
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_snake_case      = logging.get_logger(__name__)  # pylint: disable=invalid-name
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			AutoencoderKL					, SCREAMING_SNAKE_CASE__			:			CLIPTextModel					, SCREAMING_SNAKE_CASE__			:			CLIPTokenizer					, SCREAMING_SNAKE_CASE__			:			UNetaDConditionModel					, SCREAMING_SNAKE_CASE__			:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					, SCREAMING_SNAKE_CASE__			:			StableDiffusionSafetyChecker					, SCREAMING_SNAKE_CASE__			:			CLIPImageProcessor					, ):






      """simple docstring"""



      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__					, safety_checker=SCREAMING_SNAKE_CASE__					, feature_extractor=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, int]] = "auto"   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      self.enable_attention_slicing(SCREAMING_SNAKE_CASE__   )






 @torch.no_grad()
 def __call__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, List[str]]					, SCREAMING_SNAKE_CASE__			:			int = 5_12					, SCREAMING_SNAKE_CASE__			:			int = 5_12					, SCREAMING_SNAKE_CASE__			:			int = 50					, SCREAMING_SNAKE_CASE__			:			float = 7.5					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, List[str]]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[int] = 1					, SCREAMING_SNAKE_CASE__			:			float = 0.0					, SCREAMING_SNAKE_CASE__			:			Optional[torch.Generator] = None					, SCREAMING_SNAKE_CASE__			:			Optional[torch.FloatTensor] = None					, SCREAMING_SNAKE_CASE__			:			Optional[str] = "pil"					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, SCREAMING_SNAKE_CASE__			:			int = 1					, SCREAMING_SNAKE_CASE__			:			Optional[torch.FloatTensor] = None					, **SCREAMING_SNAKE_CASE__			:			Optional[Any]					, ):






      """simple docstring"""



      if isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ):
           UpperCamelCase						    =					1
      elif isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ):
           UpperCamelCase						    =					len(SCREAMING_SNAKE_CASE__   )
      else:
           raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_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}.'   )

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) or callback_steps <= 0)
      ):
           raise ValueError(
               F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
               F' {type(SCREAMING_SNAKE_CASE__   )}.'   )

      # get prompt text embeddings
      UpperCamelCase						    =					self.tokenizer(
          SCREAMING_SNAKE_CASE__					, padding='max_length'					, max_length=self.tokenizer.model_max_length					, return_tensors='pt'					, )
      UpperCamelCase						    =					text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           UpperCamelCase						    =					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
           logger.warning(
               'The following part of your input was truncated because CLIP can only handle sequences up to'
               F' {self.tokenizer.model_max_length} tokens: {removed_text}'   )
           UpperCamelCase						    =					text_input_ids[:, : self.tokenizer.model_max_length]

      if text_embeddings is None:
           UpperCamelCase						    =					self.text_encoder(text_input_ids.to(self.device   )   )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					text_embeddings.shape
      UpperCamelCase						    =					text_embeddings.repeat(1					, SCREAMING_SNAKE_CASE__					, 1   )
      UpperCamelCase						    =					text_embeddings.view(bs_embed * num_images_per_prompt					, SCREAMING_SNAKE_CASE__					, -1   )

      # 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.
      UpperCamelCase						    =					guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           UpperCamelCase						    =					42
           if negative_prompt is None:
                UpperCamelCase						    =					['']
           elif type(SCREAMING_SNAKE_CASE__   ) is not type(SCREAMING_SNAKE_CASE__   ):
                raise TypeError(
                    F'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE__   )} !='
                    F' {type(SCREAMING_SNAKE_CASE__   )}.'   )
           elif isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ):
                UpperCamelCase						    =					[negative_prompt]
           elif batch_size != len(SCREAMING_SNAKE_CASE__   ):
                raise ValueError(
                    F'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE__   )}, but `prompt`:'
                    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                    ' the batch size of `prompt`.'   )
           else:
                UpperCamelCase						    =					negative_prompt

           UpperCamelCase						    =					text_input_ids.shape[-1]
           UpperCamelCase						    =					self.tokenizer(
               SCREAMING_SNAKE_CASE__					, padding='max_length'					, max_length=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__					, return_tensors='pt'					, )
           UpperCamelCase						    =					self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           UpperCamelCase						    =					uncond_embeddings.shape[1]
           UpperCamelCase						    =					uncond_embeddings.repeat(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, 1   )
           UpperCamelCase						    =					uncond_embeddings.view(batch_size * num_images_per_prompt					, SCREAMING_SNAKE_CASE__					, -1   )

           # 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
           UpperCamelCase						    =					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`.
      UpperCamelCase						    =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      UpperCamelCase						    =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
      UpperCamelCase						    =					text_embeddings.dtype
      if latents is None:
           if self.device.type == "mps":
                # randn does not exist on mps
                UpperCamelCase						    =					torch.randn(
                    SCREAMING_SNAKE_CASE__					, generator=SCREAMING_SNAKE_CASE__					, device='cpu'					, dtype=SCREAMING_SNAKE_CASE__   ).to(self.device   )
                UpperCamelCase						    =					torch.randn(SCREAMING_SNAKE_CASE__					, generator=SCREAMING_SNAKE_CASE__					, device='cpu'					, dtype=SCREAMING_SNAKE_CASE__   ).to(
                    self.device   )
           else:
                UpperCamelCase						    =					torch.randn(
                    SCREAMING_SNAKE_CASE__					, generator=SCREAMING_SNAKE_CASE__					, device=self.device					, dtype=SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					torch.randn(SCREAMING_SNAKE_CASE__					, generator=SCREAMING_SNAKE_CASE__					, device=self.device					, dtype=SCREAMING_SNAKE_CASE__   )
      else:
           if latents_reference.shape != latents_shape:
                raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
           UpperCamelCase						    =					latents_reference.to(self.device   )
           UpperCamelCase						    =					latents.to(self.device   )

      # This is the key part of the pipeline where we
      # try to ensure that the generated images w/ the same seed
      # but different sizes actually result in similar images
      UpperCamelCase						    =					(latents_shape[3] - latents_shape_reference[3]) // 2
      UpperCamelCase						    =					(latents_shape[2] - latents_shape_reference[2]) // 2
      UpperCamelCase						    =					latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
      UpperCamelCase						    =					latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
      UpperCamelCase						    =					0 if dx < 0 else dx
      UpperCamelCase						    =					0 if dy < 0 else dy
      UpperCamelCase						    =					max(-dx					, 0   )
      UpperCamelCase						    =					max(-dy					, 0   )
      # import pdb
      # pdb.set_trace()
      UpperCamelCase						    =					latents_reference[:, :, dy : dy + h, dx : dx + w]

      # set timesteps
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

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

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

           # predict the noise residual
           UpperCamelCase						    =					self.unet(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, encoder_hidden_states=SCREAMING_SNAKE_CASE__   ).sample

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

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

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
                callback(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					1 / 0.18215 * latents
      UpperCamelCase						    =					self.vae.decode(SCREAMING_SNAKE_CASE__   ).sample

      UpperCamelCase						    =					(image / 2 + 0.5).clamp(0					, 1   )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      UpperCamelCase						    =					image.cpu().permute(0					, 2					, 3					, 1   ).float().numpy()

      if self.safety_checker is not None:
           UpperCamelCase						    =					self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )					, return_tensors='pt'   ).to(
               self.device   )
           UpperCamelCase    ,		UpperCamelCase						    =					self.safety_checker(
               images=SCREAMING_SNAKE_CASE__					, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype   )   )
      else:
           UpperCamelCase						    =					None

      if output_type == "pil":
           UpperCamelCase						    =					self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

      if not return_dict:
           return (image, has_nsfw_concept)

      return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE__					, nsfw_content_detected=SCREAMING_SNAKE_CASE__   )




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
 # setup instructions, if using on-demand hardware

 # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
 # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
 # Throw an error if user passes both BYO and on-demand cluster args
 # Otherwise, use default values
 _snake_case      = argparse.ArgumentParser()
 parser.add_argument('''--user''', type=str, default='''ubuntu''')
 parser.add_argument('''--host''', type=str, default='''localhost''')
 parser.add_argument('''--key_path''', type=str, default=None)
 parser.add_argument('''--instance''', type=str, default='''V100:1''')
 parser.add_argument('''--provider''', type=str, default='''cheapest''')
 parser.add_argument('''--use_spot''', type=bool, default=False)
 parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
 _snake_case  ,      _snake_case      = parser.parse_known_args()
 if args.host != "localhost":
  if args.instance != "V100:1" or args.provider != "cheapest":
   raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
  _snake_case      = rh.cluster(
      name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
  )
 else:
  _snake_case      = rh.cluster(
      name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 _snake_case      = args.example.rsplit('''/''', 1)[0]

 # Set up remote environment
 cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
 # Note transformers is copied into the home directory on the remote machine, so we can install from there
 cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
 cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

 # Run example. You can bypass the CLI wrapper and paste your own code here.
 cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

 # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
 # from my_script... import train
 # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
 # launch_train_gpu = rh.function(fn=train,
 #                                system=gpu,
 #                                reqs=reqs,
 #                                name='train_bert_glue')
 #
 # We can pass in arguments just like we would to a function:
 # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
 #                  stream_logs=True)


