






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      :      Optional[Any]     						=						False

if is_vision_available():
						from PIL import Image

						from transformers import PixaStructImageProcessor


class        __UpperCamelCase    (     unittest.TestCase   ):







	def __init__(  self      :							Any       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							int=7       ,				_lowerCAmelCase      :							Optional[int]=3       ,				_lowerCAmelCase      :							str=18       ,				_lowerCAmelCase      :							List[Any]=30       ,				_lowerCAmelCase      :							Dict=400       ,				_lowerCAmelCase      :							List[Any]=None       ,				_lowerCAmelCase      :							Optional[int]=True       ,				_lowerCAmelCase      :							Dict=True       ,				_lowerCAmelCase      :							int=None       ,				)		-> Optional[Any]:
		"""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  _a   (  self      :							str			)		-> Tuple:
		"""simple docstring"""







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







	def  _a   (  self      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
		__lowercase             =  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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:List[str]   =       PixaStructImageProcessor if is_vision_available() else None







	def  _a   (  self      :							List[str]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  PixaStructImageProcessingTester(self			)







	@property
	def  _a   (  self      :							Union[str, Any]			)		-> Optional[Any]:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""do_normalize"""			)			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""do_convert_rgb"""			)			)







	def  _a   (  self      :							Tuple			)		-> 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(_lowerCAmelCase       ,				return_tensors="""pt"""       ,				max_patches=_lowerCAmelCase			)
		self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,				torch.tensor(0.0_606			)       ,				atol=1e-3       ,				rtol=1e-3			)			)







	def  _a   (  self      :							Any			)		-> 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=_lowerCAmelCase			)
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase       ,				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=_lowerCAmelCase			).flattened_patches
			self.assertEqual(
			    encoded_images.shape       ,				(1, max_patch, expected_hidden_dim)       ,				)

			# Test batched
			__lowercase             =  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  _a   (  self      :							List[Any]			)		-> Union[str, 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=_lowerCAmelCase			)
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase       ,				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(_lowerCAmelCase			):
				__lowercase             =  image_processor(
				    image_inputs[0]       ,				return_tensors="""pt"""       ,				max_patches=_lowerCAmelCase			).flattened_patches

			__lowercase             =  """Hello"""

			__lowercase             =  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             =  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  _a   (  self      :							List[str]			)		-> List[str]:
		"""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=_lowerCAmelCase       ,				numpify=_lowerCAmelCase			)
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase       ,				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=_lowerCAmelCase			).flattened_patches
			self.assertEqual(
			    encoded_images.shape       ,				(1, max_patch, expected_hidden_dim)       ,				)

			# Test batched
			__lowercase             =  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  _a   (  self      :							List[str]			)		-> Optional[int]:
		"""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=_lowerCAmelCase       ,				torchify=_lowerCAmelCase			)
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase       ,				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=_lowerCAmelCase			).flattened_patches
			self.assertEqual(
			    encoded_images.shape       ,				(1, max_patch, expected_hidden_dim)       ,				)

			# Test batched
			__lowercase             =  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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:int   =       PixaStructImageProcessor if is_vision_available() else None







	def  _a   (  self      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







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







	@property
	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							Any			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""do_normalize"""			)			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""do_convert_rgb"""			)			)







	def  _a   (  self      :							Dict			)		-> int:
		"""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=_lowerCAmelCase			)
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase       ,				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=_lowerCAmelCase			).flattened_patches
			self.assertEqual(
			    encoded_images.shape       ,				(1, max_patch, expected_hidden_dim)       ,				)

			# Test batched
			__lowercase             =  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)       ,				)









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       IFInpaintingPipeline
	__snake_case	:str   =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	__snake_case	:Optional[int]   =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case	:str   =       PipelineTesterMixin.required_optional_params - {'latents'}







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._get_dummy_components()







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict=0			)		-> Any:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)

		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)
		__lowercase             =  floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(_lowerCAmelCase			)			).to(_lowerCAmelCase			)

		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """mask_image""": mask_image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}

		return inputs







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()       ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,				)
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		self._test_save_load_optional_components()







	@unittest.skipIf(torch_device != """cuda"""       ,				reason="""float16 requires CUDA"""			)
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		super().test_save_load_floataa(expected_max_diff=1e-1			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		self._test_save_load_local()







	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,				)

