




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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=7		,     _a=3		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=None		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     )						->							Optional[int]:
           _A							:     Optional[int]			=						size if size is not None else {"""shortest_edge""": 18}
           _A							:     Optional[Any]			=						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           _A							:     Tuple			=						parent
           _A							:     int			=						batch_size
           _A							:     int			=						num_channels
           _A							:     int			=						image_size
           _A							:     Optional[Any]			=						min_resolution
           _A							:     Any			=						max_resolution
           _A							:     Any			=						do_resize
           _A							:     Any			=						size
           _A							:     Optional[Any]			=						do_center_crop
           _A							:     str			=						crop_size
           _A							:     Union[str, Any]			=						do_normalize
           _A							:     Any			=						image_mean
           _A							:     Dict			=						image_std



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







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						LevitImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[Any]			=						LevitImageProcessingTester(self				)

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

     def 					a__	(  self				)						->							Tuple:
           _A							:     Dict			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """do_center_crop"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size		,     {"""shortest_edge""": 18}				)
           self.assertEqual(image_processor.crop_size		,     {"""height""": 18, """width""": 18}				)

           _A							:     Dict			=						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				)						->							Optional[int]:
           pass

     def 					a__	(  self				)						->							Tuple:
           # Initialize image_processing
           _A							:     int			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Any			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Dict			=						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
           _A							:     Union[str, Any]			=						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 					a__	(  self				)						->							str:
           # Initialize image_processing
           _A							:     Any			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     List[Any]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     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
           _A							:     Tuple			=						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 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     Tuple			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     List[str]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     Tuple			=						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
           _A							:     Any			=						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"""],
               )		,     )



class      A__							:






				def __init__(						self					:      Optional[Any]    ,     a					:      list   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		set_counts
							lowerCAmelCase__      :      str         =		max(a   )
							lowerCAmelCase__      :      Any         =		len(a   )
							lowerCAmelCase__      :      List[str]         =		[1] * num_sets
							lowerCAmelCase__      :      Dict         =		list(range(a   )   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.get_parent(a   )
							lowerCAmelCase__      :      Tuple         =		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]
										lowerCAmelCase__      :      Tuple         =		0
										lowerCAmelCase__      :      str         =		dst_parent
										if self.ranks[dst_parent] == self.ranks[src_parent]:
													self.ranks[dst_parent] += 1
										lowerCAmelCase__      :      List[Any]         =		self.set_counts[dst_parent]
							else:
										self.set_counts[src_parent] += self.set_counts[dst_parent]
										lowerCAmelCase__      :      Optional[int]         =		0
										lowerCAmelCase__      :      Tuple         =		src_parent
										lowerCAmelCase__      :      Optional[int]         =		self.set_counts[src_parent]

							lowerCAmelCase__      :      Optional[Any]         =		max(self.max_set    ,     a   )
							return True






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      int   ):






							'''simple docstring'''







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