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if x.ndim != 1:
  							print('''Error: Input data set must be one dimensional''' )
  							return
  				if len(lowercase ) != len(lowercase ):
  							print('''Error: X and y have different lengths''' )
  							return
  				if y.ndim != 1:
  							print('''Error: Data set labels must be one dimensional''' )
  							return
  
  				if len(lowercase ) < 2 * self.min_leaf_size:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				if self.depth == 1:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(lowercase      ,    np.mean(lowercase ) ) * 2
  				for i in range(len(lowercase ) ):
  							if len(x[:i] ) < self.min_leaf_size:
  										continue
  							elif len(x[i:] ) < self.min_leaf_size:
  										continue
  							else:
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[:i]      ,    np.mean(y[:i] ) )
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[i:]      ,    np.mean(y[i:] ) )
  										SCREAMING_SNAKE_CASE    	=   error_left + error_right
  										if error < min_error:
  													SCREAMING_SNAKE_CASE    	=   i
  													SCREAMING_SNAKE_CASE    	=   error
  
  				if best_split != 0:
  							SCREAMING_SNAKE_CASE    	=   x[:best_split]
  							SCREAMING_SNAKE_CASE    	=   y[:best_split]
  							SCREAMING_SNAKE_CASE    	=   x[best_split:]
  							SCREAMING_SNAKE_CASE    	=   y[best_split:]
  
  							SCREAMING_SNAKE_CASE    	=   x[best_split]
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							self.left.train(lowercase      ,    lowercase )
  							self.right.train(lowercase      ,    lowercase )
  				else:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  
  				return
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[Any] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if self.prediction is not None:
  							return self.prediction
  				elif self.left or self.right is not None:
  							if x >= self.decision_boundary:
  										return self.right.predict(lowercase )
  							else:
  										return self.left.predict(lowercase )
  				else:
  							print('''Error: Decision tree not yet trained''' )
  							return None
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:Optional[int]      ,    lowercase			:Optional[Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   float(0 )
  				for label in labels:
  							squared_error_sum += (label - prediction) ** 2
  
  				return float(squared_error_sum / labels.size )
  
  
  
  def       a			(   )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   np.arange(-1.0		,	1.0		,	0.0_05  )
  			SCREAMING_SNAKE_CASE    	=   np.sin(a  )
  
  			SCREAMING_SNAKE_CASE    	=   DecisionTree(depth=10		,	min_leaf_size=10  )
  			tree.train(a		,	a  )
  
  			SCREAMING_SNAKE_CASE    	=   (np.random.rand(10  ) * 2) - 1
  			SCREAMING_SNAKE_CASE    	=   np.array([tree.predict(a  ) for x in test_cases]  )
  			SCREAMING_SNAKE_CASE    	=   np.mean((predictions - test_cases) ** 2  )
  
  			print('''Test values: ''' + str(a  )  )
  			print('''Predictions: ''' + str(a  )  )
  			print('''Average error: ''' + str(a  )  )
  
  
  if __name__ == "__main__":
  							main()
  							import doctest
  
  							doctest.testmod(name='mean_squarred_error', verbose=True)