


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 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = 0.9_3			,       _SCREAMING_SNAKE_CASE = True			,       ):
								a_     			=   mixing_coeff
								a_     			=   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			,       )

								a_     			=   2 ** (len(self.vae.config.block_out_channels    ) - 1)
								a_     			=   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = False    ):
								a_     			=   self.tokenizer(
								    _SCREAMING_SNAKE_CASE			,       padding="""max_length"""			,       max_length=self.tokenizer.model_max_length			,       truncation=_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       )

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

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

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

									a_     			=   self.tokenizer(
									    _SCREAMING_SNAKE_CASE			,       padding="""max_length"""			,       max_length=self.tokenizer.model_max_length			,       truncation=_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""			,       )

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

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

								return prompt_embeds





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								a_     			=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								a_     			=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								# as self.alphas_cumprod is always in cpu
								a_     			=   int(_SCREAMING_SNAKE_CASE    )

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

								return alpha_prod, 1 - alpha_prod





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       ):
								a_     			=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								a_			, a_     			=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE    )
								a_			, a_     			=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE    )

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

								a_     			=   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       ):
								a_     			=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								a_			, a_     			=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE    )
								a_			, a_     			=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE    )

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

								return model_input, next_model_input.to(base.dtype    )





							@torch.no_grad()
							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   1 / self.vae.config.scaling_factor * latents
								a_     			=   self.vae.decode(_SCREAMING_SNAKE_CASE    ).sample
								a_     			=   (image / 2 + 0.5).clamp(0			,       1    )
								return image





							@torch.no_grad()
							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = None			,       ):
								a_     			=   guidance_scale > 1.0

								a_     			=   image.to(device=self.device			,       dtype=text_embeds.dtype    )
								a_     			=   self.vae.encode(_SCREAMING_SNAKE_CASE    ).latent_dist.sample(_SCREAMING_SNAKE_CASE    )

								a_     			=   self.vae.config.scaling_factor * latent

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

								for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE    )			,       total=len(_SCREAMING_SNAKE_CASE    )    ):
									a_     			=   self.noise_mixing_layer(x=coupled_latents[0]			,       y=coupled_latents[1]    )

									# j - model_input index, k - base index
									for j in range(2    ):
										a_     			=   j ^ 1

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

										a_     			=   coupled_latents[j]
										a_     			=   coupled_latents[k]

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

										a_     			=   self.unet(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       encoder_hidden_states=_SCREAMING_SNAKE_CASE    ).sample

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

										a_			, a_     			=   self.noise_step(
										    base=_SCREAMING_SNAKE_CASE			,       model_input=_SCREAMING_SNAKE_CASE			,       model_output=_SCREAMING_SNAKE_CASE			,       timestep=_SCREAMING_SNAKE_CASE			,       )

										a_     			=   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"			,       ):
								a_     			=   guidance_scale > 1.0

								a_     			=   self.image_processor.preprocess(_SCREAMING_SNAKE_CASE    )

								a_     			=   self._encode_prompt(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   self._encode_prompt(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

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

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

								a_     			=   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    ):
										a_     			=   k ^ 1

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

										a_     			=   coupled_latents[j]
										a_     			=   coupled_latents[k]

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

										a_     			=   self.unet(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       encoder_hidden_states=_SCREAMING_SNAKE_CASE    ).sample

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

										a_			, a_     			=   self.denoise_step(
										    base=_SCREAMING_SNAKE_CASE			,       model_input=_SCREAMING_SNAKE_CASE			,       model_output=_SCREAMING_SNAKE_CASE			,       timestep=_SCREAMING_SNAKE_CASE			,       )

										a_     			=   model_input

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

								# either one is fine
								a_     			=   coupled_latents[0]

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

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

								return image



from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(UpperCamelCase     ,					-1	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	str:



	"""simple docstring"""






	if not balanced_parentheses(UpperCamelCase	):
		raise ValueError("""Mismatched parentheses"""	)
	a_     			=   Stack()
	a_     			=   []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(UpperCamelCase	)
		elif char == "(":
			stack.push(UpperCamelCase	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(UpperCamelCase	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(UpperCamelCase	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(UpperCamelCase	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			_A							     =       'a+b*(c^d-e)^(f+g*h)-i'

			print('Infix to Postfix Notation demonstration:\n')
			print('Infix notation: ' + expression)
			print('Postfix notation: ' + infix_to_postfix(expression))