






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 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Optional[int]							,				_A       :						AutoencoderKL							,				_A       :						CLIPTextModel							,				_A       :						CLIPTokenizer							,				_A       :						UNetaDConditionModel							,				_A       :						DDIMScheduler							,				_A       :						float = 0.93							,				_A       :						bool = True							,				)  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   mixing_coeff
								UpperCAmelCase_     :   Union[str, Any]       =   leapfrog_steps

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

								UpperCAmelCase_     :   Optional[Any]       =   2 ** (len(self.vae.config.block_out_channels     ) - 1)
								UpperCAmelCase_     :   Optional[Any]       =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )




					def 						A     (       self       :						Optional[int]							,				_A       :						str							,				_A       :						Optional[str] = None							,				_A       :						bool = False     )  ->    Dict:
								UpperCAmelCase_     :   int       =   self.tokenizer(
								    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

								UpperCAmelCase_     :   Tuple       =   self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

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

								if do_classifier_free_guidance:
											UpperCAmelCase_     :   Optional[int]       =   '''''' if negative_prompt is None else negative_prompt

											UpperCAmelCase_     :   Union[str, Any]       =   self.tokenizer(
											    _A							,				padding='''max_length'''							,				max_length=self.tokenizer.model_max_length							,				truncation=_A							,				return_tensors='''pt'''							,				)

											UpperCAmelCase_     :   List[str]       =   self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

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

								return prompt_embeds




					def 						A     (       self       :						Optional[int]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Tuple:
								UpperCAmelCase_     :   Union[str, Any]       =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								UpperCAmelCase_     :   Any       =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




					def 						A     (       self       :						Union[str, Any]							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor     )  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								UpperCAmelCase_     :   int       =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




					def 						A     (       self       :						List[str]							,				_A       :						torch.Tensor     )  ->    List[Any]:
								# as self.alphas_cumprod is always in cpu
								UpperCAmelCase_     :   Tuple       =   int(_A     )

								UpperCAmelCase_     :   List[str]       =   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

								return alpha_prod, 1 - alpha_prod




					def 						A     (       self       :						Dict							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Tuple:
								UpperCAmelCase_     :   str       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_     :   Tuple       =   self._get_alpha_and_beta(_A     )

								UpperCAmelCase_     :   Optional[Any]       =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
								UpperCAmelCase_     :   Tuple       =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

								UpperCAmelCase_     :   List[str]       =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype     )




					def 						A     (       self       :						Any							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				)  ->    Union[str, Any]:
								UpperCAmelCase_     :   Tuple       =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								UpperCAmelCase_     :   List[Any]       =   self._get_alpha_and_beta(_A     )
								UpperCAmelCase_     :   int       =   self._get_alpha_and_beta(_A     )

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

								return model_input, next_model_input.to(base.dtype     )




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						torch.Tensor     )  ->    List[Any]:
								UpperCAmelCase_     :   Union[str, Any]       =   1 / self.vae.config.scaling_factor * latents
								UpperCAmelCase_     :   str       =   self.vae.decode(_A     ).sample
								UpperCAmelCase_     :   Any       =   (image / 2 + 0.5).clamp(0							,				1     )
								return image




					@torch.no_grad()
					def 						A     (       self       :						int							,				_A       :						Image.Image							,				_A       :						torch.Tensor							,				_A       :						torch.Tensor							,				_A       :						float							,				_A       :						Optional[torch.Generator] = None							,				)  ->    Any:
								UpperCAmelCase_     :   Optional[Any]       =   guidance_scale > 1.0

								UpperCAmelCase_     :   str       =   image.to(device=self.device							,				dtype=text_embeds.dtype     )
								UpperCAmelCase_     :   int       =   self.vae.encode(_A     ).latent_dist.sample(_A     )

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

								UpperCAmelCase_     :   Optional[int]       =   [latent.clone(), latent.clone()]

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											UpperCAmelCase_     :   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     ):
														UpperCAmelCase_     :   List[Any]       =   j ^ 1

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

														UpperCAmelCase_     :   str       =   coupled_latents[j]
														UpperCAmelCase_     :   List[Any]       =   coupled_latents[k]

														UpperCAmelCase_     :   Optional[int]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   Dict       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

														if do_classifier_free_guidance:
																	UpperCAmelCase_     :   Optional[int]       =   noise_pred.chunk(2     )
																	UpperCAmelCase_     :   List[str]       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														UpperCAmelCase_     :   Union[str, Any]       =   self.noise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

								return coupled_latents





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

								UpperCAmelCase_     :   Optional[Any]       =   self.image_processor.preprocess(_A     )

								UpperCAmelCase_     :   str       =   self._encode_prompt(_A							,				_A							,				_A     )
								UpperCAmelCase_     :   int       =   self._encode_prompt(_A							,				_A							,				_A     )

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

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

								UpperCAmelCase_     :   Optional[int]       =   self.prepare_latents(_A							,				_A							,				_A							,				_A							,				_A     )

								for i, t in tqdm(enumerate(_A     )							,				total=len(_A     )     ):
											# j - model_input index, k - base index
											for k in range(2     ):
														UpperCAmelCase_     :   Union[str, Any]       =   k ^ 1

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

														UpperCAmelCase_     :   Tuple       =   coupled_latents[j]
														UpperCAmelCase_     :   Optional[int]       =   coupled_latents[k]

														UpperCAmelCase_     :   Union[str, Any]       =   torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

														UpperCAmelCase_     :   int       =   self.unet(_A							,				_A							,				encoder_hidden_states=_A     ).sample

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

														UpperCAmelCase_     :   Any       =   self.denoise_step(
														    base=_A							,				model_input=_A							,				model_output=_A							,				timestep=_A							,				)

														UpperCAmelCase_     :   str       =   model_input

											UpperCAmelCase_     :   str       =   self.denoise_mixing_layer(x=coupled_latents[0]							,				y=coupled_latents[1]     )

								# either one is fine
								UpperCAmelCase_     :   Union[str, Any]       =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   (
											    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'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   int       =   '''np'''

								if output_type == "latent":
											UpperCAmelCase_     :   Tuple       =   final_latent
								else:
											UpperCAmelCase_     :   Optional[int]       =   self.decode_latents(_A     )
											UpperCAmelCase_     :   str       =   self.image_processor.postprocess(_A							,				output_type=_A     )

								return image










'''simple docstring'''





def __UpperCAmelCase (			A			:	list[list[int]]						)  -> bool:
			# must be
			UpperCAmelCase_     :   int       =   [list(A						) for row in matrix]
			if matrix and isinstance(A	,   A						):
						if isinstance(matrix[0]	,   A						):
									UpperCAmelCase_     :   Optional[Any]       =   0
									for row in matrix:
												if prev_len == 0:
															UpperCAmelCase_     :   List[str]       =   len(A						)
															UpperCAmelCase_     :   Any       =   True
												else:
															UpperCAmelCase_     :   Tuple       =   prev_len == len(A						)
						else:
									UpperCAmelCase_     :   Any       =   True
			else:
						UpperCAmelCase_     :   Dict       =   False

			return result
def __UpperCAmelCase (			A			:	list[list[int]]						)  -> None:
			if check_matrix(A						) and len(A						) > 0:
						UpperCAmelCase_     :   Dict       =   [list(A						) for row in a]
						UpperCAmelCase_     :   List[Any]       =   len(A						)
						if isinstance(a[0]	,   A						):
									UpperCAmelCase_     :   Any       =   len(a[0]						)
						else:
									for dat in a:
												print(A						)
									return

						# horizotal printing increasing
						for i in range(0	,   A						):
									print(a[0][i]						)
						# vertical printing down
						for i in range(1	,   A						):
									print(a[i][mat_col - 1]						)
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2	,   -1	,   -1						):
												print(a[mat_row - 1][i]						)
        # vertical printing up
						for i in range(mat_row - 2	,   0	,   -1						):
									print(a[i][0]						)
						UpperCAmelCase_     :   Union[str, Any]       =   [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(A						) > 0:
									spiral_print_clockwise(A						)
						else:
									return
			else:
						print('''Not a valid matrix'''						)
						return
def __UpperCAmelCase (			A			:	list[list]						)  -> list[int]:
			if matrix:
						return list(matrix.pop(0						)						) + spiral_traversal(list(zip(*A						)						)[::-1]						)
			else:
						return []


# driver code
if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCamelCase		:		List[Any]								=			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)


