

'''simple docstring'''
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 ChineseCLIPImageProcessor







class 							A__   (	unittest.TestCase  ):





  def __init__(		self				:     str							, _a				:     Optional[Any]							, _a				:     int=7							, _a				:     int=3							, _a				:     Dict=18							, _a				:     int=30							, _a				:     Optional[Any]=400							, _a				:     Dict=True							, _a				:     Optional[int]=None							, _a				:     int=True							, _a				:     List[Any]=None							, _a				:     Dict=True							, _a				:     Dict=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							, _a				:     Dict=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							, _a				:     Any=True							, )     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =size if size is not None else {'height': 224, 'width': 224}
       _SCREAMING_SNAKE_CASE							   =crop_size if crop_size is not None else {'height': 18, 'width': 18}
       _SCREAMING_SNAKE_CASE							   =parent
       _SCREAMING_SNAKE_CASE							   =batch_size
       _SCREAMING_SNAKE_CASE							   =num_channels
       _SCREAMING_SNAKE_CASE							   =image_size
       _SCREAMING_SNAKE_CASE							   =min_resolution
       _SCREAMING_SNAKE_CASE							   =max_resolution
       _SCREAMING_SNAKE_CASE							   =do_resize
       _SCREAMING_SNAKE_CASE							   =size
       _SCREAMING_SNAKE_CASE							   =do_center_crop
       _SCREAMING_SNAKE_CASE							   =crop_size
       _SCREAMING_SNAKE_CASE							   =do_normalize
       _SCREAMING_SNAKE_CASE							   =image_mean
       _SCREAMING_SNAKE_CASE							   =image_std
       _SCREAMING_SNAKE_CASE							   =do_convert_rgb





  def 						A  (		self				:     Optional[int]			)     ->   Optional[Any]:





       '''simple docstring'''






       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 						A  (		self				:     List[Any]							, _a				:     Union[str, Any]=False							, _a				:     List[str]=False							, _a				:     Dict=False			)     ->   List[Any]:





       '''simple docstring'''






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

       if equal_resolution:
            _SCREAMING_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:
            _SCREAMING_SNAKE_CASE							   =[]
            for i in range(self.batch_size			):
                 _SCREAMING_SNAKE_CASE   ,						_SCREAMING_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
            _SCREAMING_SNAKE_CASE							   =[Image.fromarray(np.moveaxis(_lowerCamelCase							, 0							, -1			)			) for x in image_inputs]

       if torchify:
            _SCREAMING_SNAKE_CASE							   =[torch.from_numpy(_lowerCamelCase			) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class 							A__   (	A_					,       unittest.TestCase  ):
  A__					=    ChineseCLIPImageProcessor if is_vision_available() else None





  def 						A  (		self				:     Tuple			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ChineseCLIPImageProcessingTester(self							, do_center_crop=_lowerCamelCase			)





  @property
  def 						A  (		self				:     List[str]			)     ->   Tuple:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self				:     List[Any]			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_resize'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'size'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_center_crop'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'center_crop'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_normalize'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'image_mean'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'image_std'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_convert_rgb'			)			)





  def 						A  (		self				:     int			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size							, {'height': 224, 'width': 224}			)
       self.assertEqual(image_processor.crop_size							, {'height': 18, 'width': 18}			)

       _SCREAMING_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 						A  (		self				:     str			)     ->   int:





       '''simple docstring'''






       pass





  def 						A  (		self				:     List[str]			)     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       _SCREAMING_SNAKE_CASE							   =self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase			)
       for image in image_inputs:
            self.assertIsInstance(_lowerCamelCase							, Image.Image			)

       # Test not batched input
       _SCREAMING_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
       _SCREAMING_SNAKE_CASE							   =image_processing(_lowerCamelCase							, 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 						A  (		self				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE							   =self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase							, numpify=_lowerCamelCase			)
       for image in image_inputs:
            self.assertIsInstance(_lowerCamelCase							, np.ndarray			)

       # Test not batched input
       _SCREAMING_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
       _SCREAMING_SNAKE_CASE							   =image_processing(_lowerCamelCase							, 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 						A  (		self				:     Dict			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE							   =self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase							, torchify=_lowerCamelCase			)
       for image in image_inputs:
            self.assertIsInstance(_lowerCamelCase							, torch.Tensor			)

       # Test not batched input
       _SCREAMING_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
       _SCREAMING_SNAKE_CASE							   =image_processing(_lowerCamelCase							, 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 							A__   (	A_					,       unittest.TestCase  ):
  A__					=    ChineseCLIPImageProcessor if is_vision_available() else None





  def 						A  (		self				:     Dict			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =ChineseCLIPImageProcessingTester(self							, num_channels=4							, do_center_crop=_lowerCamelCase			)
       _SCREAMING_SNAKE_CASE							   =3





  @property
  def 						A  (		self				:     Union[str, Any]			)     ->   Any:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self				:     Dict			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_resize'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'size'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_center_crop'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'center_crop'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_normalize'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'image_mean'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'image_std'			)			)
       self.assertTrue(hasattr(_lowerCamelCase							, 'do_convert_rgb'			)			)





  def 						A  (		self				:     List[Any]			)     ->   Any:





       '''simple docstring'''






       pass





  def 						A  (		self				:     Union[str, Any]			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       _SCREAMING_SNAKE_CASE							   =self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase			)
       for image in image_inputs:
            self.assertIsInstance(_lowerCamelCase							, Image.Image			)

       # Test not batched input
       _SCREAMING_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
       _SCREAMING_SNAKE_CASE							   =image_processing(_lowerCamelCase							, 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'],
           )							, )



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      bool:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =0
     for ch in input_str:
          _SCREAMING_SNAKE_CASE							   =ord(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =pow(2							,			_UpperCamelCase    )

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()
