





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					=    False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase=7     ,_lowerCamelCase=3     ,_lowerCamelCase=18     ,_lowerCamelCase=30     ,_lowerCamelCase=400     ,_lowerCamelCase=None     ,_lowerCamelCase=True     ,_lowerCamelCase=True     ,_lowerCamelCase=None     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							size if size is not None else {'''height''': 20, '''width''': 20}
				__lowercase										=							parent
				__lowercase										=							batch_size
				__lowercase										=							num_channels
				__lowercase										=							image_size
				__lowercase										=							min_resolution
				__lowercase										=							max_resolution
				__lowercase										=							size
				__lowercase										=							do_normalize
				__lowercase										=							do_convert_rgb
				__lowercase										=							[512, 1024, 2048, 4096]
				__lowercase										=							patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
				__lowercase										=							Image.open(requests.get(lowercase_     ,stream=lowercase_ ).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 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				Tuple											=       PixaStructImageProcessor if is_vision_available() else None


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







				'''simple docstring'''





				__lowercase										=							PixaStructImageProcessingTester(self )


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(lowercase_     ,'''do_normalize''' ) )
				self.assertTrue(hasattr(lowercase_     ,'''do_convert_rgb''' ) )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.image_processor_tester.prepare_dummy_image()

				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				__lowercase										=							2048

				__lowercase										=							image_processor(lowercase_     ,return_tensors='''pt'''     ,max_patches=lowercase_ )
				self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,torch.tensor(0.0_6_0_6 )     ,atol=1E-3     ,rtol=1E-3 ) )


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





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

				# Test not batched input
				__lowercase										=							(
				    (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
							__lowercase										=							image_processor(
							    image_inputs[0]     ,return_tensors='''pt'''     ,max_patches=lowercase_ ).flattened_patches
							self.assertEqual(
							    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

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


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





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

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

				__lowercase										=							True

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

							__lowercase										=							'''Hello'''

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

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


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







				'''simple docstring'''





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

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

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


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





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

				# Test not batched input
				__lowercase										=							(
				    (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
							__lowercase										=							image_processor(
							    image_inputs[0]     ,return_tensors='''pt'''     ,max_patches=lowercase_ ).flattened_patches
							self.assertEqual(
							    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

							# Test batched
							__lowercase										=							image_processor(
							    lowercase_     ,return_tensors='''pt'''     ,max_patches=lowercase_ ).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 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				Tuple											=       PixaStructImageProcessor if is_vision_available() else None


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







				'''simple docstring'''





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


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







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


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







				'''simple docstring'''





				__lowercase										=							self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(lowercase_     ,'''do_normalize''' ) )
				self.assertTrue(hasattr(lowercase_     ,'''do_convert_rgb''' ) )


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







				'''simple docstring'''





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

				# Test not batched input
				__lowercase										=							(
				    (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
							__lowercase										=							image_processor(
							    image_inputs[0]     ,return_tensors='''pt'''     ,max_patches=lowercase_ ).flattened_patches
							self.assertEqual(
							    encoded_images.shape     ,(1, max_patch, expected_hidden_dim)     ,)

							# Test batched
							__lowercase										=							image_processor(
							    lowercase_     ,return_tensors='''pt'''     ,max_patches=lowercase_ ).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_glpn import GLPNImageProcessor


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							None:







				'''simple docstring'''





				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''     ,_lowerCamelCase     ,)
				super().__init__(*_lowerCamelCase     ,**_lowerCamelCase )





