





'''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     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 0.93,__SCREAMING_SNAKE_CASE = True,):



					'''simple docstring'''

					__lowerCAmelCase											=				mixing_coeff
					__lowerCAmelCase											=				leapfrog_steps

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

					__lowerCAmelCase											=				2 ** (len(self.vae.config.block_out_channels			) - 1)
					__lowerCAmelCase											=				VaeImageProcessor(vae_scale_factor=self.vae_scale_factor			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.tokenizer(
					    __SCREAMING_SNAKE_CASE,padding="""max_length""",max_length=self.tokenizer.model_max_length,truncation=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",)

					__lowerCAmelCase											=				self.text_encoder(text_inputs.input_ids.to(self.device			)			).last_hidden_state

					__lowerCAmelCase											=				prompt_embeds.to(dtype=self.text_encoder.dtype,device=self.device			)

					if do_classifier_free_guidance:
								__lowerCAmelCase											=				"""""" if negative_prompt is None else negative_prompt

								__lowerCAmelCase											=				self.tokenizer(
								    __SCREAMING_SNAKE_CASE,padding="""max_length""",max_length=self.tokenizer.model_max_length,truncation=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",)

								__lowerCAmelCase											=				self.text_encoder(uncond_input.input_ids.to(self.device			)			).last_hidden_state

								__lowerCAmelCase											=				torch.cat([negative_prompt_embeds, prompt_embeds]			)

					return prompt_embeds
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					__lowerCAmelCase											=				self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					__lowerCAmelCase											=				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				int(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					__lowerCAmelCase											=				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					__lowerCAmelCase						,       __lowerCAmelCase											=				self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase						,       __lowerCAmelCase											=				self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				(alpha_prod_t_prev / alpha_prod_t) ** 0.5
					__lowerCAmelCase											=				-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					__lowerCAmelCase											=				(base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					__lowerCAmelCase											=				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					__lowerCAmelCase						,       __lowerCAmelCase											=				self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase						,       __lowerCAmelCase											=				self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				(alpha_prod_t_prev / alpha_prod_t) ** 0.5
					__lowerCAmelCase											=				-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					__lowerCAmelCase											=				a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype			)
		@torch.no_grad()
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				1 / self.vae.config.scaling_factor * latents
					__lowerCAmelCase											=				self.vae.decode(__SCREAMING_SNAKE_CASE			).sample
					__lowerCAmelCase											=				(image / 2 + 0.5).clamp(0,1			)
					return image
		@torch.no_grad()
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,):



					'''simple docstring'''

					__lowerCAmelCase											=				guidance_scale > 1.0

					__lowerCAmelCase											=				image.to(device=self.device,dtype=text_embeds.dtype			)
					__lowerCAmelCase											=				self.vae.encode(__SCREAMING_SNAKE_CASE			).latent_dist.sample(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				self.vae.config.scaling_factor * latent

					__lowerCAmelCase											=				[latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE			),total=len(__SCREAMING_SNAKE_CASE			)			):
								__lowerCAmelCase											=				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											=				j ^ 1

											if self.leapfrog_steps:
														if i % 2 == 0:
																	__lowerCAmelCase						,       __lowerCAmelCase											=				j, k

											__lowerCAmelCase											=				coupled_latents[j]
											__lowerCAmelCase											=				coupled_latents[k]

											__lowerCAmelCase											=				torch.cat([model_input] * 2			) if do_classifier_free_guidance else model_input

											__lowerCAmelCase											=				self.unet(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,encoder_hidden_states=__SCREAMING_SNAKE_CASE			).sample

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

											__lowerCAmelCase						,       __lowerCAmelCase											=				self.noise_step(
											    base=__SCREAMING_SNAKE_CASE,model_input=__SCREAMING_SNAKE_CASE,model_output=__SCREAMING_SNAKE_CASE,timestep=__SCREAMING_SNAKE_CASE,)

											__lowerCAmelCase											=				model_input

					return coupled_latents







		@torch.no_grad()
		def __call__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 3.0,__SCREAMING_SNAKE_CASE = 50,__SCREAMING_SNAKE_CASE = 0.8,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pil",):



					'''simple docstring'''

					__lowerCAmelCase											=				guidance_scale > 1.0

					__lowerCAmelCase											=				self.image_processor.preprocess(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				self._encode_prompt(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				self._encode_prompt(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

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

					__lowerCAmelCase											=				num_inference_steps - int(num_inference_steps * strength			)
					__lowerCAmelCase											=				self.scheduler.timesteps[t_limit:]
					__lowerCAmelCase											=				fwd_timesteps.flip(0			)

					__lowerCAmelCase											=				self.prepare_latents(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)

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

											if self.leapfrog_steps:
														if i % 2 == 1:
																	__lowerCAmelCase						,       __lowerCAmelCase											=				j, k

											__lowerCAmelCase											=				coupled_latents[j]
											__lowerCAmelCase											=				coupled_latents[k]

											__lowerCAmelCase											=				torch.cat([model_input] * 2			) if do_classifier_free_guidance else model_input

											__lowerCAmelCase											=				self.unet(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,encoder_hidden_states=__SCREAMING_SNAKE_CASE			).sample

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

											__lowerCAmelCase						,       __lowerCAmelCase											=				self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE,model_input=__SCREAMING_SNAKE_CASE,model_output=__SCREAMING_SNAKE_CASE,timestep=__SCREAMING_SNAKE_CASE,)

											__lowerCAmelCase											=				model_input

								__lowerCAmelCase											=				self.denoise_mixing_layer(x=coupled_latents[0],y=coupled_latents[1]			)

					# either one is fine
					__lowerCAmelCase											=				coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								__lowerCAmelCase											=				(
								    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""",__SCREAMING_SNAKE_CASE,standard_warn=__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				"""np"""

					if output_type == "latent":
								__lowerCAmelCase											=				final_latent
					else:
								__lowerCAmelCase											=				self.decode_latents(__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				self.image_processor.postprocess(__SCREAMING_SNAKE_CASE,output_type=__SCREAMING_SNAKE_CASE			)

					return image








'''simple docstring'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class     _UpperCAmelCase							(						unittest.TestCase   ):
		@patch("""diffusers.utils.hub_utils.get_full_repo_name"""			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""full_repo_name"""
					with TemporaryDirectory() as tmpdir:
								# Dummy args values
								__lowerCAmelCase											=				Mock()
								__lowerCAmelCase											=				tmpdir
								__lowerCAmelCase											=				0
								__lowerCAmelCase											=				"""hub_token"""
								__lowerCAmelCase											=				"""dataset_name"""
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				10_00_00
								__lowerCAmelCase											=				1_00_00
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				0.02
								__lowerCAmelCase											=				0.03
								__lowerCAmelCase											=				0.0005
								__lowerCAmelCase											=				0.00_0001
								__lowerCAmelCase											=				1
								__lowerCAmelCase											=				10
								__lowerCAmelCase											=				0.001
								__lowerCAmelCase											=				0.1
								__lowerCAmelCase											=				0.2
								__lowerCAmelCase											=				True

								# Model card mush be rendered and saved
								diffusers.utils.hub_utils.create_model_card(__SCREAMING_SNAKE_CASE,model_name="""model_name"""			)
								self.assertTrue((Path(__SCREAMING_SNAKE_CASE			) / """README.md""").is_file()			)

