


"""simple docstring"""
import unittest

import numpy as np
import requests

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

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__SCREAMING_SNAKE_CASE							:      Any						=      False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor





class        __A       (unittest.TestCase):





							'''simple docstring'''

							def __init__(			self							: List[str]						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: int=7						,    UpperCAmelCase_							: Optional[int]=3						,    UpperCAmelCase_							: str=18						,    UpperCAmelCase_							: Optional[Any]=30						,    UpperCAmelCase_							: str=400						,    UpperCAmelCase_							: int=None						,    UpperCAmelCase_							: int=True						,    UpperCAmelCase_							: List[Any]=True						,    UpperCAmelCase_							: Dict=None						,    )   ->Any:



														"""simple docstring"""



														snake_case_          =						size if size is not None else {"""height""": 20, """width""": 20}
														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_          =						size
														snake_case_          =						do_normalize
														snake_case_          =						do_convert_rgb
														snake_case_          =						[512, 1_024, 2_048, 4_096]
														snake_case_          =						patch_size if patch_size is not None else {"""height""": 16, """width""": 16}



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
														snake_case_          =						Image.open(requests.get(UpperCAmelCase_						,    stream=UpperCAmelCase_    ).raw    ).convert("""RGB"""    )
														return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,						)
@require_torch
@require_vision
class        __A       (snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Optional[int]							 =      PixaStructImageProcessor if is_vision_available() else None



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



														"""simple docstring"""



														snake_case_          =						PixaStructImageProcessingTester(self    )



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



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 						lowerCAmelCase    (			self							: Optional[Any]    )   ->Any:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """do_normalize"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """do_convert_rgb"""    )    )



							def 						lowerCAmelCase    (			self							: int    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.image_processor_tester.prepare_dummy_image()

														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														snake_case_          =						2_048

														snake_case_          =						image_processor(UpperCAmelCase_						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    )
														self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,    torch.tensor(0.0_606    )						,    atol=1E-3						,    rtol=1E-3    )    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														# create random PIL images
														snake_case_          =						prepare_image_inputs(self.image_processor_tester						,    equal_resolution=UpperCAmelCase_    )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_						,    Image.Image    )

														# Test not batched input
														snake_case_          =						(
														    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
														    * self.image_processor_tester.num_channels
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					snake_case_          =						image_processor(
																					    image_inputs[0]						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (1, max_patch, expected_hidden_dim)						,    )

																					# Test batched
																					snake_case_          =						image_processor(
																					    UpperCAmelCase_						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,    )



							def 						lowerCAmelCase    (			self							: int    )   ->Any:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														# create random PIL images
														snake_case_          =						prepare_image_inputs(self.image_processor_tester						,    equal_resolution=UpperCAmelCase_    )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_						,    Image.Image    )

														# Test not batched input
														snake_case_          =						(
														    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
														    * self.image_processor_tester.num_channels
														) + 2

														snake_case_          =						True

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					with self.assertRaises(UpperCAmelCase_    ):
																												snake_case_          =						image_processor(
																												    image_inputs[0]						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches

																					snake_case_          =						"""Hello"""

																					snake_case_          =						image_processor(
																					    image_inputs[0]						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_						,    header_text=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (1, max_patch, expected_hidden_dim)						,    )

																					# Test batched
																					snake_case_          =						image_processor(
																					    UpperCAmelCase_						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_						,    header_text=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->str:



														"""simple docstring"""



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

														snake_case_          =						(
														    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
														    * self.image_processor_tester.num_channels
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					snake_case_          =						image_processor(
																					    image_inputs[0]						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (1, max_patch, expected_hidden_dim)						,    )

																					# Test batched
																					snake_case_          =						image_processor(
																					    UpperCAmelCase_						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,    )



							def 						lowerCAmelCase    (			self							: List[Any]    )   ->int:



														"""simple docstring"""



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

														# Test not batched input
														snake_case_          =						(
														    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
														    * self.image_processor_tester.num_channels
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					snake_case_          =						image_processor(
																					    image_inputs[0]						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (1, max_patch, expected_hidden_dim)						,    )

																					# Test batched
																					snake_case_          =						image_processor(
																					    UpperCAmelCase_						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,    )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,						)
@require_torch
@require_vision
class        __A       (snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Optional[Any]							 =      PixaStructImageProcessor if is_vision_available() else None



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



														"""simple docstring"""



														snake_case_          =						PixaStructImageProcessingTester(self						,    num_channels=4    )
														snake_case_          =						3



							@property
							def 						lowerCAmelCase    (			self							: Any    )   ->str:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """do_normalize"""    )    )
														self.assertTrue(hasattr(UpperCAmelCase_						,    """do_convert_rgb"""    )    )



							def 						lowerCAmelCase    (			self							: Dict    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						self.image_processing_class(**self.image_processor_dict    )
														# create random PIL images
														snake_case_          =						prepare_image_inputs(self.image_processor_tester						,    equal_resolution=UpperCAmelCase_    )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_						,    Image.Image    )

														# Test not batched input
														snake_case_          =						(
														    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
														    * (self.image_processor_tester.num_channels - 1)
														) + 2

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					snake_case_          =						image_processor(
																					    image_inputs[0]						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (1, max_patch, expected_hidden_dim)						,    )

																					# Test batched
																					snake_case_          =						image_processor(
																					    UpperCAmelCase_						,    return_tensors="""pt"""						,    max_patches=UpperCAmelCase_    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape						,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,    )







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0			)      ->			list:
							snake_case_          =						length or len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						False
							for i in range(length - 1			):
														if list_data[i] > list_data[i + 1]:
																					snake_case_    ,  snake_case_          =						list_data[i + 1], list_data[i]
																					snake_case_          =						True

							return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE			,	length - 1			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



