





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:
			__UpperCamelCase:	Any = False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor


class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Dict=7,					lowerCamelCase_:						List[str]=3,					lowerCamelCase_:						Dict=18,					lowerCamelCase_:						Union[str, Any]=30,					lowerCamelCase_:						Tuple=400,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[int]=True,					lowerCamelCase_:						int=True,					lowerCamelCase_:						int=None,					):
			lowercase__					:      int      =		size if size is not None else {'height': 20, 'width': 20}
			lowercase__					:      Optional[Any]      =		parent
			lowercase__					:      str      =		batch_size
			lowercase__					:      Union[str, Any]      =		num_channels
			lowercase__					:      Optional[int]      =		image_size
			lowercase__					:      List[str]      =		min_resolution
			lowercase__					:      List[str]      =		max_resolution
			lowercase__					:      Any      =		size
			lowercase__					:      Dict      =		do_normalize
			lowercase__					:      List[Any]      =		do_convert_rgb
			lowercase__					:      List[Any]      =		[512, 1024, 2048, 4096]
			lowercase__					:      List[str]      =		patch_size if patch_size is not None else {'height': 16, 'width': 16}

	def    snake_case__(						self:						Dict			):
			return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





	def    snake_case__(						self:						Tuple			):
			lowercase__					:      Dict      =		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
			lowercase__					:      str      =		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       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				PixaStructImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      Optional[Any]      =		PixaStructImageProcessingTester(self			)

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return self.image_processor_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      List[Any]      =		self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_normalize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_convert_rgb'			)			)

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      Union[str, Any]      =		self.image_processor_tester.prepare_dummy_image()

			lowercase__					:      Tuple      =		self.image_processing_class(**self.image_processor_dict			)
			lowercase__					:      str      =		2048

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

	def    snake_case__(						self:						Union[str, Any]			):
			# Initialize image_processor
			lowercase__					:      Optional[Any]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      int      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					Image.Image			)

			# Test not batched input
			lowercase__					:      int      =		(
			    (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__					:      Tuple      =		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
					lowercase__					:      Any      =		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    snake_case__(						self:						str			):
			# Initialize image_processor
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      Any      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					Image.Image			)

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

			lowercase__					:      Tuple      =		True

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

					lowercase__					:      Any      =		'Hello'

					lowercase__					:      str      =		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
					lowercase__					:      List[Any]      =		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    snake_case__(						self:						Tuple			):
			# Initialize image_processor
			lowercase__					:      Dict      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      Optional[Any]      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					numpify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					np.ndarray			)

			lowercase__					:      List[Any]      =		(
			    (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__					:      Optional[int]      =		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
					lowercase__					:      str      =		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    snake_case__(						self:						Optional[Any]			):
			# Initialize image_processor
			lowercase__					:      Union[str, Any]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      List[str]      =		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
			lowercase__					:      Any      =		(
			    (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__					:      Union[str, Any]      =		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
					lowercase__					:      Optional[Any]      =		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       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				PixaStructImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      Tuple      =		PixaStructImageProcessingTester(self,					num_channels=4			)
			lowercase__					:      Union[str, Any]      =		3

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

	def    snake_case__(						self:						str			):
			lowercase__					:      int      =		self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_normalize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_convert_rgb'			)			)





	def    snake_case__(						self:						Dict			):
			# Initialize image_processor
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      List[str]      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					Image.Image			)

			# Test not batched input
			lowercase__					:      Union[str, Any]      =		(
			    (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__					:      Union[str, Any]      =		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
					lowercase__					:      int      =		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),					)









from ..utils import DummyObject, requires_backends


class       __lowerCAmelCase					(       metaclass=_UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["note_seq"]

	def __init__(						self:						int,					*lowerCamelCase_:						Optional[int],					**lowerCamelCase_:						Tuple			):
			requires_backends(self,					['note_seq']			)

	@classmethod
	def    snake_case__(						cls:						Optional[Any],					*lowerCamelCase_:						Tuple,					**lowerCamelCase_:						Optional[Any]			):
			requires_backends(cls,					['note_seq']			)





	@classmethod
	def    snake_case__(						cls:						Any,					*lowerCamelCase_:						Dict,					**lowerCamelCase_:						str			):
			requires_backends(cls,					['note_seq']			)


