






'''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:
		__lowerCAmelCase       		=      False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Any:
							a_		:  Tuple											=      '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
							a_		:  List[str]											=      Image.open(requests.get(__SCREAMING_SNAKE_CASE  ,						stream=__SCREAMING_SNAKE_CASE					).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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				PixaStructImageProcessor if is_vision_available() else None






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Dict:
							a_		:  List[str]											=      PixaStructImageProcessingTester(self					)






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			List[str]:
							a_		:  Union[str, Any]											=      self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''do_normalize'''					)					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''do_convert_rgb'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Tuple:
							a_		:  Union[str, Any]											=      self.image_processor_tester.prepare_dummy_image()

							a_		:  Tuple											=      self.image_processing_class(**self.image_processor_dict					)
							a_		:  int											=      2048

							a_		:  Tuple											=      image_processor(__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,						torch.tensor(0.0606					)  ,						atol=1e-3  ,						rtol=1e-3					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Tuple:
							# Initialize image_processor
							a_		:  str											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a_		:  Optional[Any]											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						Image.Image					)

							# Test not batched input
							a_		:  Dict											=      (
							    (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
											a_		:  int											=      image_processor(
											    image_inputs[0]  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(1, max_patch, expected_hidden_dim)  ,						)

											# Test batched
											a_		:  List[Any]											=      image_processor(
											    __SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,						)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							# Initialize image_processor
							a_		:  List[str]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a_		:  Optional[int]											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						Image.Image					)

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

							a_		:  Any											=      True

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

											a_		:  str											=      '''Hello'''

											a_		:  Optional[int]											=      image_processor(
											    image_inputs[0]  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE  ,						header_text=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(1, max_patch, expected_hidden_dim)  ,						)

											# Test batched
											a_		:  str											=      image_processor(
											    __SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE  ,						header_text=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,						)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[int]:
							# Initialize image_processor
							a_		:  Optional[int]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							a_		:  List[Any]											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE  ,						numpify=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					)

							a_		:  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
											a_		:  Optional[Any]											=      image_processor(
											    image_inputs[0]  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(1, max_patch, expected_hidden_dim)  ,						)

											# Test batched
											a_		:  Union[str, Any]											=      image_processor(
											    __SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,						)







			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[str]:
							# Initialize image_processor
							a_		:  Any											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							a_		:  Optional[Any]											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE  ,						torchify=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)

							# Test not batched input
							a_		:  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
											a_		:  Union[str, Any]											=      image_processor(
											    image_inputs[0]  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(1, max_patch, expected_hidden_dim)  ,						)

											# Test batched
											a_		:  Tuple											=      image_processor(
											    __SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				PixaStructImageProcessor if is_vision_available() else None






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Any:
							a_		:  Optional[Any]											=      PixaStructImageProcessingTester(self  ,						num_channels=4					)
							a_		:  Tuple											=      3






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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Optional[int]:
							a_		:  Optional[int]											=      self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''do_normalize'''					)					)
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,						'''do_convert_rgb'''					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Optional[Any]:
							# Initialize image_processor
							a_		:  Optional[int]											=      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							a_		:  Dict											=      prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__SCREAMING_SNAKE_CASE					)
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						Image.Image					)

							# Test not batched input
							a_		:  int											=      (
							    (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
											a_		:  Any											=      image_processor(
											    image_inputs[0]  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(1, max_patch, expected_hidden_dim)  ,						)

											# Test batched
											a_		:  Dict											=      image_processor(
											    __SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						max_patches=__SCREAMING_SNAKE_CASE					).flattened_patches
											self.assertEqual(
											    encoded_images.shape  ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,						)












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				DDIMPipeline
			snake_case__  =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			snake_case__  =				PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "latents",
			    "callback",
			    "callback_steps",
			}
			snake_case__  =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			snake_case__  =				False






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[Any]:
							torch.manual_seed(0					)
							a_		:  int											=      UNetaDModel(
							    block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,						)
							a_		:  str											=      DDIMScheduler()
							a_		:  Union[str, Any]											=      {'''unet''': unet, '''scheduler''': scheduler}
							return components






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Tuple=0					)			->			str:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Dict											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  Union[str, Any]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      {
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''

							a_		:  List[Any]											=      self.get_dummy_components()
							a_		:  List[str]											=      self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
							pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      pipe(**__SCREAMING_SNAKE_CASE					).images
							a_		:  List[str]											=      image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,						(1, 32, 32, 3)					)
							a_		:  int											=      np.array(
							    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]					)
							a_		:  Union[str, Any]											=      np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(__SCREAMING_SNAKE_CASE  ,						1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[int]:
							super().test_save_load_local(expected_max_difference=3e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			List[str]:
							super().test_save_load_optional_components(expected_max_difference=3e-3					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[int]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Any:
							a_		:  Optional[Any]											=      '''google/ddpm-cifar10-32'''

							a_		:  Optional[Any]											=      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      DDIMScheduler()

							a_		:  List[str]											=      DDIMPipeline(unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							ddim.to(__SCREAMING_SNAKE_CASE					)
							ddim.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      torch.manual_seed(0					)
							a_		:  Tuple											=      ddim(generator=__SCREAMING_SNAKE_CASE  ,						eta=0.0  ,						output_type='''numpy'''					).images

							a_		:  List[Any]											=      image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							a_		:  List[str]											=      np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							a_		:  int											=      '''google/ddpm-ema-bedroom-256'''

							a_		:  str											=      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					)

							a_		:  Any											=      DDIMPipeline(unet=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							ddpm.to(__SCREAMING_SNAKE_CASE					)
							ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Tuple											=      torch.manual_seed(0					)
							a_		:  List[Any]											=      ddpm(generator=__SCREAMING_SNAKE_CASE  ,						output_type='''numpy'''					).images

							a_		:  Optional[int]											=      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							a_		:  Optional[Any]											=      np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




