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					,   lowerCAmelCase__					,   lowerCAmelCase__=1.0							):
            super().__init__()

            _A=						cut_size
            _A=						cut_power





     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A,      _A=						pixel_values.shape[2:4]
            _A=						min(lowerCAmelCase__					,   lowerCAmelCase__							)
            _A=						min(lowerCAmelCase__					,   lowerCAmelCase__					,   self.cut_size							)
            _A=						[]
            for _ in range(lowerCAmelCase__							):
                   _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(lowerCAmelCase__					,   self.cut_size							)							)
            return torch.cat(lowerCAmelCase__							)


def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    Any:




       '''simple docstring'''






       _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 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    List[str]:




       '''simple docstring'''






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





class   lowerCAmelCase    (       _a      ):



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

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

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



     def       a__	(       self					,   lowerCAmelCase__ = "auto"							):
            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(lowerCAmelCase__							)



     def       a__	(       self							):
            self.enable_attention_slicing(lowerCAmelCase__							)



     def       a__	(       self							):
            set_requires_grad(self.vae					,   lowerCAmelCase__							)



     def       a__	(       self							):
            set_requires_grad(self.vae					,   lowerCAmelCase__							)



     def       a__	(       self							):
            set_requires_grad(self.unet					,   lowerCAmelCase__							)



     def       a__	(       self							):
            set_requires_grad(self.unet					,   lowerCAmelCase__							)



     @torch.enable_grad()
     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__=True					,   ):
            _A=						latents.detach().requires_grad_()

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

            # predict the noise residual
            _A=						self.unet(lowerCAmelCase__					,   lowerCAmelCase__					,   encoder_hidden_states=lowerCAmelCase__							).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(lowerCAmelCase__							)
                   _A=						pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler					,   lowerCAmelCase__							):
                   _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(lowerCAmelCase__							).sample
            _A=						(image / 2 + 0.5).clamp(0					,   1							)

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

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

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

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

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





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

            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(
                lowerCAmelCase__					,   padding='max_length'					,   max_length=self.tokenizer.model_max_length					,   truncation=lowerCAmelCase__					,   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(lowerCAmelCase__					,   dim=0							)

            if clip_guidance_scale > 0:
                   if clip_prompt is not None:
                          _A=						self.tokenizer(
                              lowerCAmelCase__					,   padding='max_length'					,   max_length=self.tokenizer.model_max_length					,   truncation=lowerCAmelCase__					,   return_tensors='pt'					,   ).input_ids.to(self.device							)
                   else:
                          _A=						text_input.input_ids.to(self.device							)
                   _A=						self.clip_model.get_text_features(lowerCAmelCase__							)
                   _A=						text_embeddings_clip / text_embeddings_clip.norm(p=2					,   dim=-1					,   keepdim=lowerCAmelCase__							)
                   # duplicate text embeddings clip for each generation per prompt
                   _A=						text_embeddings_clip.repeat_interleave(lowerCAmelCase__					,   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=lowerCAmelCase__					,   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(lowerCAmelCase__					,   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(lowerCAmelCase__					,   generator=lowerCAmelCase__					,   device='cpu'					,   dtype=lowerCAmelCase__							).to(
                              self.device							)
                   else:
                          _A=						torch.randn(lowerCAmelCase__					,   generator=lowerCAmelCase__					,   device=self.device					,   dtype=lowerCAmelCase__							)
            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(lowerCAmelCase__					,   **lowerCAmelCase__							)

            # 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(lowerCAmelCase__							)							):
                   # 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(lowerCAmelCase__					,   lowerCAmelCase__							)

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

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                          _A,      _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,      _A=						self.cond_fn(
                              lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   )

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

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

            if not return_dict:
                   return (image, None)

            return StableDiffusionPipelineOutput(images=lowerCAmelCase__					,   nsfw_content_detected=lowerCAmelCase__							)
import argparse
import os


UpperCAmelCase_    		=				{
    '''pytorch''': {
        '''1.13''': {
            '''torch''': '''1.13.1''',
            '''torchvision''': '''0.14.1''',
            '''torchaudio''': '''0.13.1''',
            '''python''': 3.9,
            '''cuda''': '''cu116''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu116'''
            ),
            '''base_image''': '''nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.12''': {
            '''torch''': '''1.12.1''',
            '''torchvision''': '''0.13.1''',
            '''torchaudio''': '''0.12.1''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.11''': {
            '''torch''': '''1.11.0''',
            '''torchvision''': '''0.12.0''',
            '''torchaudio''': '''0.11.0''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.10''': {
            '''torch''': '''1.10.2''',
            '''torchvision''': '''0.11.3''',
            '''torchaudio''': '''0.10.2''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        '''1.9''': {
            '''torch''': '''1.9.1''',
            '''torchvision''': '''0.10.1''',
            '''torchaudio''': '''0.9.1''',
            '''python''': 3.9,
            '''cuda''': '''cu111''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu111'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
    '''tensorflow''': {
        '''2.11''': {
            '''tensorflow''': '''2.11.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.11.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.10''': {
            '''tensorflow''': '''2.10.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.10.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.9''': {
            '''tensorflow''': '''2.9.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.9.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.8''': {
            '''tensorflow''': '''2.8.2''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.8.2''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.7''': {
            '''tensorflow''': '''2.7.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.7.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.6''': {
            '''tensorflow''': '''2.6.5''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.6.5''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.5''': {
            '''tensorflow''': '''2.5.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.5.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
}


if __name__ == "__main__":
      UpperCAmelCase_    		=				argparse.ArgumentParser('''Choose the framework and version to install''')
      parser.add_argument(
          '''--framework''', help='''The framework to install. Should be `torch` or `tensorflow`''', type=str, required=True
      )
      parser.add_argument('''--version''', help='''The version of the framework to install.''', type=str, required=True)
      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				past_versions_testing[args.framework][args.version]

      os.system(F"echo \"export INSTALL_CMD=\'{info['install']}\'\" >> ~/.profile")
      print(F"echo \"export INSTALL_CMD=\'{info['install']}\'\" >> ~/.profile")

      UpperCAmelCase_    		=				''''''
      if args.framework == "pytorch":
            UpperCAmelCase_    		=				info['''cuda''']
      os.system(F"echo \"export CUDA='{cuda}'\" >> ~/.profile")
      print(F"echo \"export CUDA='{cuda}'\" >> ~/.profile")