


"""simple docstring"""

class    lowerCamelCase__						:
						def __init__(		self	:	List[Any]      ,       A_	:	list							):






							'''simple docstring'''


							__lowercase									=	set_counts
							__lowercase									=	max(A_							)
							__lowercase									=	len(A_							)
							__lowercase									=	[1] * num_sets
							__lowercase									=	list(range(A_							)							)
						def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	int      ,       A_	:	int							):






							'''simple docstring'''


							__lowercase									=	self.get_parent(A_							)
							__lowercase									=	self.get_parent(A_							)

							if src_parent == dst_parent:
								return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
								self.set_counts[dst_parent] += self.set_counts[src_parent]
								__lowercase									=	0
								__lowercase									=	dst_parent
								if self.ranks[dst_parent] == self.ranks[src_parent]:
									self.ranks[dst_parent] += 1
								__lowercase									=	self.set_counts[dst_parent]
							else:
								self.set_counts[src_parent] += self.set_counts[dst_parent]
								__lowercase									=	0
								__lowercase									=	src_parent
								__lowercase									=	self.set_counts[src_parent]

							__lowercase									=	max(self.max_set      ,       A_							)
							return True






						def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	int							):






							'''simple docstring'''


							if self.parents[disj_set] == disj_set:
								return disj_set
							__lowercase									=	self.get_parent(self.parents[disj_set]							)
							return self.parents[disj_set]




"""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    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	List[Any]      ,       A_	:	List[str]      ,       A_	:	str=7      ,       A_	:	Optional[int]=3      ,       A_	:	str=1_8      ,       A_	:	Tuple=3_0      ,       A_	:	Optional[Any]=4_0_0      ,       A_	:	Union[str, Any]=True      ,       A_	:	str=None      ,       A_	:	str=True      ,       A_	:	Optional[Any]=None      ,       A_	:	List[Any]=True      ,       A_	:	Union[str, Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]      ,       A_	:	str=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]      ,       A_	:	List[str]=True      ,       ):






       '''simple docstring'''


       __lowercase									=	size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       __lowercase									=	crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	image_size
       __lowercase									=	min_resolution
       __lowercase									=	max_resolution
       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	do_center_crop
       __lowercase									=	crop_size
       __lowercase									=	do_normalize
       __lowercase									=	image_mean
       __lowercase									=	image_std
       __lowercase									=	do_convert_rgb
      def SCREAMING_SNAKE_CASE_	(		self	:	List[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 SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	List[Any]=False      ,       A_	:	List[Any]=False      ,       A_	:	Optional[Any]=False							):






       '''simple docstring'''


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

       if equal_resolution:
        __lowercase									=	[]
        for i in range(self.batch_size							):
         image_inputs.append(
             np.random.randint(
                 2_5_5      ,       size=(self.num_channels, self.max_resolution, self.max_resolution)      ,       dtype=np.uinta							)							)
       else:
        __lowercase									=	[]
        for i in range(self.batch_size							):
         __lowercase							,		__lowercase									=	np.random.choice(np.arange(self.min_resolution      ,       self.max_resolution							)      ,       2							)
         image_inputs.append(np.random.randint(2_5_5      ,       size=(self.num_channels, width, height)      ,       dtype=np.uinta							)							)

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

       if torchify:
        __lowercase									=	[torch.from_numpy(A_							) for x in image_inputs]

       return image_inputs






@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Union[str, Any]											=		ChineseCLIPImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	ChineseCLIPImageProcessingTester(self      ,       do_center_crop=A_							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size      ,       {"""height""": 2_2_4, """width""": 2_2_4}							)
       self.assertEqual(image_processor.crop_size      ,       {"""height""": 1_8, """width""": 1_8}							)

       __lowercase									=	self.image_processing_class.from_dict(self.image_processor_dict      ,       size=4_2      ,       crop_size=8_4							)
       self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 4_2}							)
       self.assertEqual(image_processor.crop_size      ,       {"""height""": 8_4, """width""": 8_4}							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


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

       # Test not batched input
       __lowercase									=	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
       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       __lowercase									=	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
       __lowercase									=	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
       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowercase									=	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
       __lowercase									=	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
       __lowercase									=	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    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Optional[Any]											=		ChineseCLIPImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	ChineseCLIPImageProcessingTester(self      ,       num_channels=4      ,       do_center_crop=A_							)
       __lowercase									=	3
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       pass






      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


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

       # Test not batched input
       __lowercase									=	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
       __lowercase									=	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"""],
           )      ,       )
