



"""simple docstring"""






import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput

class 	__lowerCAmelCase							(nn.Module  ):


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a=1.0   ):




										"""simple docstring"""





										super().__init__()

										snake_case_						:List[Any]								=						cut_size
										snake_case_						:Optional[Any]								=						cut_power







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_	,  snake_case_						:List[Any]								=						pixel_values.shape[2:4]
										snake_case_						:str								=						min(a    ,       a   )
										snake_case_						:List[str]								=						min(a    ,       a    ,       self.cut_size   )
										snake_case_						:Optional[int]								=						[]
										for _ in range(a   ):
															snake_case_						:Any								=						int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
															snake_case_						:int								=						torch.randint(0    ,       sideX - size + 1    ,       ()   )
															snake_case_						:Optional[int]								=						torch.randint(0    ,       sideY - size + 1    ,       ()   )
															snake_case_						:List[str]								=						pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(a    ,       self.cut_size   )   )
										return torch.cat(a   )



def      A      (			_A,     _A							):




					"""simple docstring"""
					snake_case_						:Any								=						F.normalize(_A,     dim=-1							)
					snake_case_						:str								=						F.normalize(_A,     dim=-1							)
					return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)
def      A      (			_A,     _A							):




					"""simple docstring"""
					for param in model.parameters():
										snake_case_						:Optional[int]								=						value

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       ):




										"""simple docstring"""





										super().__init__()
										self.register_modules(
										    vae=a    ,       text_encoder=a    ,       clip_model=a    ,       tokenizer=a    ,       unet=a    ,       scheduler=a    ,       feature_extractor=a    ,       )

										snake_case_						:Optional[int]								=						transforms.Normalize(mean=feature_extractor.image_mean    ,       std=feature_extractor.image_std   )
										snake_case_						:Optional[Any]								=						(
										    feature_extractor.size
										    if isinstance(feature_extractor.size    ,       a   )
										    else feature_extractor.size["shortest_edge"]
										)
										snake_case_						:Optional[Any]								=						MakeCutouts(self.cut_out_size   )

										set_requires_grad(self.text_encoder    ,       a   )
										set_requires_grad(self.clip_model    ,       a   )







					def 		_a (      self    ,       a = "auto"   ):




										"""simple docstring"""





										if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															snake_case_						:int								=						self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(a   )







					def 		_a (      self   ):




										"""simple docstring"""





										self.enable_attention_slicing(a   )







					def 		_a (      self   ):




										"""simple docstring"""





										set_requires_grad(self.vae    ,       a   )







					def 		_a (      self   ):




										"""simple docstring"""





										set_requires_grad(self.vae    ,       a   )







					def 		_a (      self   ):




										"""simple docstring"""





										set_requires_grad(self.unet    ,       a   )







					def 		_a (      self   ):




										"""simple docstring"""





										set_requires_grad(self.unet    ,       a   )







					@torch.enable_grad()
					def 		_a (      self    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a=True    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						latents.detach().requires_grad_()

										snake_case_						:Dict								=						self.scheduler.scale_model_input(a    ,       a   )

										# predict the noise residual
										snake_case_						:Optional[Any]								=						self.unet(a    ,       a    ,       encoder_hidden_states=a   ).sample

										if isinstance(self.scheduler    ,       (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
															snake_case_						:Dict								=						self.scheduler.alphas_cumprod[timestep]
															snake_case_						:Tuple								=						1 - alpha_prod_t
															# compute predicted original sample from predicted noise also called
															# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
															snake_case_						:List[str]								=						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															snake_case_						:Tuple								=						torch.sqrt(a   )
															snake_case_						:Dict								=						pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler    ,       a   ):
															snake_case_						:Optional[Any]								=						self.scheduler.sigmas[index]
															snake_case_						:List[str]								=						latents - sigma * noise_pred
										else:
															raise ValueError(F'''scheduler type {type(self.scheduler   )} not supported'''   )

										snake_case_						:int								=						1 / self.vae.config.scaling_factor * sample
										snake_case_						:Dict								=						self.vae.decode(a   ).sample
										snake_case_						:int								=						(image / 2 + 0.5).clamp(0    ,       1   )

										if use_cutouts:
															snake_case_						:Tuple								=						self.make_cutouts(a    ,       a   )
										else:
															snake_case_						:Any								=						transforms.Resize(self.cut_out_size   )(a   )
										snake_case_						:Dict								=						self.normalize(a   ).to(latents.dtype   )

										snake_case_						:str								=						self.clip_model.get_image_features(a   )
										snake_case_						:str								=						image_embeddings_clip / image_embeddings_clip.norm(p=2    ,       dim=-1    ,       keepdim=a   )

										if use_cutouts:
															snake_case_						:Optional[int]								=						spherical_dist_loss(a    ,       a   )
															snake_case_						:Optional[int]								=						dists.view([num_cutouts, sample.shape[0], -1]   )
															snake_case_						:int								=						dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
										else:
															snake_case_						:Dict								=						spherical_dist_loss(a    ,       a   ).mean() * clip_guidance_scale

										snake_case_						:List[Any]								=						-torch.autograd.grad(a    ,       a   )[0]

										if isinstance(self.scheduler    ,       a   ):
															snake_case_						:Optional[int]								=						latents.detach() + grads * (sigma**2)
															snake_case_						:Union[str, Any]								=						noise_pred_original
										else:
															snake_case_						:Optional[int]								=						noise_pred_original - torch.sqrt(a   ) * grads
										return noise_pred, latents







					@torch.no_grad()
					def __call__(      self    ,       a    ,       a = 5_12    ,       a = 5_12    ,       a = 50    ,       a = 7.5    ,       a = 1    ,       a = 0.0    ,       a = 1_00    ,       a = None    ,       a = 4    ,       a = True    ,       a = None    ,       a = None    ,       a = "pil"    ,       a = True    ,       ):




										"""simple docstring"""





										if isinstance(a    ,       a   ):
															snake_case_						:Tuple								=						1
										elif isinstance(a    ,       a   ):
															snake_case_						:List[Any]								=						len(a   )
										else:
															raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(a   )}'''   )

										if height % 8 != 0 or width % 8 != 0:
															raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

										# get prompt text embeddings
										snake_case_						:Dict								=						self.tokenizer(
										    a    ,       padding="max_length"    ,       max_length=self.tokenizer.model_max_length    ,       truncation=a    ,       return_tensors="pt"    ,       )
										snake_case_						:List[str]								=						self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
										# duplicate text embeddings for each generation per prompt
										snake_case_						:Optional[Any]								=						text_embeddings.repeat_interleave(a    ,       dim=0   )

										if clip_guidance_scale > 0:
															if clip_prompt is not None:
																				snake_case_						:Optional[int]								=						self.tokenizer(
																				    a    ,       padding="max_length"    ,       max_length=self.tokenizer.model_max_length    ,       truncation=a    ,       return_tensors="pt"    ,       ).input_ids.to(self.device   )
															else:
																				snake_case_						:List[str]								=						text_input.input_ids.to(self.device   )
															snake_case_						:List[Any]								=						self.clip_model.get_text_features(a   )
															snake_case_						:str								=						text_embeddings_clip / text_embeddings_clip.norm(p=2    ,       dim=-1    ,       keepdim=a   )
															# duplicate text embeddings clip for each generation per prompt
															snake_case_						:Any								=						text_embeddings_clip.repeat_interleave(a    ,       dim=0   )

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										snake_case_						:Dict								=						guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
															snake_case_						:Dict								=						text_input.input_ids.shape[-1]
															snake_case_						:Tuple								=						self.tokenizer([""]    ,       padding="max_length"    ,       max_length=a    ,       return_tensors="pt"   )
															snake_case_						:List[str]								=						self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
															# duplicate unconditional embeddings for each generation per prompt
															snake_case_						:Union[str, Any]								=						uncond_embeddings.repeat_interleave(a    ,       dim=0   )

															# 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
															snake_case_						:Optional[Any]								=						torch.cat([uncond_embeddings, text_embeddings]   )

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										snake_case_						:Optional[Any]								=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										snake_case_						:Optional[int]								=						text_embeddings.dtype
										if latents is None:
															if self.device.type == "mps":
																				# randn does not work reproducibly on mps
																				snake_case_						:Tuple								=						torch.randn(a    ,       generator=a    ,       device="cpu"    ,       dtype=a   ).to(
																				    self.device   )
															else:
																				snake_case_						:int								=						torch.randn(a    ,       generator=a    ,       device=self.device    ,       dtype=a   )
										else:
															if latents.shape != latents_shape:
																				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
															snake_case_						:Optional[int]								=						latents.to(self.device   )

										# set timesteps
										snake_case_						:Any								=						"offset" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
										snake_case_						:Dict								=						{}
										if accepts_offset:
															snake_case_						:str								=						1

										self.scheduler.set_timesteps(a    ,       **a   )

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										snake_case_						:Dict								=						self.scheduler.timesteps.to(self.device   )

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case_						:str								=						latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										snake_case_						:Any								=						"eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
										snake_case_						:List[str]								=						{}
										if accepts_eta:
															snake_case_						:Any								=						eta

										# check if the scheduler accepts generator
										snake_case_						:Optional[int]								=						"generator" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
										if accepts_generator:
															snake_case_						:Optional[int]								=						generator

										for i, t in enumerate(self.progress_bar(a   )   ):
															# expand the latents if we are doing classifier free guidance
															snake_case_						:str								=						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
															snake_case_						:str								=						self.scheduler.scale_model_input(a    ,       a   )

															# predict the noise residual
															snake_case_						:Union[str, Any]								=						self.unet(a    ,       a    ,       encoder_hidden_states=a   ).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																				snake_case_	,  snake_case_						:Dict								=						noise_pred.chunk(2   )
																				snake_case_						:List[str]								=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																				snake_case_						:int								=						(
																				    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																				)
																				snake_case_	,  snake_case_						:List[Any]								=						self.cond_fn(
																				    a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a    ,       )

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_						:Dict								=						self.scheduler.step(a    ,       a    ,       a    ,       **a   ).prev_sample

										# scale and decode the image latents with vae
										snake_case_						:int								=						1 / self.vae.config.scaling_factor * latents
										snake_case_						:Optional[int]								=						self.vae.decode(a   ).sample

										snake_case_						:Dict								=						(image / 2 + 0.5).clamp(0    ,       1   )
										snake_case_						:List[Any]								=						image.cpu().permute(0    ,       2    ,       3    ,       1   ).numpy()

										if output_type == "pil":
															snake_case_						:Tuple								=						self.numpy_to_pil(a   )

										if not return_dict:
															return (image, None)

										return StableDiffusionPipelineOutput(images=a    ,       nsfw_content_detected=a   )






"""simple docstring"""






def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     ):




					"""simple docstring"""
					snake_case_						:List[Any]								=						list(_A							)
					snake_case_						:int								=						list(_A							)
					snake_case_						:List[Any]								=						len(_A							)
					snake_case_						:List[Any]								=						len(_A							)

					snake_case_						:Union[str, Any]								=						[
					    [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]
					snake_case_						:int								=						[
					    ["0" for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]

					for i in range(1,     len_source_seq + 1							):
										snake_case_						:List[str]								=						i * delete_cost
										snake_case_						:str								=						F'''D{source_seq[i - 1]:c}'''

					for i in range(1,     len_destination_seq + 1							):
										snake_case_						:int								=						i * insert_cost
										snake_case_						:Dict								=						F'''I{destination_seq[i - 1]:c}'''

					for i in range(1,     len_source_seq + 1							):
										for j in range(1,     len_destination_seq + 1							):
															if source_seq[i - 1] == destination_seq[j - 1]:
																				snake_case_						:List[Any]								=						costs[i - 1][j - 1] + copy_cost
																				snake_case_						:str								=						F'''C{source_seq[i - 1]:c}'''
															else:
																				snake_case_						:Any								=						costs[i - 1][j - 1] + replace_cost
																				snake_case_						:Any								=						F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

															if costs[i - 1][j] + delete_cost < costs[i][j]:
																				snake_case_						:str								=						costs[i - 1][j] + delete_cost
																				snake_case_						:Tuple								=						F'''D{source_seq[i - 1]:c}'''

															if costs[i][j - 1] + insert_cost < costs[i][j]:
																				snake_case_						:int								=						costs[i][j - 1] + insert_cost
																				snake_case_						:int								=						F'''I{destination_seq[j - 1]:c}'''

					return costs, ops
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					if i == 0 and j == 0:
										return []
					else:
										if ops[i][j][0] == "C" or ops[i][j][0] == "R":
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     i - 1,     j - 1							)
															seq.append(ops[i][j]							)
															return seq
										elif ops[i][j][0] == "D":
															snake_case_						:Union[str, Any]								=						assemble_transformation(_A,     i - 1,     _A							)
															seq.append(ops[i][j]							)
															return seq
										else:
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     _A,     j - 1							)
															seq.append(ops[i][j]							)
															return seq


if __name__ == "__main__":
				__UpperCAmelCase					,	__UpperCAmelCase      :      List[Any]   					=					compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				__UpperCAmelCase      :      List[Any]   					=					len(operations)
				__UpperCAmelCase      :      Dict   					=					len(operations[0])
				__UpperCAmelCase      :      List[str]   					=					assemble_transformation(operations, m - 1, n - 1)

				__UpperCAmelCase      :      Any   					=					list('Python')
				__UpperCAmelCase      :      Tuple   					=					0
				__UpperCAmelCase      :      Optional[int]   					=					0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																__UpperCAmelCase      :      Any   					=					op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))

