


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        lowerCamelCase      (     unittest.TestCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__=7							,    lowercase__=3							,    lowercase__=1_8							,    lowercase__=3_0							,    lowercase__=4_0_0							,    lowercase__=None							,    lowercase__=True							,    lowercase__=True							,    lowercase__=None							,    ):
							__UpperCAmelCase :       Any  						=  size if size is not None else {'''height''': 2_0, '''width''': 2_0}
							__UpperCAmelCase :       str  						=  parent
							__UpperCAmelCase :       Optional[int]  						=  batch_size
							__UpperCAmelCase :       Dict  						=  num_channels
							__UpperCAmelCase :       Optional[int]  						=  image_size
							__UpperCAmelCase :       Any  						=  min_resolution
							__UpperCAmelCase :       Optional[int]  						=  max_resolution
							__UpperCAmelCase :       str  						=  size
							__UpperCAmelCase :       List[str]  						=  do_normalize
							__UpperCAmelCase :       int  						=  do_convert_rgb
							__UpperCAmelCase :       Any  						=  [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
							__UpperCAmelCase :       Any  						=  patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}


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


	def    A(						self):
							__UpperCAmelCase :       Dict  						=  '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
							__UpperCAmelCase :       Union[str, Any]  						=  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        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Optional[int]        =	PixaStructImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  PixaStructImageProcessingTester(self)


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       str  						=  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__							,    '''do_normalize'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_convert_rgb'''))


	def    A(						self):
							__UpperCAmelCase :       Dict  						=  self.image_processor_tester.prepare_dummy_image()

							__UpperCAmelCase :       List[str]  						=  self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase :       Optional[Any]  						=  2_0_4_8

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

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

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

							__UpperCAmelCase :       Tuple  						=  True

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

													__UpperCAmelCase :       Union[str, Any]  						=  '''Hello'''

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

							__UpperCAmelCase :       Optional[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
													__UpperCAmelCase :       str  						=  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
													__UpperCAmelCase :       str  						=  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    A(						self):
							# Initialize image_processor
							__UpperCAmelCase :       Dict  						=  self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							__UpperCAmelCase :       Tuple  						=  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
							__UpperCAmelCase :       Tuple  						=  (
							    (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
													__UpperCAmelCase :       Union[str, Any]  						=  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
													__UpperCAmelCase :       Optional[Any]  						=  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        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Union[str, Any]        =	PixaStructImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Dict  						=  PixaStructImageProcessingTester(self							,    num_channels=4)
							__UpperCAmelCase :       str  						=  3


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       int  						=  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__							,    '''do_normalize'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_convert_rgb'''))


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

							# Test not batched input
							__UpperCAmelCase :       List[str]  						=  (
							    (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
													__UpperCAmelCase :       str  						=  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
													__UpperCAmelCase :       Dict  						=  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)							,    )





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCAmelCase							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase							=						"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=8			)		->    List[Any]:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__UpperCAmelCase :       Union[str, Any]  						=  width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
							super().__init__()

							self.register_modules(
							    unet=lowercase__							,    scheduler=lowercase__							,    movq=lowercase__							,    )
							__UpperCAmelCase :       Any  						=  2 ** (len(self.movq.config.block_out_channels) - 1)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__):
							if latents is None:
													__UpperCAmelCase :       Any  						=  randn_tensor(lowercase__							,    generator=lowercase__							,    device=lowercase__							,    dtype=lowercase__)
							else:
													if latents.shape != shape:
																			raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
													__UpperCAmelCase :       Union[str, Any]  						=  latents.to(lowercase__)

							__UpperCAmelCase :       Union[str, Any]  						=  latents * scheduler.init_noise_sigma
							return latents


	def    A(						self							,    lowercase__=0):
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('''Please install accelerate via `pip install accelerate`''')

							__UpperCAmelCase :       List[str]  						=  torch.device(F"cuda:{gpu_id}")

							__UpperCAmelCase :       List[Any]  						=  [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(lowercase__							,    lowercase__)


	def    A(						self							,    lowercase__=0):
							if is_accelerate_available() and is_accelerate_version('''>='''							,    '''0.17.0.dev0'''):
													from accelerate import cpu_offload_with_hook
							else:
													raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''')

							__UpperCAmelCase :       Optional[Any]  						=  torch.device(F"cuda:{gpu_id}")

							if self.device.type != "cpu":
													self.to('''cpu'''							,    silence_dtype_warnings=lowercase__)
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							__UpperCAmelCase :       List[Any]  						=  None
							for cpu_offloaded_model in [self.unet, self.movq]:
													__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  cpu_offload_with_hook(lowercase__							,    lowercase__							,    prev_module_hook=lowercase__)

							# We'll offload the last model manually.
							__UpperCAmelCase :       Any  						=  hook


	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    A(						self):
							if not hasattr(self.unet							,    '''_hf_hook'''):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(lowercase__							,    '''_hf_hook''')
													    and hasattr(module._hf_hook							,    '''execution_device''')
													    and module._hf_hook.execution_device is not None
													):
																			return torch.device(module._hf_hook.execution_device)
							return self.device


	@torch.no_grad()
	@replace_example_docstring(lowercase__)
	def __call__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = 5_1_2							,    lowercase__ = 5_1_2							,    lowercase__ = 1_0_0							,    lowercase__ = 4.0							,    lowercase__ = 1							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "pil"							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       str  						=  self._execution_device

							__UpperCAmelCase :       List[str]  						=  guidance_scale > 1.0

							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Dict  						=  torch.cat(lowercase__							,    dim=0)
							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Tuple  						=  torch.cat(lowercase__							,    dim=0)
							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Any  						=  torch.cat(lowercase__							,    dim=0)

							__UpperCAmelCase :       Union[str, Any]  						=  image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
													__UpperCAmelCase :       Optional[int]  						=  image_embeds.repeat_interleave(lowercase__							,    dim=0)
													__UpperCAmelCase :       Dict  						=  negative_image_embeds.repeat_interleave(lowercase__							,    dim=0)
													__UpperCAmelCase :       List[Any]  						=  hint.repeat_interleave(lowercase__							,    dim=0)

							__UpperCAmelCase :       Tuple  						=  torch.cat([negative_image_embeds, image_embeds]							,    dim=0).to(dtype=self.unet.dtype							,    device=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  torch.cat([hint, hint]							,    dim=0).to(dtype=self.unet.dtype							,    device=lowercase__)
							self.scheduler.set_timesteps(lowercase__							,    device=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  self.scheduler.timesteps

							__UpperCAmelCase :       Any  						=  self.movq.config.latent_channels

							__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  downscale_height_and_width(lowercase__							,    lowercase__							,    self.movq_scale_factor)

							# create initial latent
							__UpperCAmelCase :       Union[str, Any]  						=  self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)							,    image_embeds.dtype							,    lowercase__							,    lowercase__							,    lowercase__							,    self.scheduler							,    )

							for i, t in enumerate(self.progress_bar(lowercase__)):
													# expand the latents if we are doing classifier free guidance
													__UpperCAmelCase :       List[Any]  						=  torch.cat([latents] * 2) if do_classifier_free_guidance else latents

													__UpperCAmelCase :       Union[str, Any]  						=  {'''image_embeds''': image_embeds, '''hint''': hint}
													__UpperCAmelCase :       Any  						=  self.unet(
													    sample=lowercase__							,    timestep=lowercase__							,    encoder_hidden_states=lowercase__							,    added_cond_kwargs=lowercase__							,    return_dict=lowercase__							,    )[0]

													if do_classifier_free_guidance:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred.split(latents.shape[1]							,    dim=1)
																			__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  noise_pred.chunk(2)
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  variance_pred.chunk(2)
																			__UpperCAmelCase :       Union[str, Any]  						=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__UpperCAmelCase :       int  						=  torch.cat([noise_pred, variance_pred_text]							,    dim=1)

													if not (
													    hasattr(self.scheduler.config							,    '''variance_type''')
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred.split(latents.shape[1]							,    dim=1)

													# compute the previous noisy sample x_t -> x_t-1
													__UpperCAmelCase :       Tuple  						=  self.scheduler.step(
													    lowercase__							,    lowercase__							,    lowercase__							,    generator=lowercase__							,    )[0]
							# post-processing
							__UpperCAmelCase :       str  						=  self.movq.decode(lowercase__							,    force_not_quantize=lowercase__)['''sample''']

							if output_type not in ["pt", "np", "pil"]:
													raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")

							if output_type in ["np", "pil"]:
													__UpperCAmelCase :       Dict  						=  image * 0.5 + 0.5
													__UpperCAmelCase :       Union[str, Any]  						=  image.clamp(0							,    1)
													__UpperCAmelCase :       List[str]  						=  image.cpu().permute(0							,    2							,    3							,    1).float().numpy()

							if output_type == "pil":
													__UpperCAmelCase :       List[str]  						=  self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowercase__)

