

'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					int     	=							UnCLIPImageVariationPipeline
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
   _SCREAMING_SNAKE_CASE   :					str     	=							IMAGE_VARIATION_BATCH_PARAMS

   _SCREAMING_SNAKE_CASE   :					int     	=							[
       'generator',
       'return_dict',
       'decoder_num_inference_steps',
       'super_res_num_inference_steps',
   ]
   _SCREAMING_SNAKE_CASE   :					Any     	=							False


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return 32


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return 32


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self.time_input_dim


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self.time_input_dim * 4


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return 100


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
      return tokenizer


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )
      _lowercase      :				Optional[Any]      					=       CLIPTextConfig(
          bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=self.text_embedder_hidden_size      ,    projection_dim=self.text_embedder_hidden_size      ,    intermediate_size=37      ,    layer_norm_eps=1E-0_5      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=1000      ,    )
      return CLIPTextModelWithProjection(_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )
      _lowercase      :				Tuple      					=       CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size      ,    projection_dim=self.text_embedder_hidden_size      ,    num_hidden_layers=5      ,    num_attention_heads=4      ,    image_size=32      ,    intermediate_size=37      ,    patch_size=1      ,    )
      return CLIPVisionModelWithProjection(_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )

      _lowercase      :				str      					=       {
          "clip_embeddings_dim": self.text_embedder_hidden_size,
          "time_embed_dim": self.time_embed_dim,
          "cross_attention_dim": self.cross_attention_dim,
      }

      _lowercase      :				Optional[int]      					=       UnCLIPTextProjModel(**_UpperCamelCase  )
      return model


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )

      _lowercase      :				int      					=       {
          "sample_size": 32,
          # RGB in channels
          "in_channels": 3,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 6,
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": "identity",
      }

      _lowercase      :				Tuple      					=       UNetaDConditionModel(**_UpperCamelCase  )
      return model


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return {
          "sample_size": 64,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(0  )

      _lowercase      :				Union[str, Any]      					=       UNetaDModel(**self.dummy_super_res_kwargs  )
      return model


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      torch.manual_seed(1  )

      _lowercase      :				Optional[Any]      					=       UNetaDModel(**self.dummy_super_res_kwargs  )
      return model


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.dummy_decoder
      _lowercase      :				Tuple      					=       self.dummy_text_proj
      _lowercase      :				Tuple      					=       self.dummy_text_encoder
      _lowercase      :				Any      					=       self.dummy_tokenizer
      _lowercase      :				Union[str, Any]      					=       self.dummy_super_res_first
      _lowercase      :				Union[str, Any]      					=       self.dummy_super_res_last

      _lowercase      :				int      					=       UnCLIPScheduler(
          variance_type="learned_range"      ,    prediction_type="epsilon"      ,    num_train_timesteps=1000      ,    )

      _lowercase      :				Any      					=       UnCLIPScheduler(
          variance_type="fixed_small_log"      ,    prediction_type="epsilon"      ,    num_train_timesteps=1000      ,    )

      _lowercase      :				Optional[int]      					=       CLIPImageProcessor(crop_size=32      ,    size=32  )

      _lowercase      :				List[str]      					=       self.dummy_image_encoder

      return {
          "decoder": decoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_proj": text_proj,
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				int      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				Optional[Any]      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				Optional[int]      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      if pil_image:
         _lowercase      :				List[Any]      					=       input_image * 0.5 + 0.5
         _lowercase      :				Tuple      					=       input_image.clamp(0      ,    1  )
         _lowercase      :				str      					=       input_image.cpu().permute(0      ,    2      ,    3      ,    1  ).float().numpy()
         _lowercase      :				Optional[int]      					=       DiffusionPipeline.numpy_to_pil(_UpperCamelCase  )[0]

      return {
          "image": input_image,
          "generator": generator,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "np",
      }


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       "cpu"

      _lowercase      :				Union[str, Any]      					=       self.get_dummy_components()

      _lowercase      :				Optional[Any]      					=       self.pipeline_class(**_UpperCamelCase  )
      _lowercase      :				Tuple      					=       pipe.to(_UpperCamelCase  )

      pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				Optional[Any]      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )

      _lowercase      :				Optional[Any]      					=       pipe(**_UpperCamelCase  )
      _lowercase      :				Any      					=       output.images

      _lowercase      :				Tuple      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )

      _lowercase      :				Union[str, Any]      					=       pipe(
          **_UpperCamelCase      ,    return_dict=_UpperCamelCase      ,    )[0]

      _lowercase      :				Tuple      					=       image[0, -3:, -3:, -1]
      _lowercase      :				int      					=       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      _lowercase      :				int      					=       np.array(
          [
              0.9_9_9_7,
              0.0_0_0_2,
              0.9_9_9_7,
              0.9_9_9_7,
              0.9_9_6_9,
              0.0_0_2_3,
              0.9_9_9_7,
              0.9_9_6_9,
              0.9_9_7_0,
          ]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       "cpu"

      _lowercase      :				Union[str, Any]      					=       self.get_dummy_components()

      _lowercase      :				Optional[Any]      					=       self.pipeline_class(**_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       pipe.to(_UpperCamelCase  )

      pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				Optional[int]      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )

      _lowercase      :				Optional[int]      					=       pipe(**_UpperCamelCase  )
      _lowercase      :				int      					=       output.images

      _lowercase      :				Any      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )

      _lowercase      :				Optional[Any]      					=       pipe(
          **_UpperCamelCase      ,    return_dict=_UpperCamelCase      ,    )[0]

      _lowercase      :				List[Any]      					=       image[0, -3:, -3:, -1]
      _lowercase      :				str      					=       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      _lowercase      :				Any      					=       np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       "cpu"

      _lowercase      :				List[str]      					=       self.get_dummy_components()

      _lowercase      :				List[Any]      					=       self.pipeline_class(**_UpperCamelCase  )
      _lowercase      :				Dict      					=       pipe.to(_UpperCamelCase  )

      pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				Dict      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )
      _lowercase      :				Any      					=       [
          pipeline_inputs["image"],
          pipeline_inputs["image"],
      ]

      _lowercase      :				Any      					=       pipe(**_UpperCamelCase  )
      _lowercase      :				List[Any]      					=       output.images

      _lowercase      :				Optional[Any]      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       [
          tuple_pipeline_inputs["image"],
          tuple_pipeline_inputs["image"],
      ]

      _lowercase      :				List[Any]      					=       pipe(
          **_UpperCamelCase      ,    return_dict=_UpperCamelCase      ,    )[0]

      _lowercase      :				Tuple      					=       image[0, -3:, -3:, -1]
      _lowercase      :				Dict      					=       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (2, 64, 64, 3)

      _lowercase      :				Optional[Any]      					=       np.array(
          [
              0.9_9_9_7,
              0.9_9_8_9,
              0.0_0_0_8,
              0.0_0_2_1,
              0.9_9_6_0,
              0.0_0_1_8,
              0.0_0_1_4,
              0.0_0_0_2,
              0.9_9_3_3,
          ]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       torch.device("cpu"  )


      class        a__  :
         _SCREAMING_SNAKE_CASE   :					Tuple     	=							1




      _lowercase      :				Any      					=       self.get_dummy_components()

      _lowercase      :				List[Any]      					=       self.pipeline_class(**_UpperCamelCase  )
      _lowercase      :				int      					=       pipe.to(_UpperCamelCase  )

      pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				List[str]      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(0  )
      _lowercase      :				int      					=       pipe.decoder.dtype
      _lowercase      :				List[Any]      					=       1

      _lowercase      :				List[str]      					=       (
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      _lowercase      :				Union[str, Any]      					=       pipe.prepare_latents(
          _UpperCamelCase      ,    dtype=_UpperCamelCase      ,    device=_UpperCamelCase      ,    generator=_UpperCamelCase      ,    latents=_UpperCamelCase      ,    scheduler=DummyScheduler()  )

      _lowercase      :				List[str]      					=       (
          batch_size,
          pipe.super_res_first.config.in_channels // 2,
          pipe.super_res_first.config.sample_size,
          pipe.super_res_first.config.sample_size,
      )
      _lowercase      :				List[Any]      					=       pipe.prepare_latents(
          _UpperCamelCase      ,    dtype=_UpperCamelCase      ,    device=_UpperCamelCase      ,    generator=_UpperCamelCase      ,    latents=_UpperCamelCase      ,    scheduler=DummyScheduler()  )

      _lowercase      :				List[Any]      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )

      _lowercase      :				str      					=       pipe(
          **_UpperCamelCase      ,    decoder_latents=_UpperCamelCase      ,    super_res_latents=_UpperCamelCase  ).images

      _lowercase      :				Any      					=       self.get_dummy_inputs(_UpperCamelCase      ,    pil_image=_UpperCamelCase  )
      # Don't pass image, instead pass embedding
      _lowercase      :				Any      					=       pipeline_inputs.pop("image"  )
      _lowercase      :				Union[str, Any]      					=       pipe.image_encoder(_UpperCamelCase  ).image_embeds

      _lowercase      :				int      					=       pipe(
          **_UpperCamelCase      ,    decoder_latents=_UpperCamelCase      ,    super_res_latents=_UpperCamelCase      ,    image_embeddings=_UpperCamelCase      ,    ).images

      # make sure passing text embeddings manually is identical
      assert np.abs(img_out_a - img_out_a  ).max() < 1E-4


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       torch_device == "cpu"

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      _lowercase      :				Any      					=       1E-2

      self._test_attention_slicing_forward_pass(
          test_max_difference=_UpperCamelCase      ,    expected_max_diff=_UpperCamelCase  )


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       torch_device == "cpu"
      _lowercase      :				Optional[int]      					=       True
      _lowercase      :				List[Any]      					=       [
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=_UpperCamelCase      ,    relax_max_difference=_UpperCamelCase      ,    additional_params_copy_to_batched_inputs=_UpperCamelCase      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       [
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      if torch_device == "mps":
         # TODO: MPS errors with larger batch sizes
         _lowercase      :				List[Any]      					=       [2, 3]
         self._test_inference_batch_consistent(
             batch_sizes=_UpperCamelCase      ,    additional_params_copy_to_batched_inputs=_UpperCamelCase      ,    )
      else:
         self._test_inference_batch_consistent(
             additional_params_copy_to_batched_inputs=_UpperCamelCase  )


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return super().test_dict_tuple_outputs_equivalent()


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return super().test_save_load_local()






   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return super().test_save_load_optional_components()




@slow
@require_torch_gpu
class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"  )
      _lowercase      :				Tuple      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"  )

      _lowercase      :				Union[str, Any]      					=       UnCLIPImageVariationPipeline.from_pretrained(
          "kakaobrain/karlo-v1-alpha-image-variations"      ,    torch_dtype=torch.floataa  )
      _lowercase      :				List[Any]      					=       pipeline.to(_UpperCamelCase  )
      pipeline.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				Optional[int]      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				int      					=       pipeline(
          _UpperCamelCase      ,    generator=_UpperCamelCase      ,    output_type="np"      ,    )

      _lowercase      :				List[Any]      					=       output.images[0]

      assert image.shape == (256, 256, 3)

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase      ,    15  )









'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


_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'

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


_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        a__  (							datasets.Metric  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






