

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=False					,			_UpperCamelCase=False					,			_UpperCamelCase=False	)				->      str:



  """simple docstring"""






  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _snake_case		=				[]
  for i in range(image_processor_tester.batch_size	):
    if equal_resolution:
      _snake_case		=				_snake_case		=				image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      _snake_case		=				image_processor_tester.min_resolution
      if getattr(_UpperCamelCase					,			'''size_divisor'''					,			_UpperCamelCase	):
        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
        _snake_case		=				max(image_processor_tester.size_divisor					,			_UpperCamelCase	)
      _snake_case,	_snake_case		=				np.random.choice(np.arange(_UpperCamelCase					,			image_processor_tester.max_resolution	)					,			2	)
    image_inputs.append(
        np.random.randint(255					,			size=(image_processor_tester.num_channels, width, height)					,			dtype=np.uinta	)	)

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    _snake_case		=				[Image.fromarray(np.moveaxis(_UpperCamelCase					,			0					,			-1	)	) for image in image_inputs]

  if torchify:
    _snake_case		=				[torch.from_numpy(_UpperCamelCase	) for image in image_inputs]

  return image_inputs



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=10					,			_UpperCamelCase=10					,			_UpperCamelCase=False					,			_UpperCamelCase=False	)				->      int:



  """simple docstring"""






  _snake_case		=				[]
  for i in range(image_processor_tester.num_frames	):
    video.append(np.random.randint(255					,			size=(image_processor_tester.num_channels, width, height)					,			dtype=np.uinta	)	)

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    _snake_case		=				[Image.fromarray(np.moveaxis(_UpperCamelCase					,			0					,			-1	)	) for frame in video]

  if torchify:
    _snake_case		=				[torch.from_numpy(_UpperCamelCase	) for frame in video]

  return video



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=False					,			_UpperCamelCase=False					,			_UpperCamelCase=False	)				->      Tuple:



  """simple docstring"""






  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _snake_case		=				[]
  for i in range(image_processor_tester.batch_size	):
    if equal_resolution:
      _snake_case		=				_snake_case		=				image_processor_tester.max_resolution
    else:
      _snake_case,	_snake_case		=				np.random.choice(
          np.arange(image_processor_tester.min_resolution					,			image_processor_tester.max_resolution	)					,			2	)
      _snake_case		=				prepare_video(
          image_processor_tester=_UpperCamelCase					,			width=_UpperCamelCase					,			height=_UpperCamelCase					,			numpify=_UpperCamelCase					,			torchify=_UpperCamelCase					,			)
    video_inputs.append(_UpperCamelCase	)

  return video_inputs



class     lowercase_       :
      UpperCamelCase_     :						int		 =							None



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Tuple:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        _snake_case		=				json.loads(image_processor.to_json_string()						)
        for key, value in self.image_processor_dict.items():
          self.assertEqual(obj[key]     ,   A__						)



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	List[Any]:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case		=				os.path.join(A__     ,   '''image_processor.json'''						)
          image_processor_first.to_json_file(A__						)
          _snake_case		=				self.image_processing_class.from_json_file(A__						)

        self.assertEqual(image_processor_second.to_dict()     ,   image_processor_first.to_dict()						)



      def 		UpperCamelCase_						(     self :							str						)       ->	Optional[Any]:
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case		=				image_processor_first.save_pretrained(A__						)[0]
          check_json_file_has_correct_format(A__						)
          _snake_case		=				self.image_processing_class.from_pretrained(A__						)

        self.assertEqual(image_processor_second.to_dict()     ,   image_processor_first.to_dict()						)



      def 		UpperCamelCase_						(     self :							Tuple						)       ->	str:
        _snake_case		=				self.image_processing_class()
        self.assertIsNotNone(A__						)







      @require_torch
      @require_vision
      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	int:
        if self.test_cast_dtype is not None:
          # Initialize image_processor
          _snake_case		=				self.image_processing_class(**self.image_processor_dict						)

          # create random PyTorch tensors
          _snake_case		=				prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=A__     ,   torchify=A__						)

          _snake_case		=				image_processor(A__     ,   return_tensors='''pt'''						)
          # for layoutLM compatiblity
          self.assertEqual(encoding.pixel_values.device     ,   torch.device('''cpu'''						)						)
          self.assertEqual(encoding.pixel_values.dtype     ,   torch.floataa						)

          _snake_case		=				image_processor(A__     ,   return_tensors='''pt'''						).to(torch.floataa						)
          self.assertEqual(encoding.pixel_values.device     ,   torch.device('''cpu'''						)						)
          self.assertEqual(encoding.pixel_values.dtype     ,   torch.floataa						)

          _snake_case		=				image_processor(A__     ,   return_tensors='''pt'''						).to('''cpu'''     ,   torch.bfloataa						)
          self.assertEqual(encoding.pixel_values.device     ,   torch.device('''cpu'''						)						)
          self.assertEqual(encoding.pixel_values.dtype     ,   torch.bfloataa						)

          with self.assertRaises(A__						):
            _snake_case		=				image_processor(A__     ,   return_tensors='''pt'''						).to(torch.bfloataa     ,   '''cpu'''						)

          # Try with text + image feature
          _snake_case		=				image_processor(A__     ,   return_tensors='''pt'''						)
          encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
          _snake_case		=				encoding.to(torch.floataa						)

          self.assertEqual(encoding.pixel_values.device     ,   torch.device('''cpu'''						)						)
          self.assertEqual(encoding.pixel_values.dtype     ,   torch.floataa						)
          self.assertEqual(encoding.input_ids.dtype     ,   torch.long						)




import numpy as np



class     lowercase_       :



      def __init__(     self :							Any     ,   A__ :							Optional[int]=5     ,   A__ :							Any=5						)       ->	Any:
        _snake_case		=				depth
        _snake_case		=				0
        _snake_case		=				None
        _snake_case		=				None
        _snake_case		=				min_leaf_size
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Tuple     ,   A__ :							Union[str, Any]						)       ->	int:
        if labels.ndim != 1:
          print('''Error: Input labels must be one dimensional'''						)

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



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

        if self.depth == 1:
          _snake_case		=				np.mean(A__						)
          return

        _snake_case		=				0
        _snake_case		=				self.mean_squared_error(A__     ,   np.mean(A__						)						) * 2
        for i in range(len(A__						)						):
          if len(x[:i]						) < self.min_leaf_size:
            continue
          elif len(x[i:]						) < self.min_leaf_size:
            continue
          else:
            _snake_case		=				self.mean_squared_error(x[:i]     ,   np.mean(y[:i]						)						)
            _snake_case		=				self.mean_squared_error(x[i:]     ,   np.mean(y[i:]						)						)
            _snake_case		=				error_left + error_right
            if error < min_error:
              _snake_case		=				i
              _snake_case		=				error

        if best_split != 0:
          _snake_case		=				x[:best_split]
          _snake_case		=				y[:best_split]
          _snake_case		=				x[best_split:]
          _snake_case		=				y[best_split:]

          _snake_case		=				x[best_split]
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          self.left.train(A__     ,   A__						)
          self.right.train(A__     ,   A__						)
        else:
          _snake_case		=				np.mean(A__						)

        return







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



class     lowercase_       :



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        _snake_case		=				float(0						)
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size						)



def  snake_case_()				->      Any:



  """simple docstring"""






  _snake_case		=				np.arange(-1.0					,			1.0					,			0.005	)
  _snake_case		=				np.sin(_UpperCamelCase	)

  _snake_case		=				DecisionTree(depth=10					,			min_leaf_size=10	)
  tree.train(_UpperCamelCase					,			_UpperCamelCase	)

  _snake_case		=				(np.random.rand(10	) * 2) - 1
  _snake_case		=				np.array([tree.predict(_UpperCamelCase	) for x in test_cases]	)
  _snake_case		=				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)

