
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    ):





     def __init__(self :       Optional[int]							,       _A :       List[Any]							,       _A :       List[str]=1.0  )		->		Dict:
         super().__init__()

         snake_case						=   cut_size
         snake_case						=   cut_power





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



def 		lowercase_     (  A__					,						A__						)				->				List[Any]:
    """simple docstring"""

    snake_case						=   F.normalize(A__					,						dim=-1						)
    snake_case						=   F.normalize(A__					,						dim=-1						)
    return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)



def 		lowercase_     (  A__					,						A__						)				->				Optional[int]:
    """simple docstring"""

    for param in model.parameters():
        snake_case						=   value







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       List[str]							,       _A :       AutoencoderKL							,       _A :       CLIPTextModel							,       _A :       CLIPModel							,       _A :       CLIPTokenizer							,       _A :       UNetaDConditionModel							,       _A :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,       _A :       CLIPImageProcessor							,       )		->		List[str]:
         super().__init__()
         self.register_modules(
             vae=_A							,       text_encoder=_A							,       clip_model=_A							,       tokenizer=_A							,       unet=_A							,       scheduler=_A							,       feature_extractor=_A							,       )

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

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





     def 						UpperCAmelCase(self :       Optional[int]							,       _A :       Optional[Union[str, int]] = "auto"  )		->		Union[str, Any]:
         if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             snake_case						=   self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_A  )





     def 						UpperCAmelCase(self :       Any  )		->		Optional[Any]:
         self.enable_attention_slicing(_A  )





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





     def 						UpperCAmelCase(self :       Tuple  )		->		str:
         set_requires_grad(self.vae							,       _A  )





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





     def 						UpperCAmelCase(self :       Union[str, Any]  )		->		int:
         set_requires_grad(self.unet							,       _A  )





     @torch.enable_grad()
     def 						UpperCAmelCase(self :       Tuple							,       _A :       Optional[Any]							,       _A :       List[Any]							,       _A :       Union[str, Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       List[str]							,       _A :       List[str]							,       _A :       List[str]							,       _A :       int=True							,       )		->		str:
         snake_case						=   latents.detach().requires_grad_()

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

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

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

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

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

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

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

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

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

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





     @torch.no_grad()
     def __call__(self :       Any							,       _A :       Union[str, List[str]]							,       _A :       Optional[int] = 5_1_2							,       _A :       Optional[int] = 5_1_2							,       _A :       Optional[int] = 5_0							,       _A :       Optional[float] = 7.5							,       _A :       Optional[int] = 1							,       _A :       float = 0.0							,       _A :       Optional[float] = 1_0_0							,       _A :       Optional[Union[str, List[str]]] = None							,       _A :       Optional[int] = 4							,       _A :       Optional[bool] = True							,       _A :       Optional[torch.Generator] = None							,       _A :       Optional[torch.FloatTensor] = None							,       _A :       Optional[str] = "pil"							,       _A :       bool = True							,       )		->		Optional[int]:
         if isinstance(_A							,       _A  ):
             snake_case						=   1
         elif isinstance(_A							,       _A  ):
             snake_case						=   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						=   self.tokenizer(
             _A							,       padding="max_length"							,       max_length=self.tokenizer.model_max_length							,       truncation=_A							,       return_tensors="pt"							,       )
         snake_case						=   self.text_encoder(text_input.input_ids.to(self.device  )  )[0]
         # duplicate text embeddings for each generation per prompt
         snake_case						=   text_embeddings.repeat_interleave(_A							,       dim=0  )

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

         # set timesteps
         snake_case						=   "offset" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
         snake_case						=   {}
         if accepts_offset:
             snake_case						=   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						=   self.scheduler.timesteps.to(self.device  )

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

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

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

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

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

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

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

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

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

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

         if not return_dict:
             return (image, None)

         return StableDiffusionPipelineOutput(images=_A							,       nsfw_content_detected=_A  )



_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

