
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       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=1.0	):
		super().__init__()

		lowercase					  =   cut_size
		lowercase					  =   cut_power





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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   F.normalize(__SCREAMING_SNAKE_CASE			,							dim=-1							)
	lowercase					  =   F.normalize(__SCREAMING_SNAKE_CASE			,							dim=-1							)
	return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	for param in model.parameters():
		lowercase					  =   value







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


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

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

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





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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		self.enable_attention_slicing(snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		set_requires_grad(self.vae		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		set_requires_grad(self.vae		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		set_requires_grad(self.unet		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		set_requires_grad(self.unet		,   snake_case	)





	@torch.enable_grad()
	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case=True		,   ):
		lowercase					  =   latents.detach().requires_grad_()

		lowercase					  =   self.scheduler.scale_model_input(snake_case		,   snake_case	)

		# predict the noise residual
		lowercase					  =   self.unet(snake_case		,   snake_case		,   encoder_hidden_states=snake_case	).sample

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

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

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

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

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

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

		lowercase					  =   -torch.autograd.grad(snake_case		,   snake_case	)[0]

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





	@torch.no_grad()
	def __call__(	self		,   snake_case		,   snake_case = 512		,   snake_case = 512		,   snake_case = 50		,   snake_case = 7.5		,   snake_case = 1		,   snake_case = 0.0		,   snake_case = 100		,   snake_case = None		,   snake_case = 4		,   snake_case = True		,   snake_case = None		,   snake_case = None		,   snake_case = "pil"		,   snake_case = True		,   ):
		if isinstance(snake_case		,   snake_case	):
			lowercase					  =   1
		elif isinstance(snake_case		,   snake_case	):
			lowercase					  =   len(snake_case	)
		else:
			raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case	)}'''	)

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

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

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

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

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

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

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

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

			# predict the noise residual
			lowercase					  =   self.unet(snake_case		,   snake_case		,   encoder_hidden_states=snake_case	).sample

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

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

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

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

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

		if output_type == "pil":
			lowercase					  =   self.numpy_to_pil(snake_case	)

		if not return_dict:
			return (image, None)

		return StableDiffusionPipelineOutput(images=snake_case		,   nsfw_content_detected=snake_case	)



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

