




'''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:
	_lowercase  =			False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor
class 						a_				(			unittest.TestCase						):




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Union[str, Any]=7       ,		__lowerCAmelCase     :  Dict=3       ,		__lowerCAmelCase     :  int=1_8       ,		__lowerCAmelCase     :  List[str]=3_0       ,		__lowerCAmelCase     :  List[str]=4_0_0       ,		__lowerCAmelCase     :  Any=None       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  Optional[Any]=True       ,		__lowerCAmelCase     :  Optional[int]=None       ,		):
							__snake_case						 =   size if size is not None else {'height': 2_0, 'width': 2_0}
							__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						 =   [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
							__snake_case						 =   patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}




	def   lowercase__   (       self     :  List[str]      ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
							__snake_case						 =   Image.open(requests.get(__lowerCAmelCase       ,		stream=__lowerCAmelCase      ).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_				(			UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     str			  =      PixaStructImageProcessor if is_vision_available() else None




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   PixaStructImageProcessingTester(self      )




	@property
	def   lowercase__   (       self     :  int      ):
							return self.image_processor_tester.prepare_image_processor_dict()




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(__lowerCAmelCase       ,		'do_normalize'      )      )
							self.assertTrue(hasattr(__lowerCAmelCase       ,		'do_convert_rgb'      )      )




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.image_processor_tester.prepare_dummy_image()

							__snake_case						 =   self.image_processing_class(**self.image_processor_dict      )
							__snake_case						 =   2_0_4_8

							__snake_case						 =   image_processor(__lowerCAmelCase       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase      )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,		torch.tensor(0.0606      )       ,		atol=1E-3       ,		rtol=1E-3      )      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							# Initialize image_processor
							__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=__lowerCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase       ,		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=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

													# Test batched
													__snake_case						 =   image_processor(
													    __lowerCAmelCase       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)




	def   lowercase__   (       self     :  Optional[int]      ):
							# Initialize image_processor
							__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=__lowerCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase       ,		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(__lowerCAmelCase      ):
																			__snake_case						 =   image_processor(
																			    image_inputs[0]       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase      ).flattened_patches

													__snake_case						 =   'Hello'

													__snake_case						 =   image_processor(
													    image_inputs[0]       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase       ,		header_text=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

													# Test batched
													__snake_case						 =   image_processor(
													    __lowerCAmelCase       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase       ,		header_text=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)




	def   lowercase__   (       self     :  Any      ):
							# Initialize image_processor
							__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=__lowerCAmelCase       ,		numpify=__lowerCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase       ,		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=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

													# Test batched
													__snake_case						 =   image_processor(
													    __lowerCAmelCase       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)




	def   lowercase__   (       self     :  Union[str, Any]      ):
							# Initialize image_processor
							__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=__lowerCAmelCase       ,		torchify=__lowerCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase       ,		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=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

													# Test batched
													__snake_case						 =   image_processor(
													    __lowerCAmelCase       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase      ).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_				(			UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     Tuple			  =      PixaStructImageProcessor if is_vision_available() else None




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   PixaStructImageProcessingTester(self       ,		num_channels=4      )
							__snake_case						 =   3




	@property
	def   lowercase__   (       self     :  Any      ):
							return self.image_processor_tester.prepare_image_processor_dict()




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(__lowerCAmelCase       ,		'do_normalize'      )      )
							self.assertTrue(hasattr(__lowerCAmelCase       ,		'do_convert_rgb'      )      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							# Initialize image_processor
							__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=__lowerCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(__lowerCAmelCase       ,		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=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

													# Test batched
													__snake_case						 =   image_processor(
													    __lowerCAmelCase       ,		return_tensors='pt'       ,		max_patches=__lowerCAmelCase      ).flattened_patches
													self.assertEqual(
													    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Tuple       ,		*__lowerCAmelCase     :  List[str]       ,		**__lowerCAmelCase     :  List[str]      ):
							warnings.warn(
							    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use VideoMAEImageProcessor instead.'       ,		__lowerCAmelCase       ,		)
							super().__init__(*__lowerCAmelCase       ,		**__lowerCAmelCase      )

