
'''simple docstring'''

import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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


def 		_UpperCAmelCase			(					self  :    Dict					, _lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Dict						)      ->  Optional[int]:
		_lowerCAmelCase    :					int							=   image.to(device=_lowerCamelCase						)
		_lowerCAmelCase    :					str							=   image  # take image as image_embeddings
		_lowerCAmelCase    :					List[str]							=   image_embeddings.unsqueeze(1						)

		# duplicate image embeddings for each generation per prompt, using mps friendly method
		_lowerCAmelCase    :					List[str]							=   image_embeddings.shape
		_lowerCAmelCase    :					Dict							=   image_embeddings.repeat(1					, _lowerCamelCase					, 1						)
		_lowerCAmelCase    :					int							=   image_embeddings.view(bs_embed * num_images_per_prompt					, _lowerCamelCase					, -1						)

		if do_classifier_free_guidance:
				_lowerCAmelCase    :					str							=   torch.zeros_like(_lowerCamelCase						)

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				_lowerCAmelCase    :					List[Any]							=   torch.cat([uncond_embeddings, image_embeddings]						)

		return image_embeddings



class 					a_					(snake_case__      ):




							def __init__(       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = None				,				):
									super().__init__()

									_lowerCAmelCase    :					Union[str, Any]							=   {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									_lowerCAmelCase    :					Dict							=   decoder_pipe_kwargs.get("""torch_dtype"""				,				_A					) or prior.dtype

									_lowerCAmelCase    :					Any							=   StableDiffusionImageVariationPipeline.from_pretrained(
									    """lambdalabs/sd-image-variations-diffusers"""				,				**_A					)

									# replace `_encode_image` method
									_lowerCAmelCase    :					int							=   types.MethodType(_encode_image				,				self.decoder_pipe					)

									self.register_modules(
									    prior=_A				,				tokenizer=_A				,				text_encoder=_A				,				prior_scheduler=_A				,				)




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = None				,				snake_case_ = None				,				):
									if text_model_output is None:
											_lowerCAmelCase    :					Dict							=   len(_A					) if isinstance(_A				,				_A					) else 1
											# get prompt text embeddings
											_lowerCAmelCase    :					Union[str, Any]							=   self.tokenizer(
											    _A				,				padding="""max_length"""				,				max_length=self.tokenizer.model_max_length				,				return_tensors="""pt"""				,				)
											_lowerCAmelCase    :					List[str]							=   text_inputs.input_ids
											_lowerCAmelCase    :					Tuple							=   text_inputs.attention_mask.bool().to(_A					)

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													_lowerCAmelCase    :					int							=   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
													logger.warning(
													    """The following part of your input was truncated because CLIP can only handle sequences up to"""
													    f' {self.tokenizer.model_max_length} tokens: {removed_text}'					)
													_lowerCAmelCase    :					Union[str, Any]							=   text_input_ids[:, : self.tokenizer.model_max_length]

											_lowerCAmelCase    :					List[str]							=   self.text_encoder(text_input_ids.to(_A					)					)

											_lowerCAmelCase    :					int							=   text_encoder_output.text_embeds
											_lowerCAmelCase    :					Any							=   text_encoder_output.last_hidden_state

									else:
											_lowerCAmelCase    :					List[str]							=   text_model_output[0].shape[0]
											_lowerCAmelCase    :					int							=   text_model_output[0], text_model_output[1]
											_lowerCAmelCase    :					List[str]							=   text_attention_mask

									_lowerCAmelCase    :					Union[str, Any]							=   text_embeddings.repeat_interleave(_A				,				dim=0					)
									_lowerCAmelCase    :					Union[str, Any]							=   text_encoder_hidden_states.repeat_interleave(_A				,				dim=0					)
									_lowerCAmelCase    :					Tuple							=   text_mask.repeat_interleave(_A				,				dim=0					)

									if do_classifier_free_guidance:
											_lowerCAmelCase    :					Dict							=   [''] * batch_size

											_lowerCAmelCase    :					Optional[Any]							=   self.tokenizer(
											    _A				,				padding="""max_length"""				,				max_length=self.tokenizer.model_max_length				,				truncation=_A				,				return_tensors="""pt"""				,				)
											_lowerCAmelCase    :					Tuple							=   uncond_input.attention_mask.bool().to(_A					)
											_lowerCAmelCase    :					str							=   self.text_encoder(uncond_input.input_ids.to(_A					)					)

											_lowerCAmelCase    :					Any							=   uncond_embeddings_text_encoder_output.text_embeds
											_lowerCAmelCase    :					Dict							=   uncond_embeddings_text_encoder_output.last_hidden_state

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

											_lowerCAmelCase    :					Union[str, Any]							=   uncond_embeddings.shape[1]
											_lowerCAmelCase    :					str							=   uncond_embeddings.repeat(1				,				_A					)
											_lowerCAmelCase    :					Optional[Any]							=   uncond_embeddings.view(batch_size * num_images_per_prompt				,				_A					)

											_lowerCAmelCase    :					List[Any]							=   uncond_text_encoder_hidden_states.shape[1]
											_lowerCAmelCase    :					int							=   uncond_text_encoder_hidden_states.repeat(1				,				_A				,				1					)
											_lowerCAmelCase    :					int							=   uncond_text_encoder_hidden_states.view(
											    batch_size * num_images_per_prompt				,				_A				,				-1					)
											_lowerCAmelCase    :					int							=   uncond_text_mask.repeat_interleave(_A				,				dim=0					)

											# done duplicates

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											_lowerCAmelCase    :					Any							=   torch.cat([uncond_embeddings, text_embeddings]					)
											_lowerCAmelCase    :					str							=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

											_lowerCAmelCase    :					Dict							=   torch.cat([uncond_text_mask, text_mask]					)

									return text_embeddings, text_encoder_hidden_states, text_mask




							@property
							def 							__UpperCamelCase    (       self					):
									if self.device != torch.device("""meta"""					) or not hasattr(self.prior				,				"""_hf_hook"""					):
											return self.device
									for module in self.prior.modules():
											if (
											    hasattr(_A				,				"""_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




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
									if latents is None:
											_lowerCAmelCase    :					Optional[Any]							=   randn_tensor(_A				,				generator=_A				,				device=_A				,				dtype=_A					)
									else:
											if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'					)
											_lowerCAmelCase    :					Union[str, Any]							=   latents.to(_A					)

									_lowerCAmelCase    :					Tuple							=   latents * scheduler.init_noise_sigma
									return latents




							def 							__UpperCamelCase    (       self				,				snake_case_ = None					):
									self.decoder_pipe.to(_A					)
									super().to(_A					)







							@torch.no_grad()
							def __call__(       self				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = 1				,				snake_case_ = 2_5				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = 4.0				,				snake_case_ = 8.0				,				snake_case_ = 5_0				,				snake_case_ = 1				,				snake_case_ = 0.0				,				snake_case_ = "pil"				,				snake_case_ = True				,				):
									if prompt is not None:
											if isinstance(_A				,				_A					):
													_lowerCAmelCase    :					Optional[Any]							=   1
											elif isinstance(_A				,				_A					):
													_lowerCAmelCase    :					Union[str, Any]							=   len(_A					)
											else:
													raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_A					)}'					)
									else:
											_lowerCAmelCase    :					Union[str, Any]							=   text_model_output[0].shape[0]

									_lowerCAmelCase    :					Tuple							=   self._execution_device

									_lowerCAmelCase    :					Union[str, Any]							=   batch_size * num_images_per_prompt

									_lowerCAmelCase    :					Optional[int]							=   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									_lowerCAmelCase    :					Any							=   self._encode_prompt(
									    _A				,				_A				,				_A				,				_A				,				_A				,				_A					)

									# prior

									self.prior_scheduler.set_timesteps(_A				,				device=_A					)
									_lowerCAmelCase    :					Union[str, Any]							=   self.prior_scheduler.timesteps

									_lowerCAmelCase    :					List[str]							=   self.prior.config.embedding_dim

									_lowerCAmelCase    :					Optional[Any]							=   self.prepare_latents(
									    (batch_size, embedding_dim)				,				text_embeddings.dtype				,				_A				,				_A				,				_A				,				self.prior_scheduler				,				)

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

											_lowerCAmelCase    :					List[Any]							=   self.prior(
											    _A				,				timestep=_A				,				proj_embedding=_A				,				encoder_hidden_states=_A				,				attention_mask=_A				,				).predicted_image_embedding

											if do_classifier_free_guidance:
													_lowerCAmelCase    :					Any							=   predicted_image_embedding.chunk(2					)
													_lowerCAmelCase    :					Optional[Any]							=   predicted_image_embedding_uncond + prior_guidance_scale * (
													    predicted_image_embedding_text - predicted_image_embedding_uncond
													)

											if i + 1 == prior_timesteps_tensor.shape[0]:
													_lowerCAmelCase    :					List[Any]							=   None
											else:
													_lowerCAmelCase    :					Any							=   prior_timesteps_tensor[i + 1]

											_lowerCAmelCase    :					List[str]							=   self.prior_scheduler.step(
											    _A				,				timestep=_A				,				sample=_A				,				generator=_A				,				prev_timestep=_A				,				).prev_sample

									_lowerCAmelCase    :					str							=   self.prior.post_process_latents(_A					)

									_lowerCAmelCase    :					List[str]							=   prior_latents

									_lowerCAmelCase    :					Optional[int]							=   self.decoder_pipe(
									    image=_A				,				height=_A				,				width=_A				,				num_inference_steps=_A				,				guidance_scale=_A				,				generator=_A				,				output_type=_A				,				return_dict=_A				,				num_images_per_prompt=_A				,				eta=_A				,				)
									return output




'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Union[str, Any]      = PaintByExamplePipeline
       __lowerCAmelCase      :			int      = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       __lowerCAmelCase      :			str      = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __lowerCAmelCase      :			Any      = frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




       def 							__UpperCamelCase    (       self					):
         torch.manual_seed(0					)
         _lowerCAmelCase    :					Dict							=   UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)				,				layers_per_block=2				,				sample_size=3_2				,				in_channels=9				,				out_channels=4				,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,				cross_attention_dim=3_2				,				)
         _lowerCAmelCase    :					List[Any]							=   PNDMScheduler(skip_prk_steps=snake_case_					)
         torch.manual_seed(0					)
         _lowerCAmelCase    :					List[Any]							=   AutoencoderKL(
             block_out_channels=[3_2, 6_4]				,				in_channels=3				,				out_channels=3				,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,				latent_channels=4				,				)
         torch.manual_seed(0					)
         _lowerCAmelCase    :					List[str]							=   CLIPVisionConfig(
             hidden_size=3_2				,				projection_dim=3_2				,				intermediate_size=3_7				,				layer_norm_eps=1E-05				,				num_attention_heads=4				,				num_hidden_layers=5				,				image_size=3_2				,				patch_size=4				,				)
         _lowerCAmelCase    :					Optional[int]							=   PaintByExampleImageEncoder(snake_case_				,				proj_size=3_2					)
         _lowerCAmelCase    :					Optional[int]							=   CLIPImageProcessor(crop_size=3_2				,				size=3_2					)

         _lowerCAmelCase    :					Union[str, Any]							=   {
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """image_encoder""": image_encoder,
             """safety_checker""": None,
             """feature_extractor""": feature_extractor,
         }
         return components




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Optional[int]							=   np.array(image.convert("""RGB"""					)					)
         _lowerCAmelCase    :					Any							=   image[None].transpose(0				,				3				,				1				,				2					)
         _lowerCAmelCase    :					List[str]							=   torch.from_numpy(snake_case_					).to(dtype=torch.floataa					) / 127.5 - 1.0
         return image




       def 							__UpperCamelCase    (       self				,				snake_case_="cpu"				,				snake_case_=0					):
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         _lowerCAmelCase    :					Tuple							=   floats_tensor((1, 3, 3_2, 3_2)				,				rng=random.Random(snake_case_					)					).to(snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   image.cpu().permute(0				,				2				,				3				,				1					)[0]
         _lowerCAmelCase    :					List[Any]							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((6_4, 6_4)					)
         _lowerCAmelCase    :					List[str]							=   Image.fromarray(np.uinta(image + 4					)					).convert("""RGB"""					).resize((6_4, 6_4)					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((3_2, 3_2)					)

         if str(snake_case_					).startswith("""mps"""					):
           _lowerCAmelCase    :					str							=   torch.manual_seed(snake_case_					)
         else:
           _lowerCAmelCase    :					Tuple							=   torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
         _lowerCAmelCase    :					str							=   {
             """example_image""": example_image,
             """image""": init_image,
             """mask_image""": mask_image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.get_dummy_components()

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Optional[Any]							=   PaintByExamplePipeline(**snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   pipe.to("""cpu"""					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         _lowerCAmelCase    :					List[str]							=   pipe(**snake_case_					)
         _lowerCAmelCase    :					Any							=   output.images

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

         assert image.shape == (1, 6_4, 6_4, 3)
         _lowerCAmelCase    :					Optional[Any]							=   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]					)

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   """cpu"""
         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         inputs.pop("""mask_image"""					)
         _lowerCAmelCase    :					Optional[int]							=   self.convert_to_pt(inputs.pop("""image"""					)					)
         _lowerCAmelCase    :					int							=   image.clamp(0				,				1					) / 2

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					List[Any]							=   PaintByExamplePipeline(**self.get_dummy_components()					)
         _lowerCAmelCase    :					Optional[int]							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   pipe(image=snake_case_				,				mask_image=mask_image[:, 0]				,				**snake_case_					)
         _lowerCAmelCase    :					str							=   output.images

         _lowerCAmelCase    :					List[str]							=   image.cpu().permute(0				,				2				,				3				,				1					)[0]
         _lowerCAmelCase    :					List[str]							=   mask_image.cpu().permute(0				,				2				,				3				,				1					)[0]

         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)

         _lowerCAmelCase    :					str							=   pipe(**self.get_dummy_inputs()					)
         _lowerCAmelCase    :					int							=   output.images

         assert out_a.shape == (1, 6_4, 6_4, 3)
         assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







       def 							__UpperCamelCase    (       self					):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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




       def 							__UpperCamelCase    (       self					):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







       def 							__UpperCamelCase    (       self					):
         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Union[str, Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/dog_in_bucket.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/mask.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/panda.jpg"""					)

         _lowerCAmelCase    :					str							=   PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""					)
         _lowerCAmelCase    :					int							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   torch.manual_seed(3_2_1					)
         _lowerCAmelCase    :					Optional[int]							=   pipe(
             image=snake_case_				,				mask_image=snake_case_				,				example_image=snake_case_				,				generator=snake_case_				,				guidance_scale=5.0				,				num_inference_steps=5_0				,				output_type="""np"""				,				)

         _lowerCAmelCase    :					List[str]							=   output.images

         _lowerCAmelCase    :					List[str]							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase    :					Any							=   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]					)

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


