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 						a__							(   nn.Module		):



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=1.0    ) ->    Optional[int]:
								super().__init__()

								__a							=   cut_size
								__a							=   cut_power







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






def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   F.normalize(__lowerCamelCase      ,				dim=-1						)
					__a							=   F.normalize(__lowerCamelCase      ,				dim=-1						)
					return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					for param in model.parameters():
										__a							=   value



class 						a__							(   UpperCAmelCase_		):



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						) ->    Tuple:
								super().__init__()
								self.register_modules(
								    vae=UpperCAmelCase      ,						text_encoder=UpperCAmelCase      ,						clip_model=UpperCAmelCase      ,						tokenizer=UpperCAmelCase      ,						unet=UpperCAmelCase      ,						scheduler=UpperCAmelCase      ,						feature_extractor=UpperCAmelCase      ,						)

								__a							=   transforms.Normalize(mean=feature_extractor.image_mean      ,						std=feature_extractor.image_std    )
								__a							=   (
								    feature_extractor.size
								    if isinstance(feature_extractor.size      ,						UpperCAmelCase    )
								    else feature_extractor.size['shortest_edge']
								)
								__a							=   MakeCutouts(self.cut_out_size    )

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



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase = "auto"    ) ->    Optional[Any]:





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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								self.enable_attention_slicing(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								set_requires_grad(self.vae      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								set_requires_grad(self.vae      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								set_requires_grad(self.unet      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								set_requires_grad(self.unet      ,						UpperCAmelCase    )



			@torch.enable_grad()
			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=True      ,						) ->    int:
								__a							=   latents.detach().requires_grad_()

								__a							=   self.scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

								# predict the noise residual
								__a							=   self.unet(UpperCAmelCase      ,						UpperCAmelCase      ,						encoder_hidden_states=UpperCAmelCase    ).sample

								if isinstance(self.scheduler      ,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)    ):
													__a							=   self.scheduler.alphas_cumprod[timestep]
													__a							=   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
													__a							=   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

													__a							=   torch.sqrt(UpperCAmelCase    )
													__a							=   pred_original_sample * (fac) + latents * (1 - fac)
								elif isinstance(self.scheduler      ,						UpperCAmelCase    ):
													__a							=   self.scheduler.sigmas[index]
													__a							=   latents - sigma * noise_pred
								else:
													raise ValueError(f'''scheduler type {type(self.scheduler    )} not supported'''    )

								__a							=   1 / self.vae.config.scaling_factor * sample
								__a							=   self.vae.decode(UpperCAmelCase    ).sample
								__a							=   (image / 2 + 0.5).clamp(0      ,						1    )

								if use_cutouts:
													__a							=   self.make_cutouts(UpperCAmelCase      ,						UpperCAmelCase    )
								else:
													__a							=   transforms.Resize(self.cut_out_size    )(UpperCAmelCase    )
								__a							=   self.normalize(UpperCAmelCase    ).to(latents.dtype    )

								__a							=   self.clip_model.get_image_features(UpperCAmelCase    )
								__a							=   image_embeddings_clip / image_embeddings_clip.norm(p=2      ,						dim=-1      ,						keepdim=UpperCAmelCase    )

								if use_cutouts:
													__a							=   spherical_dist_loss(UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   dists.view([num_cutouts, sample.shape[0], -1]    )
													__a							=   dists.sum(2    ).mean(0    ).sum() * clip_guidance_scale
								else:
													__a							=   spherical_dist_loss(UpperCAmelCase      ,						UpperCAmelCase    ).mean() * clip_guidance_scale

								__a							=   -torch.autograd.grad(UpperCAmelCase      ,						UpperCAmelCase    )[0]

								if isinstance(self.scheduler      ,						UpperCAmelCase    ):
													__a							=   latents.detach() + grads * (sigma**2)
													__a							=   noise_pred_original
								else:
													__a							=   noise_pred_original - torch.sqrt(UpperCAmelCase    ) * grads
								return noise_pred, latents







			@torch.no_grad()
			def __call__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = 5_1_2      ,						UpperCAmelCase = 5_1_2      ,						UpperCAmelCase = 5_0      ,						UpperCAmelCase = 7.5      ,						UpperCAmelCase = 1      ,						UpperCAmelCase = 0.0      ,						UpperCAmelCase = 1_0_0      ,						UpperCAmelCase = None      ,						UpperCAmelCase = 4      ,						UpperCAmelCase = True      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = "pil"      ,						UpperCAmelCase = True      ,						) ->    List[str]:





								if isinstance(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   1
								elif isinstance(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   len(UpperCAmelCase    )
								else:
													raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase    )}'''    )

								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
								__a							=   self.tokenizer(
								    UpperCAmelCase      ,						padding='max_length'      ,						max_length=self.tokenizer.model_max_length      ,						truncation=UpperCAmelCase      ,						return_tensors='pt'      ,						)
								__a							=   self.text_encoder(text_input.input_ids.to(self.device    )    )[0]
								# duplicate text embeddings for each generation per prompt
								__a							=   text_embeddings.repeat_interleave(UpperCAmelCase      ,						dim=0    )

								if clip_guidance_scale > 0:
													if clip_prompt is not None:
																		__a							=   self.tokenizer(
																		    UpperCAmelCase      ,						padding='max_length'      ,						max_length=self.tokenizer.model_max_length      ,						truncation=UpperCAmelCase      ,						return_tensors='pt'      ,						).input_ids.to(self.device    )
													else:
																		__a							=   text_input.input_ids.to(self.device    )
													__a							=   self.clip_model.get_text_features(UpperCAmelCase    )
													__a							=   text_embeddings_clip / text_embeddings_clip.norm(p=2      ,						dim=-1      ,						keepdim=UpperCAmelCase    )
													# duplicate text embeddings clip for each generation per prompt
													__a							=   text_embeddings_clip.repeat_interleave(UpperCAmelCase      ,						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.
								__a							=   guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
													__a							=   text_input.input_ids.shape[-1]
													__a							=   self.tokenizer(['']      ,						padding='max_length'      ,						max_length=UpperCAmelCase      ,						return_tensors='pt'    )
													__a							=   self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
													# duplicate unconditional embeddings for each generation per prompt
													__a							=   uncond_embeddings.repeat_interleave(UpperCAmelCase      ,						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
													__a							=   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`.
								__a							=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								__a							=   text_embeddings.dtype
								if latents is None:
													if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		__a							=   torch.randn(UpperCAmelCase      ,						generator=UpperCAmelCase      ,						device='cpu'      ,						dtype=UpperCAmelCase    ).to(
																		    self.device    )
													else:
																		__a							=   torch.randn(UpperCAmelCase      ,						generator=UpperCAmelCase      ,						device=self.device      ,						dtype=UpperCAmelCase    )
								else:
													if latents.shape != latents_shape:
																		raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
													__a							=   latents.to(self.device    )

								# set timesteps
								__a							=   'offset' in set(inspect.signature(self.scheduler.set_timesteps    ).parameters.keys()    )
								__a							=   {}
								if accepts_offset:
													__a							=   1

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

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

								# scale the initial noise by the standard deviation required by the scheduler
								__a							=   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]
								__a							=   'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
								__a							=   {}
								if accepts_eta:
													__a							=   eta

								# check if the scheduler accepts generator
								__a							=   'generator' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
								if accepts_generator:
													__a							=   generator

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

													# predict the noise residual
													__a							=   self.unet(UpperCAmelCase      ,						UpperCAmelCase      ,						encoder_hidden_states=UpperCAmelCase    ).sample

													# perform classifier free guidance
													if do_classifier_free_guidance:
																		__a							=   noise_pred.chunk(2    )
																		__a							=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# perform clip guidance
													if clip_guidance_scale > 0:
																		__a							=   (
																		    text_embeddings.chunk(2    )[1] if do_classifier_free_guidance else text_embeddings
																		)
																		__a							=   self.cond_fn(
																		    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						)

													# compute the previous noisy sample x_t -> x_t-1
													__a							=   self.scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						**UpperCAmelCase    ).prev_sample

								# scale and decode the image latents with vae
								__a							=   1 / self.vae.config.scaling_factor * latents
								__a							=   self.vae.decode(UpperCAmelCase    ).sample

								__a							=   (image / 2 + 0.5).clamp(0      ,						1    )
								__a							=   image.cpu().permute(0      ,						2      ,						3      ,						1    ).numpy()

								if output_type == "pil":
													__a							=   self.numpy_to_pil(UpperCAmelCase    )

								if not return_dict:
													return (image, None)

								return StableDiffusionPipelineOutput(images=UpperCAmelCase      ,						nsfw_content_detected=UpperCAmelCase    )











def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						int:
  # The longer word should come first
  if len(lowerCAmelCase ) < len(lowerCAmelCase ):
    return levenshtein_distance(lowerCAmelCase			,							lowerCAmelCase )

  if len(lowerCAmelCase ) == 0:
    return len(lowerCAmelCase )

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

    for j, ca in enumerate(lowerCAmelCase ):
      # Calculate insertions, deletions and substitutions
      _snake_case      : Optional[Any]														=		previous_row[j + 1] + 1
      _snake_case      : Optional[int]														=		current_row[j] + 1
      _snake_case      : Union[str, Any]														=		previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase ) )

    # Store the previous row
    _snake_case      : str														=		current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
      lowerCAmelCase_        =			input("""Enter the first word:\n""").strip()
      lowerCAmelCase_        =			input("""Enter the second word:\n""").strip()

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




