






'''simple docstring'''







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class _lowercase      (							_lowercase						):




							def __init__(				self:							Dict     ,				UpperCamelCase__:							AutoencoderKL     ,				UpperCamelCase__:							CLIPTextModel     ,				UpperCamelCase__:							CLIPTokenizer     ,				UpperCamelCase__:							UNetaDConditionModel     ,				UpperCamelCase__:							DDIMScheduler     ,				UpperCamelCase__:							float = 0.93     ,				UpperCamelCase__:							bool = True     ,				):
														lowerCamelCase__   :      Tuple			    =			mixing_coeff
														lowerCamelCase__   :      Tuple			    =			leapfrog_steps

														super().__init__()
														self.register_modules(
														    vae=UpperCamelCase__     ,				text_encoder=UpperCamelCase__     ,				tokenizer=UpperCamelCase__     ,				unet=UpperCamelCase__     ,				scheduler=UpperCamelCase__     ,				)

														lowerCamelCase__   :      str			    =			2 ** (len(self.vae.config.block_out_channels	) - 1)
														lowerCamelCase__   :      int			    =			VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)




							def 						lowerCamelCase_							(				self:							Union[str, Any]     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							Optional[str] = None     ,				UpperCamelCase__:							bool = False	):
														lowerCamelCase__   :      List[Any]			    =			self.tokenizer(
														    UpperCamelCase__     ,				padding="""max_length"""     ,				max_length=self.tokenizer.model_max_length     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""     ,				)

														lowerCamelCase__   :      Optional[Any]			    =			self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

														lowerCamelCase__   :      Tuple			    =			prompt_embeds.to(dtype=self.text_encoder.dtype     ,				device=self.device	)

														if do_classifier_free_guidance:
																					lowerCamelCase__   :      Dict			    =			"""""" if negative_prompt is None else negative_prompt

																					lowerCamelCase__   :      int			    =			self.tokenizer(
																					    UpperCamelCase__     ,				padding="""max_length"""     ,				max_length=self.tokenizer.model_max_length     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""     ,				)

																					lowerCamelCase__   :      Optional[int]			    =			self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

																					lowerCamelCase__   :      Tuple			    =			torch.cat([negative_prompt_embeds, prompt_embeds]	)

														return prompt_embeds




							def 						lowerCamelCase_							(				self:							List[Any]     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor	):
														lowerCamelCase__   :      Any			    =			self.mixing_coeff * x + (1 - self.mixing_coeff) * y
														lowerCamelCase__   :      Dict			    =			self.mixing_coeff * y + (1 - self.mixing_coeff) * x

														return [x, y]




							def 						lowerCamelCase_							(				self:							Optional[Any]     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor	):
														lowerCamelCase__   :      Optional[int]			    =			(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
														lowerCamelCase__   :      Optional[Any]			    =			(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

														return [x, y]




							def 						lowerCamelCase_							(				self:							Union[str, Any]     ,				UpperCamelCase__:							torch.Tensor	):
														# as self.alphas_cumprod is always in cpu
														lowerCamelCase__   :      Optional[Any]			    =			int(UpperCamelCase__	)

														lowerCamelCase__   :      Any			    =			self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

														return alpha_prod, 1 - alpha_prod




							def 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				):
														lowerCamelCase__   :      List[str]			    =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

														lowerCamelCase__    ,						lowerCamelCase__   :      Dict			    =			self._get_alpha_and_beta(UpperCamelCase__	)
														lowerCamelCase__    ,						lowerCamelCase__   :      List[Any]			    =			self._get_alpha_and_beta(UpperCamelCase__	)

														lowerCamelCase__   :      Dict			    =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
														lowerCamelCase__   :      Union[str, Any]			    =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

														lowerCamelCase__   :      List[Any]			    =			(base - b_t * model_output) / a_t

														return model_input, next_model_input.to(base.dtype	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				):
														lowerCamelCase__   :      int			    =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

														lowerCamelCase__    ,						lowerCamelCase__   :      Optional[int]			    =			self._get_alpha_and_beta(UpperCamelCase__	)
														lowerCamelCase__    ,						lowerCamelCase__   :      Tuple			    =			self._get_alpha_and_beta(UpperCamelCase__	)

														lowerCamelCase__   :      Optional[int]			    =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
														lowerCamelCase__   :      str			    =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
														lowerCamelCase__   :      int			    =			a_t * base + b_t * model_output

														return model_input, next_model_input.to(base.dtype	)




							@torch.no_grad()
							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							torch.Tensor	):
														lowerCamelCase__   :      Optional[int]			    =			1 / self.vae.config.scaling_factor * latents
														lowerCamelCase__   :      Optional[Any]			    =			self.vae.decode(UpperCamelCase__	).sample
														lowerCamelCase__   :      Tuple			    =			(image / 2 + 0.5).clamp(0     ,				1	)
														return image




							@torch.no_grad()
							def 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							Image.Image     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							torch.Tensor     ,				UpperCamelCase__:							float     ,				UpperCamelCase__:							Optional[torch.Generator] = None     ,				):
														lowerCamelCase__   :      Union[str, Any]			    =			guidance_scale > 1.0

														lowerCamelCase__   :      Union[str, Any]			    =			image.to(device=self.device     ,				dtype=text_embeds.dtype	)
														lowerCamelCase__   :      List[str]			    =			self.vae.encode(UpperCamelCase__	).latent_dist.sample(UpperCamelCase__	)

														lowerCamelCase__   :      List[str]			    =			self.vae.config.scaling_factor * latent

														lowerCamelCase__   :      Dict			    =			[latent.clone(), latent.clone()]

														for i, t in tqdm(enumerate(UpperCamelCase__	)     ,				total=len(UpperCamelCase__	)	):
																					lowerCamelCase__   :      List[str]			    =			self.noise_mixing_layer(x=coupled_latents[0]     ,				y=coupled_latents[1]	)

																					# j - model_input index, k - base index
																					for j in range(2	):
																												lowerCamelCase__   :      Any			    =			j ^ 1

																												if self.leapfrog_steps:
																																			if i % 2 == 0:
																																										lowerCamelCase__    ,						lowerCamelCase__   :      Optional[Any]			    =			j, k

																												lowerCamelCase__   :      Optional[Any]			    =			coupled_latents[j]
																												lowerCamelCase__   :      int			    =			coupled_latents[k]

																												lowerCamelCase__   :      List[Any]			    =			torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

																												lowerCamelCase__   :      List[str]			    =			self.unet(UpperCamelCase__     ,				UpperCamelCase__     ,				encoder_hidden_states=UpperCamelCase__	).sample

																												if do_classifier_free_guidance:
																																			lowerCamelCase__    ,						lowerCamelCase__   :      Union[str, Any]			    =			noise_pred.chunk(2	)
																																			lowerCamelCase__   :      List[str]			    =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																												lowerCamelCase__    ,						lowerCamelCase__   :      str			    =			self.noise_step(
																												    base=UpperCamelCase__     ,				model_input=UpperCamelCase__     ,				model_output=UpperCamelCase__     ,				timestep=UpperCamelCase__     ,				)

																												lowerCamelCase__   :      Optional[Any]			    =			model_input

														return coupled_latents




							@torch.no_grad()
							def __call__(				self:							Dict     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							Image.Image     ,				UpperCamelCase__:							float = 3.0     ,				UpperCamelCase__:							int = 50     ,				UpperCamelCase__:							float = 0.8     ,				UpperCamelCase__:							Optional[str] = None     ,				UpperCamelCase__:							Optional[torch.Generator] = None     ,				UpperCamelCase__:							Optional[str] = "pil"     ,				):
														lowerCamelCase__   :      Union[str, Any]			    =			guidance_scale > 1.0

														lowerCamelCase__   :      List[str]			    =			self.image_processor.preprocess(UpperCamelCase__	)

														lowerCamelCase__   :      List[Any]			    =			self._encode_prompt(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)
														lowerCamelCase__   :      Union[str, Any]			    =			self._encode_prompt(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)

														self.scheduler.set_timesteps(UpperCamelCase__     ,				self.device	)

														lowerCamelCase__   :      Tuple			    =			num_inference_steps - int(num_inference_steps * strength	)
														lowerCamelCase__   :      Tuple			    =			self.scheduler.timesteps[t_limit:]
														lowerCamelCase__   :      Union[str, Any]			    =			fwd_timesteps.flip(0	)

														lowerCamelCase__   :      str			    =			self.prepare_latents(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)

														for i, t in tqdm(enumerate(UpperCamelCase__	)     ,				total=len(UpperCamelCase__	)	):
																					# j - model_input index, k - base index
																					for k in range(2	):
																												lowerCamelCase__   :      int			    =			k ^ 1

																												if self.leapfrog_steps:
																																			if i % 2 == 1:
																																										lowerCamelCase__    ,						lowerCamelCase__   :      Union[str, Any]			    =			j, k

																												lowerCamelCase__   :      Tuple			    =			coupled_latents[j]
																												lowerCamelCase__   :      int			    =			coupled_latents[k]

																												lowerCamelCase__   :      Any			    =			torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

																												lowerCamelCase__   :      Optional[int]			    =			self.unet(UpperCamelCase__     ,				UpperCamelCase__     ,				encoder_hidden_states=UpperCamelCase__	).sample

																												if do_classifier_free_guidance:
																																			lowerCamelCase__    ,						lowerCamelCase__   :      str			    =			noise_pred.chunk(2	)
																																			lowerCamelCase__   :      str			    =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																												lowerCamelCase__    ,						lowerCamelCase__   :      int			    =			self.denoise_step(
																												    base=UpperCamelCase__     ,				model_input=UpperCamelCase__     ,				model_output=UpperCamelCase__     ,				timestep=UpperCamelCase__     ,				)

																												lowerCamelCase__   :      Any			    =			model_input

																					lowerCamelCase__   :      Any			    =			self.denoise_mixing_layer(x=coupled_latents[0]     ,				y=coupled_latents[1]	)

														# either one is fine
														lowerCamelCase__   :      Optional[int]			    =			coupled_latents[0]

														if output_type not in ["latent", "pt", "np", "pil"]:
																					lowerCamelCase__   :      Optional[Any]			    =			(
																					    F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
																					    """`pil`, `np`, `pt`, `latent`"""
																					)
																					deprecate("""Unsupported output_type"""     ,				"""1.0.0"""     ,				UpperCamelCase__     ,				standard_warn=UpperCamelCase__	)
																					lowerCamelCase__   :      str			    =			"""np"""

														if output_type == "latent":
																					lowerCamelCase__   :      Tuple			    =			final_latent
														else:
																					lowerCamelCase__   :      List[str]			    =			self.decode_latents(UpperCamelCase__	)
																					lowerCamelCase__   :      Optional[Any]			    =			self.image_processor.postprocess(UpperCamelCase__     ,				output_type=UpperCamelCase__	)

														return image









'''simple docstring'''







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
			from PIL import Image

class _lowercase      (							unittest.TestCase							, _lowercase						):




							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      Optional[Any]			    =			load_tool("""image-captioning"""	)
														self.tool.setup()
														lowerCamelCase__   :      Optional[Any]			    =			load_tool("""image-captioning"""     ,				remote=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							int	):
														lowerCamelCase__   :      List[Any]			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      Optional[Any]			    =			self.tool(UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      Tuple			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      Dict			    =			self.remote_tool(UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)




							def 						lowerCamelCase_							(				self:							str	):
														lowerCamelCase__   :      Optional[int]			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      str			    =			self.tool(image=UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      Optional[int]			    =			Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	)
														lowerCamelCase__   :      Optional[int]			    =			self.remote_tool(image=UpperCamelCase__	)
														self.assertEqual(UpperCamelCase__     ,				"""two cats sleeping on a couch"""	)

