




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       LevitForImageClassification,
       LevitForImageClassificationWithTeacher,
       LevitModel,
   )
   from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.config_class(**self.inputs_dict		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'hidden_sizes'		)		)
       self.parent.assertTrue(hasattr(_lowerCAmelCase							,							'num_attention_heads'		)		)





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						int=13							,							_lowerCAmelCase    :						Dict=64							,							_lowerCAmelCase    :						Optional[Any]=3							,							_lowerCAmelCase    :						Dict=3							,							_lowerCAmelCase    :						List[Any]=2							,							_lowerCAmelCase    :						Tuple=1							,							_lowerCAmelCase    :						str=16							,							_lowerCAmelCase    :						Dict=[128, 256, 384]							,							_lowerCAmelCase    :						List[str]=[4, 6, 8]							,							_lowerCAmelCase    :						List[Any]=[2, 3, 4]							,							_lowerCAmelCase    :						Optional[int]=[16, 16, 16]							,							_lowerCAmelCase    :						List[Any]=0							,							_lowerCAmelCase    :						Optional[int]=[2, 2, 2]							,							_lowerCAmelCase    :						Optional[Any]=[2, 2, 2]							,							_lowerCAmelCase    :						Optional[int]=0.02							,							_lowerCAmelCase    :						List[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						int=2							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     kernel_size
       SCREAMING_SNAKE_CASE_      		=     stride
       SCREAMING_SNAKE_CASE_      		=     padding
       SCREAMING_SNAKE_CASE_      		=     hidden_sizes
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     depths
       SCREAMING_SNAKE_CASE_      		=     key_dim
       SCREAMING_SNAKE_CASE_      		=     drop_path_rate
       SCREAMING_SNAKE_CASE_      		=     patch_size
       SCREAMING_SNAKE_CASE_      		=     attention_ratio
       SCREAMING_SNAKE_CASE_      		=     mlp_ratio
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     [
           ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     use_labels
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     initializer_range



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       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.num_labels		)

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



 def  lowerCAmelCase_    (				self    :						int		):
       return LevitConfig(
           image_size=self.image_size							,							num_channels=self.num_channels							,							kernel_size=self.kernel_size							,							stride=self.stride							,							padding=self.padding							,							patch_size=self.patch_size							,							hidden_sizes=self.hidden_sizes							,							num_attention_heads=self.num_attention_heads							,							depths=self.depths							,							key_dim=self.key_dim							,							drop_path_rate=self.drop_path_rate							,							mlp_ratio=self.mlp_ratio							,							attention_ratio=self.attention_ratio							,							initializer_range=self.initializer_range							,							down_ops=self.down_ops							,							)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     LevitModel(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     (self.image_size, self.image_size)
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     image_size[0], image_size[1]
       for _ in range(4		):
             SCREAMING_SNAKE_CASE_      		=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
             SCREAMING_SNAKE_CASE_      		=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
       self.parent.assertEqual(
           result.last_hidden_state.shape							,							(self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])							,							)



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.num_labels
       SCREAMING_SNAKE_CASE_      		=     LevitForImageClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)		)




 def  lowerCAmelCase_    (				self    :						Dict		):
       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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (
     (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
     if is_torch_available()
     else ()
 )
 lowercase_							       =       (
     {
         "feature-extraction": LevitModel,
         "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     LevitModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase							,							has_text_modality=_lowerCAmelCase							,							hidden_size=37		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()



 def  lowerCAmelCase_    (				self    :						Dict		):
       return



 @unittest.skip(reason='Levit does not use inputs_embeds'		)
 def  lowerCAmelCase_    (				self    :						str		):
       pass



 @unittest.skip(reason='Levit does not support input and output embeddings'		)
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       pass



 @unittest.skip(reason='Levit does not output attentions'		)
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       pass



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       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  lowerCAmelCase_    (				self    :						Tuple		):
       def check_hidden_states_output(_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						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_      		=     len(self.model_tester.depths		) + 1
             self.assertEqual(len(_lowerCAmelCase		)							,							_lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_      		=     (self.model_tester.image_size, self.model_tester.image_size)
             SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     image_size[0], image_size[1]
             for _ in range(4		):
                   SCREAMING_SNAKE_CASE_      		=     floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1		)
                   SCREAMING_SNAKE_CASE_      		=     floor(
                       (
                           (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1		)
             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]		)							,							[
                     height * width,
                     self.model_tester.hidden_sizes[0],
                 ]							,							)

       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		)



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



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]=False		):
       SCREAMING_SNAKE_CASE_      		=     super()._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase							,							return_labels=_lowerCAmelCase		)

       if return_labels:
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

       return inputs_dict



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       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:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(_lowerCAmelCase		)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   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()



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_      		=     False
       SCREAMING_SNAKE_CASE_      		=     True

       for model_class in self.all_model_classes:
             if model_class in get_values(_lowerCAmelCase		) or not model_class.supports_gradient_checkpointing:
                   continue
             # LevitForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   continue
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             model.gradient_checkpointing_enable()
             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()



 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE_      		=     [
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(_lowerCAmelCase		),
                 ]
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   continue

             for problem_type in problem_types:
                   with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"		):
                         SCREAMING_SNAKE_CASE_      		=     problem_type['title']
                         SCREAMING_SNAKE_CASE_      		=     problem_type['num_labels']

                         SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
                         model.to(_lowerCAmelCase		)
                         model.train()

                         SCREAMING_SNAKE_CASE_      		=     self._prepare_for_class(_lowerCAmelCase							,							_lowerCAmelCase							,							return_labels=_lowerCAmelCase		)

                         if problem_type["num_labels"] > 1:
                               SCREAMING_SNAKE_CASE_      		=     inputs['labels'].unsqueeze(1		).repeat(1							,							problem_type['num_labels']		)

                         SCREAMING_SNAKE_CASE_      		=     inputs['labels'].to(problem_type['dtype']		)

                         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                         # they have the same size." which is a symptom something in wrong for the regression problem.
                         # See https://github.com/huggingface/transformers/issues/11780
                         with warnings.catch_warnings(record=_lowerCAmelCase		) as warning_list:
                               SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		).loss
                         for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message		):
                                     raise ValueError(
                                         F"Something is going wrong in the regression problem: intercepted {w.message}"		)

                         loss.backward()




 @slow
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_      		=     LevitModel.from_pretrained(_lowerCAmelCase		)
             self.assertIsNotNone(_lowerCAmelCase		)


def    UpperCAmelCase_				( )   ->					List[Any]:
      SCREAMING_SNAKE_CASE_      		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





@require_torch
@require_vision
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 @cached_property
 def  lowerCAmelCase_    (				self    :						Dict		):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)




 @slow
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
           _lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(images=_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       # verify the logits
       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 1_000)		)
       self.assertEqual(outputs.logits.shape							,							_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor([1.0448, -0.3745, -1.8317]		).to(_lowerCAmelCase		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,							_lowerCAmelCase							,							atol=1E-4		)		)






'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

_SCREAMING_SNAKE_CASE									=				"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION		)
class  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				Dict:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(self._get_feature_types()		)      ,  reference_urls=[
             "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
         ]      ,  )




 def 				_snake_case (						self		)     ->				Tuple:
     if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"		)		),
             "references": datasets.Sequence(datasets.Value("float"		)		),
         }
     else:
         return {
             "predictions": datasets.Value("float"		),
             "references": datasets.Value("float"		),
         }





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


