





'''simple docstring'''


import numpy as np


class 		UpperCamelCase__     :
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE=5     , SCREAMING_SNAKE_CASE=5 )				->  Optional[Any]:
        __lowerCAmelCase		:    Union[str, Any]			     =  depth
        __lowerCAmelCase		:    Optional[int]			     =  0
        __lowerCAmelCase		:    int			     =  None
        __lowerCAmelCase		:    Optional[int]			     =  None
        __lowerCAmelCase		:    List[str]			     =  min_leaf_size
        __lowerCAmelCase		:    Any			     =  None




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Optional[Any]:
        if labels.ndim != 1:
          print('Error: Input labels must be one dimensional' )

        return np.mean((labels - prediction) ** 2 )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  str:
        if x.ndim != 1:
          print('Error: Input data set must be one dimensional' )
          return
        if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
          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(SCREAMING_SNAKE_CASE ) < 2 * self.min_leaf_size:
          __lowerCAmelCase		:    Optional[Any]			     =  np.mean(SCREAMING_SNAKE_CASE )
          return

        if self.depth == 1:
          __lowerCAmelCase		:    Optional[Any]			     =  np.mean(SCREAMING_SNAKE_CASE )
          return

        __lowerCAmelCase		:    Optional[int]			     =  0
        __lowerCAmelCase		:    List[Any]			     =  self.mean_squared_error(SCREAMING_SNAKE_CASE     , np.mean(SCREAMING_SNAKE_CASE ) ) * 2
        for i in range(len(SCREAMING_SNAKE_CASE ) ):
          if len(x[:i] ) < self.min_leaf_size:
            continue
          elif len(x[i:] ) < self.min_leaf_size:
            continue
          else:
            __lowerCAmelCase		:    List[str]			     =  self.mean_squared_error(x[:i]     , np.mean(y[:i] ) )
            __lowerCAmelCase		:    Tuple			     =  self.mean_squared_error(x[i:]     , np.mean(y[i:] ) )
            __lowerCAmelCase		:    Dict			     =  error_left + error_right
            if error < min_error:
              __lowerCAmelCase		:    Any			     =  i
              __lowerCAmelCase		:    Dict			     =  error

        if best_split != 0:
          __lowerCAmelCase		:    str			     =  x[:best_split]
          __lowerCAmelCase		:    Union[str, Any]			     =  y[:best_split]
          __lowerCAmelCase		:    Union[str, Any]			     =  x[best_split:]
          __lowerCAmelCase		:    Union[str, Any]			     =  y[best_split:]

          __lowerCAmelCase		:    Tuple			     =  x[best_split]
          __lowerCAmelCase		:    Dict			     =  DecisionTree(
              depth=self.depth - 1     , min_leaf_size=self.min_leaf_size )
          __lowerCAmelCase		:    Optional[Any]			     =  DecisionTree(
              depth=self.depth - 1     , min_leaf_size=self.min_leaf_size )
          self.left.train(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
          self.right.train(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        else:
          __lowerCAmelCase		:    List[Any]			     =  np.mean(SCREAMING_SNAKE_CASE )

        return







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Optional[int]:
        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(SCREAMING_SNAKE_CASE )
          else:
            return self.left.predict(SCREAMING_SNAKE_CASE )
        else:
          print('Error: Decision tree not yet trained' )
          return None



class 		UpperCamelCase__     :
      '''simple docstring'''






      @staticmethod
      def 	snake_case  (     SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  int:
        __lowerCAmelCase		:    Any			     =  float(0 )
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size )




def       A							(      )  ->				int:


  '''simple docstring'''




  __lowerCAmelCase		:    List[Any]			     =  np.arange(-1.0						,1.0						,0.005					)
  __lowerCAmelCase		:    Optional[Any]			     =  np.sin(_UpperCAmelCase					)

  __lowerCAmelCase		:    Optional[int]			     =  DecisionTree(depth=1_0						,min_leaf_size=1_0					)
  tree.train(_UpperCAmelCase						,_UpperCAmelCase					)

  __lowerCAmelCase		:    Union[str, Any]			     =  (np.random.rand(1_0					) * 2) - 1
  __lowerCAmelCase		:    Optional[Any]			     =  np.array([tree.predict(_UpperCAmelCase					) for x in test_cases]					)
  __lowerCAmelCase		:    Tuple			     =  np.mean((predictions - test_cases) ** 2					)

  print('Test values: ' + str(_UpperCAmelCase					)					)
  print('Predictions: ' + str(_UpperCAmelCase					)					)
  print('Average error: ' + str(_UpperCAmelCase					)					)


if __name__ == "__main__":
    main()
    import doctest

    doctest.testmod(name="mean_squarred_error", verbose=True)












'''simple docstring'''


import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
    from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import SwinaSRImageProcessor


class 		UpperCamelCase__     :
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=13     , SCREAMING_SNAKE_CASE=32     , SCREAMING_SNAKE_CASE=1     , SCREAMING_SNAKE_CASE=3     , SCREAMING_SNAKE_CASE=16     , SCREAMING_SNAKE_CASE=[1, 2, 1]     , SCREAMING_SNAKE_CASE=[2, 2, 4]     , SCREAMING_SNAKE_CASE=2     , SCREAMING_SNAKE_CASE=2.0     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=0.0     , SCREAMING_SNAKE_CASE=0.0     , SCREAMING_SNAKE_CASE=0.1     , SCREAMING_SNAKE_CASE="gelu"     , SCREAMING_SNAKE_CASE=False     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=0.0_2     , SCREAMING_SNAKE_CASE=1e-5     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=None     , SCREAMING_SNAKE_CASE=False     , SCREAMING_SNAKE_CASE=2     , )				->  Tuple:
        __lowerCAmelCase		:    Optional[int]			     =  parent
        __lowerCAmelCase		:    List[str]			     =  batch_size
        __lowerCAmelCase		:    int			     =  image_size
        __lowerCAmelCase		:    List[Any]			     =  patch_size
        __lowerCAmelCase		:    List[Any]			     =  num_channels
        __lowerCAmelCase		:    Any			     =  embed_dim
        __lowerCAmelCase		:    List[str]			     =  depths
        __lowerCAmelCase		:    List[str]			     =  num_heads
        __lowerCAmelCase		:    Any			     =  window_size
        __lowerCAmelCase		:    Any			     =  mlp_ratio
        __lowerCAmelCase		:    Tuple			     =  qkv_bias
        __lowerCAmelCase		:    Optional[Any]			     =  hidden_dropout_prob
        __lowerCAmelCase		:    str			     =  attention_probs_dropout_prob
        __lowerCAmelCase		:    Optional[Any]			     =  drop_path_rate
        __lowerCAmelCase		:    Dict			     =  hidden_act
        __lowerCAmelCase		:    Dict			     =  use_absolute_embeddings
        __lowerCAmelCase		:    List[str]			     =  patch_norm
        __lowerCAmelCase		:    Dict			     =  layer_norm_eps
        __lowerCAmelCase		:    str			     =  initializer_range
        __lowerCAmelCase		:    int			     =  is_training
        __lowerCAmelCase		:    Optional[int]			     =  scope
        __lowerCAmelCase		:    str			     =  use_labels
        __lowerCAmelCase		:    int			     =  upscale

        # here we set some attributes to make tests pass
        __lowerCAmelCase		:    Any			     =  len(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  embed_dim
        __lowerCAmelCase		:    Union[str, Any]			     =  (image_size // patch_size) ** 2




      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    Union[str, Any]			     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        __lowerCAmelCase		:    Union[str, Any]			     =  None
        if self.use_labels:
          __lowerCAmelCase		:    int			     =  ids_tensor([self.batch_size]     , self.type_sequence_label_size )

        __lowerCAmelCase		:    List[Any]			     =  self.get_config()

        return config, pixel_values, labels




      def 	snake_case  (     self )				->  Any:
        return SwinaSRConfig(
            image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , embed_dim=self.embed_dim     , depths=self.depths     , num_heads=self.num_heads     , window_size=self.window_size     , mlp_ratio=self.mlp_ratio     , qkv_bias=self.qkv_bias     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , drop_path_rate=self.drop_path_rate     , hidden_act=self.hidden_act     , use_absolute_embeddings=self.use_absolute_embeddings     , path_norm=self.patch_norm     , layer_norm_eps=self.layer_norm_eps     , initializer_range=self.initializer_range     , upscale=self.upscale     , )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Optional[Any]:
        __lowerCAmelCase		:    Dict			     =  SwinaSRModel(config=SCREAMING_SNAKE_CASE )
        model.to(SCREAMING_SNAKE_CASE )
        model.eval()
        __lowerCAmelCase		:    Dict			     =  model(SCREAMING_SNAKE_CASE )

        self.parent.assertEqual(
            result.last_hidden_state.shape     , (self.batch_size, self.embed_dim, self.image_size, self.image_size) )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Dict:
        __lowerCAmelCase		:    Any			     =  SwinaSRForImageSuperResolution(SCREAMING_SNAKE_CASE )
        model.to(SCREAMING_SNAKE_CASE )
        model.eval()
        __lowerCAmelCase		:    Tuple			     =  model(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    Dict			     =  self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape     , (self.batch_size, self.num_channels, expected_image_size, expected_image_size) )







      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase		:    List[Any]			     =  self.prepare_config_and_inputs()
        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Union[str, Any]			     =  config_and_inputs
        __lowerCAmelCase		:    str			     =  {'pixel_values': pixel_values}
        return config, inputs_dict



@require_torch
class 		UpperCamelCase__     (       a	,	a	,	unittest.TestCase							):
      '''simple docstring'''






      _snake_case           =				(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      _snake_case           =				{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

      _snake_case           =				False
      _snake_case           =				False
      _snake_case           =				False
      _snake_case           =				False
      _snake_case           =				False




      def 	snake_case  (     self )				->  Optional[int]:
        __lowerCAmelCase		:    Optional[Any]			     =  SwinaSRModelTester(self )
        __lowerCAmelCase		:    List[str]			     =  ConfigTester(self     , config_class=SCREAMING_SNAKE_CASE     , embed_dim=37 )




      def 	snake_case  (     self )				->  Optional[int]:
        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 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    Optional[int]			     =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Optional[Any]:
        __lowerCAmelCase		:    Union[str, Any]			     =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_super_resolution(*SCREAMING_SNAKE_CASE )




      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
      def 	snake_case  (     self )				->  Any:
        pass




      @unittest.skip(reason='Swin2SR does not use inputs_embeds' )
      def 	snake_case  (     self )				->  List[str]:
        pass




      @unittest.skip(reason='Swin2SR does not support training yet' )
      def 	snake_case  (     self )				->  int:
        pass




      @unittest.skip(reason='Swin2SR does not support training yet' )
      def 	snake_case  (     self )				->  List[str]:
        pass




      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Any			     =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __lowerCAmelCase		:    List[str]			     =  model_class(SCREAMING_SNAKE_CASE )
          self.assertIsInstance(model.get_input_embeddings()     , (nn.Module) )
          __lowerCAmelCase		:    Union[str, Any]			     =  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE     , nn.Linear ) )




      def 	snake_case  (     self )				->  int:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    str			     =  self.model_tester.prepare_config_and_inputs_for_common()

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

          __lowerCAmelCase		:    Dict			     =  ['pixel_values']
          self.assertListEqual(arg_names[:1]     , SCREAMING_SNAKE_CASE )




      @slow
      def 	snake_case  (     self )				->  List[Any]:
        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __lowerCAmelCase		:    Any			     =  SwinaSRModel.from_pretrained(SCREAMING_SNAKE_CASE )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self )				->  Optional[int]:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Union[str, Any]			     =  self.model_tester.prepare_config_and_inputs_for_common()

        __lowerCAmelCase		:    List[Any]			     =  _config_zero_init(SCREAMING_SNAKE_CASE )
        for model_class in self.all_model_classes:
          __lowerCAmelCase		:    Any			     =  model_class(config=SCREAMING_SNAKE_CASE )
          for name, param in model.named_parameters():
            if "logit_scale" in name:
              continue
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()     , [0.0, 1.0]     , msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     , )







      def 	snake_case  (     self )				->  Dict:
        __lowerCAmelCase     ,					__lowerCAmelCase		:    Dict			     =  self.model_tester.prepare_config_and_inputs_for_common()
        __lowerCAmelCase		:    Dict			     =  True

        for model_class in self.all_model_classes:
          __lowerCAmelCase		:    Tuple			     =  True
          __lowerCAmelCase		:    int			     =  False
          __lowerCAmelCase		:    Dict			     =  True
          __lowerCAmelCase		:    Optional[int]			     =  model_class(SCREAMING_SNAKE_CASE )
          model.to(SCREAMING_SNAKE_CASE )
          model.eval()
          with torch.no_grad():
            __lowerCAmelCase		:    Any			     =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) )
          __lowerCAmelCase		:    Dict			     =  outputs.attentions
          __lowerCAmelCase		:    Optional[Any]			     =  len(self.model_tester.depths )
          self.assertEqual(len(SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __lowerCAmelCase		:    str			     =  True
          __lowerCAmelCase		:    Any			     =  config.window_size**2
          __lowerCAmelCase		:    int			     =  model_class(SCREAMING_SNAKE_CASE )
          model.to(SCREAMING_SNAKE_CASE )
          model.eval()
          with torch.no_grad():
            __lowerCAmelCase		:    Optional[int]			     =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) )
          __lowerCAmelCase		:    Optional[Any]			     =  outputs.attentions
          self.assertEqual(len(SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE )

          self.assertListEqual(
              list(attentions[0].shape[-3:] )     , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     , )
          __lowerCAmelCase		:    Dict			     =  len(SCREAMING_SNAKE_CASE )

          # Check attention is always last and order is fine
          __lowerCAmelCase		:    Optional[int]			     =  True
          __lowerCAmelCase		:    List[Any]			     =  True
          __lowerCAmelCase		:    Union[str, Any]			     =  model_class(SCREAMING_SNAKE_CASE )
          model.to(SCREAMING_SNAKE_CASE )
          model.eval()
          with torch.no_grad():
            __lowerCAmelCase		:    List[str]			     =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) )

          self.assertEqual(out_len + 1     , len(SCREAMING_SNAKE_CASE ) )

          __lowerCAmelCase		:    Optional[Any]			     =  outputs.attentions

          self.assertEqual(len(SCREAMING_SNAKE_CASE )     , SCREAMING_SNAKE_CASE )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:] )     , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     , )



@require_vision
@require_torch
@slow
class 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      def 	snake_case  (     self )				->  str:
        __lowerCAmelCase		:    int			     =  SwinaSRImageProcessor()
        __lowerCAmelCase		:    Optional[Any]			     =  SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64' ).to(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    int			     =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
        __lowerCAmelCase		:    Union[str, Any]			     =  processor(images=SCREAMING_SNAKE_CASE     , return_tensors='pt' ).to(SCREAMING_SNAKE_CASE )

        # forward pass
        with torch.no_grad():
          __lowerCAmelCase		:    List[Any]			     =  model(**SCREAMING_SNAKE_CASE )

        # verify the logits
        __lowerCAmelCase		:    Dict			     =  torch.Size([1, 3, 9_76, 12_96] )
        self.assertEqual(outputs.reconstruction.shape     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  torch.tensor(
            [[0.5_4_5_8, 0.5_5_4_6, 0.5_6_3_8], [0.5_5_2_6, 0.5_5_6_5, 0.5_6_5_1], [0.5_3_9_6, 0.5_4_2_6, 0.5_6_2_1]] ).to(SCREAMING_SNAKE_CASE )
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]     , SCREAMING_SNAKE_CASE     , atol=1e-4 ) )





