




'''simple docstring'''



import numpy as np







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[int]=5						,   __lowerCamelCase      :			Tuple=5					):

        """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 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			str					):

        """simple docstring"""






        if labels.ndim != 1:
             print("Error: Input labels must be one dimensional"					)

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



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Optional[Any]					):

        """simple docstring"""






        if x.ndim != 1:
             print("Error: Input data set must be one dimensional"					)
             return
        if len(lowerCAmelCase_					) != len(lowerCAmelCase_					):
             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(lowerCAmelCase_					) < 2 * self.min_leaf_size:
             _SCREAMING_SNAKE_CASE =   np.mean(lowerCAmelCase_					)
             return

        if self.depth == 1:
             _SCREAMING_SNAKE_CASE =   np.mean(lowerCAmelCase_					)
             return

        _SCREAMING_SNAKE_CASE =   0
        _SCREAMING_SNAKE_CASE =   self.mean_squared_error(lowerCAmelCase_						,   np.mean(lowerCAmelCase_					)					) * 2
        for i in range(len(lowerCAmelCase_					)					):
             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(lowerCAmelCase_						,   lowerCAmelCase_					)
             self.right.train(lowerCAmelCase_						,   lowerCAmelCase_					)
        else:
             _SCREAMING_SNAKE_CASE =   np.mean(lowerCAmelCase_					)

        return



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]					):

        """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(lowerCAmelCase_					)
             else:
                  return self.left.predict(lowerCAmelCase_					)
        else:
             print("Error: Decision tree not yet trained"					)
             return None







class 	lowercase_					:


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_    (					__lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Any					):

        """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 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
     _SCREAMING_SNAKE_CASE =   np.arange(-1.0 ,  1.0 ,  0.0_0_5						)
     _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)







'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 	lowercase_					:


    """simple docstring"""

    @staticmethod
    def 							lowerCAmelCase_    (					*__lowerCamelCase      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

       """simple docstring"""
       pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   lowerCamelCase_       						=		TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ImageClassificationPipeline(model=__lowerCamelCase						,   image_processor=__lowerCamelCase						,   top_k=2					)
      _SCREAMING_SNAKE_CASE =   [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)

      self.assertEqual(
          __lowerCamelCase						,   [
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
          ]						,   )

      import datasets

      _SCREAMING_SNAKE_CASE =   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,   "image"						,   split="test"					)

      # Accepts URL + PIL.Image + lists
      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]					)
      self.assertEqual(
          __lowerCamelCase						,   [
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
          ]						,   )



   @require_torch
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   @require_tf
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase						,   framework="tf"					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      _SCREAMING_SNAKE_CASE =   pipeline(
          "image-classification"						,   model="hf-internal-testing/tiny-random-vit"						,   tokenizer=__lowerCamelCase					)

      self.assertIs(image_classifier.tokenizer						,   __lowerCamelCase					)



   @slow
   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # Perceiver is not tested by `run_pipeline_test` properly.
      # That is because the type of feature_extractor and model preprocessor need to be kept
      # in sync, which is not the case in the current design
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-conv"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-fourier"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-learned"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]						,   )

