

import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor



class 						lowercase_					(						unittest.TestCase  ):



      def __init__(    self   :       Union[str, Any]    ,      A__   :       Tuple    ,      A__   :       Union[str, Any]=7    ,      A__   :       Optional[Any]=3    ,      A__   :       Optional[Any]=18    ,      A__   :       str=30    ,      A__   :       Optional[int]=400    ,      A__   :       Optional[Any]=True    ,      A__   :       int=None    ,      A__   :       Any=True    ,      A__   :       Optional[int]=None    ,      A__   :       Dict=True    ,      A__   :       List[Any]=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,      A__   :       Optional[int]=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,      A__   :       Dict=True    ,      )					->  Union[str, Any]:
        _snake_case		=							size if size is not None else {'''shortest_edge''': 20}
        _snake_case		=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        _snake_case		=							parent
        _snake_case		=							batch_size
        _snake_case		=							num_channels
        _snake_case		=							image_size
        _snake_case		=							min_resolution
        _snake_case		=							max_resolution
        _snake_case		=							do_resize
        _snake_case		=							size
        _snake_case		=							do_center_crop
        _snake_case		=							crop_size
        _snake_case		=							do_normalize
        _snake_case		=							image_mean
        _snake_case		=							image_std
        _snake_case		=							do_convert_rgb



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  Any:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }







      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       str=False    ,      A__   :       Dict=False    ,      A__   :       Optional[Any]=False	)					->  int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          _snake_case		=							[]
          for i in range(self.batch_size	):
            image_inputs.append(
                np.random.randint(
                    255    ,      size=(self.num_channels, self.max_resolution, self.max_resolution)    ,      dtype=np.uinta	)	)
        else:
          _snake_case		=							[]
          for i in range(self.batch_size	):
            _snake_case,  _snake_case		=							np.random.choice(np.arange(self.min_resolution    ,      self.max_resolution	)    ,      2	)
            image_inputs.append(np.random.randint(255    ,      size=(self.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(A__    ,      0    ,      -1	)	) for x in image_inputs]

        if torchify:
          _snake_case		=							[torch.from_numpy(A__	) for x in image_inputs]

        return image_inputs



@require_torch
@require_vision
class 						lowercase_					(						__lowercase     ,							unittest.TestCase  ):
      UpperCamelCase_    :	Union[str, Any]		   =       CLIPImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  List[Any]:
        _snake_case		=							CLIPImageProcessingTester(self	)



      @property
      def 		UpperCamelCase_	(    self   :       Dict	)					->  Any:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Optional[Any]:
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A__    ,      '''do_resize'''	)	)
        self.assertTrue(hasattr(A__    ,      '''size'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_center_crop'''	)	)
        self.assertTrue(hasattr(A__    ,      '''center_crop'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_normalize'''	)	)
        self.assertTrue(hasattr(A__    ,      '''image_mean'''	)	)
        self.assertTrue(hasattr(A__    ,      '''image_std'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_convert_rgb'''	)	)



      def 		UpperCamelCase_	(    self   :       str	)					->  int:
        _snake_case		=							self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size    ,      {'''shortest_edge''': 20}	)
        self.assertEqual(image_processor.crop_size    ,      {'''height''': 18, '''width''': 18}	)

        _snake_case		=							self.image_processing_class.from_dict(self.image_processor_dict    ,      size=42    ,      crop_size=84	)
        self.assertEqual(image_processor.size    ,      {'''shortest_edge''': 42}	)
        self.assertEqual(image_processor.crop_size    ,      {'''height''': 84, '''width''': 84}	)



      def 		UpperCamelCase_	(    self   :       List[str]	)					->  Optional[int]:
        pass



      def 		UpperCamelCase_	(    self   :       str	)					->  Union[str, Any]:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _snake_case		=							self.image_processor_tester.prepare_inputs(equal_resolution=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      Image.Image	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )



      def 		UpperCamelCase_	(    self   :       int	)					->  Union[str, Any]:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _snake_case		=							self.image_processor_tester.prepare_inputs(equal_resolution=A__    ,      numpify=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      np.ndarray	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )







      def 		UpperCamelCase_	(    self   :       Tuple	)					->  List[str]:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _snake_case		=							self.image_processor_tester.prepare_inputs(equal_resolution=A__    ,      torchify=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      torch.Tensor	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )



@require_torch
@require_vision
class 						lowercase_					(						__lowercase     ,							unittest.TestCase  ):
      UpperCamelCase_    :	int		   =       CLIPImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_	(    self   :       int	)					->  Any:
        _snake_case		=							CLIPImageProcessingTester(self    ,      num_channels=4	)
        _snake_case		=							3



      @property
      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Any:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 		UpperCamelCase_	(    self   :       List[str]	)					->  int:
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(A__    ,      '''do_resize'''	)	)
        self.assertTrue(hasattr(A__    ,      '''size'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_center_crop'''	)	)
        self.assertTrue(hasattr(A__    ,      '''center_crop'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_normalize'''	)	)
        self.assertTrue(hasattr(A__    ,      '''image_mean'''	)	)
        self.assertTrue(hasattr(A__    ,      '''image_std'''	)	)
        self.assertTrue(hasattr(A__    ,      '''do_convert_rgb'''	)	)



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  str:
        pass







      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  int:
        # Initialize image_processing
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _snake_case		=							self.image_processor_tester.prepare_inputs(equal_resolution=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      Image.Image	)

        # Test not batched input
        _snake_case		=							image_processing(image_inputs[0]    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							image_processing(A__    ,      return_tensors='''pt'''	).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )    ,      )




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

