






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 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=18    ,	_lowerCAmelCase=30    ,	_lowerCAmelCase=400    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,	_lowerCAmelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,	_lowerCAmelCase=True    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				size if size is not None else {"height": 224, "width": 224}
          lowerCAmelCase__   :List[str]      							=				crop_size if crop_size is not None else {"height": 18, "width": 18}
          lowerCAmelCase__   :Dict      							=				parent
          lowerCAmelCase__   :Any      							=				batch_size
          lowerCAmelCase__   :Optional[int]      							=				num_channels
          lowerCAmelCase__   :Union[str, Any]      							=				image_size
          lowerCAmelCase__   :Union[str, Any]      							=				min_resolution
          lowerCAmelCase__   :Tuple      							=				max_resolution
          lowerCAmelCase__   :Optional[int]      							=				do_resize
          lowerCAmelCase__   :int      							=				size
          lowerCAmelCase__   :Union[str, Any]      							=				do_center_crop
          lowerCAmelCase__   :Union[str, Any]      							=				crop_size
          lowerCAmelCase__   :Union[str, Any]      							=				do_normalize
          lowerCAmelCase__   :Any      							=				image_mean
          lowerCAmelCase__   :Any      							=				image_std
          lowerCAmelCase__   :List[str]      							=				do_convert_rgb
      def 			snake_case_      (   self		):



          '''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 			snake_case_      (   self    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False		):



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

          if equal_resolution:
              lowerCAmelCase__   :List[Any]      							=				[]
              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:
              lowerCAmelCase__   :Optional[int]      							=				[]
              for i in range(self.batch_size		):
                  lowerCAmelCase__   :List[Any]      							=				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
              lowerCAmelCase__   :int      							=				[Image.fromarray(np.moveaxis(_lowerCAmelCase    ,	0    ,	-1		)		) for x in image_inputs]

          if torchify:
              lowerCAmelCase__   :Dict      							=				[torch.from_numpy(_lowerCAmelCase		) for x in image_inputs]

          return image_inputs







@require_torch
@require_vision
class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    ChineseCLIPImageProcessor if is_vision_available() else None
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				ChineseCLIPImageProcessingTester(self    ,	do_center_crop=_lowerCAmelCase		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				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}		)

          lowerCAmelCase__   :List[str]      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          pass
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          lowerCAmelCase__   :Union[str, Any]      							=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase		)
          for image in image_inputs:
              self.assertIsInstance(_lowerCAmelCase    ,	Image.Image		)

          # Test not batched input
          lowerCAmelCase__   :List[Any]      							=				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
          lowerCAmelCase__   :List[Any]      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          lowerCAmelCase__   :int      							=				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
          lowerCAmelCase__   :int      							=				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
          lowerCAmelCase__   :Dict      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          lowerCAmelCase__   :str      							=				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
          lowerCAmelCase__   :Union[str, Any]      							=				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
          lowerCAmelCase__   :Optional[Any]      							=				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 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    ChineseCLIPImageProcessor if is_vision_available() else None
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				ChineseCLIPImageProcessingTester(self    ,	num_channels=4    ,	do_center_crop=_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				3
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          pass



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          lowerCAmelCase__   :List[Any]      							=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase		)
          for image in image_inputs:
              self.assertIsInstance(_lowerCAmelCase    ,	Image.Image		)

          # Test not batched input
          lowerCAmelCase__   :Any      							=				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
          lowerCAmelCase__   :Optional[int]      							=				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"],
              )    ,	)














import re


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]"    ,			str_       )]


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :List[Any]      							=				split_input(str_       )
    return "".join(
        ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  bool    ,			UpperCAmelCase							:  str       ):
    try:
        lowerCAmelCase__   :Dict      							=				split_input(UpperCAmelCase       )
        if upper:
            lowerCAmelCase__   :Dict      							=				"".join(
                [
                    separator.join([char.upper() for char in sub_str]       )
                    for sub_str in string_split
                ]       )
        else:
            lowerCAmelCase__   :Any      							=				"".join(
                [
                    separator.join([char.lower() for char in sub_str]       )
                    for sub_str in string_split
                ]       )
        return res_str
    except IndexError:
        return "not valid string"


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return to_simple_case(UpperCAmelCase       )


def 				snake_case__							(							UpperCAmelCase							:  str       ):
    try:
        lowerCAmelCase__   :Any      							=				to_simple_case(UpperCAmelCase       )
        return res_str[0].lower() + res_str[1:]
    except IndexError:
        return "not valid string"


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  bool       ):
    return to_complex_case(UpperCAmelCase    ,			UpperCAmelCase    ,			"_"       )


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  bool       ):
    return to_complex_case(UpperCAmelCase    ,			UpperCAmelCase    ,			"-"       )


if __name__ == "__main__":
 __import__("""doctest""").testmod()



