code: 
  
  """simple docstring"""
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from ...models import UNetaDConditionModel, VQModel
  from ...pipelines import DiffusionPipeline
  from ...pipelines.pipeline_utils import ImagePipelineOutput
  from ...schedulers import DDPMScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  
  
  _A						=     logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  _A						=     """
      Examples:
          ```py
          >>> import torch
          >>> import numpy as np
  
          >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
          >>> from transformers import pipeline
          >>> from diffusers.utils import load_image
  
  
          >>> def make_hint(image, depth_estimator):
          ...     image = depth_estimator(image)[\"depth\"]
          ...     image = np.array(image)
          ...     image = image[:, :, None]
          ...     image = np.concatenate([image, image, image], axis=2)
          ...     detected_map = torch.from_numpy(image).float() / 255.0
          ...     hint = detected_map.permute(2, 0, 1)
          ...     return hint
  
  
          >>> depth_estimator = pipeline(\"depth-estimation\")
  
          >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
          ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
          ... )
          >>> pipe_prior = pipe_prior.to(\"cuda\")
  
          >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
          ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
          ... )
          >>> pipe = pipe.to(\"cuda\")
  
          >>> img = load_image(
          ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
          ...     \"/kandinsky/cat.png\"
          ... ).resize((768, 768))
  
  
          >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
  
          >>> prompt = \"A robot, 4k photo\"
          >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
  
          >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
  
          >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
          >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
  
          >>> images = pipe(
          ...     image=img,
          ...     strength=0.5,
          ...     image_embeds=img_emb.image_embeds,
          ...     negative_image_embeds=negative_emb.image_embeds,
          ...     hint=hint,
          ...     num_inference_steps=50,
          ...     generator=generator,
          ...     height=768,
          ...     width=768,
          ... ).images
  
          >>> images[0].save(\"robot_cat.png\")
          ```
  """
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=8						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       Optional[Any]						=						height // scale_factor**2
        if height % scale_factor**2 != 0:
              new_height += 1
        UpperCAmelCase__ :       Optional[int]						=						width // scale_factor**2
        if width % scale_factor**2 != 0:
              new_width += 1
        return new_height * scale_factor, new_width * scale_factor
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=5_12 ,  lowerCAmelCase=5_12						)  ->   Optional[Any]:
        UpperCAmelCase__ :       Union[str, Any]						=						pil_image.resize((w, h) ,  resample=Image.BICUBIC ,  reducing_gap=1						)
        UpperCAmelCase__ :       List[Any]						=						np.array(pil_image.convert("""RGB"""						)						)
        UpperCAmelCase__ :       Optional[int]						=						arr.astype(np.floataa						) / 127.5 - 1
        UpperCAmelCase__ :       Optional[int]						=						np.transpose(lowerCAmelCase ,  [2, 0, 1]						)
        UpperCAmelCase__ :       List[str]						=						torch.from_numpy(lowerCAmelCase						).unsqueeze(0						)
        return image
  
  
  
  
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          super().__init__()
  
          self.register_modules(
              unet=_lowerCamelCase       ,					scheduler=_lowerCamelCase       ,					movq=_lowerCamelCase       ,					)
          UpperCAmelCase__ :       List[str]						=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						min(int(num_inference_steps * strength  )       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       str						=						max(num_inference_steps - init_timestep       ,					0  )
          UpperCAmelCase__ :       str						=						self.scheduler.timesteps[t_start:]
  
          return timesteps, num_inference_steps - t_start
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          if not isinstance(_lowerCamelCase       ,					(torch.Tensor, PIL.Image.Image, list)  ):
                raise ValueError(
                    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase  )}"""  )
  
          UpperCAmelCase__ :       Any						=						image.to(device=_lowerCamelCase       ,					dtype=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						batch_size * num_images_per_prompt
  
          if image.shape[1] == 4:
                UpperCAmelCase__ :       Any						=						image
  
          else:
                if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and len(_lowerCamelCase  ) != batch_size:
                      raise ValueError(
                          F"""You have passed a list of generators of length {len(_lowerCamelCase  )}, but requested an effective batch"""
                          F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )
  
                elif isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                      UpperCAmelCase__ :       int						=						[
                          self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(_lowerCamelCase  )
                      ]
                      UpperCAmelCase__ :       List[str]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
                else:
                      UpperCAmelCase__ :       Union[str, Any]						=						self.movq.encode(_lowerCamelCase  ).latent_dist.sample(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						self.movq.config.scaling_factor * init_latents
  
          UpperCAmelCase__ :       List[str]						=						torch.cat([init_latents]       ,					dim=0  )
  
          UpperCAmelCase__ :       List[str]						=						init_latents.shape
          UpperCAmelCase__ :       Tuple						=						randn_tensor(_lowerCamelCase       ,					generator=_lowerCamelCase       ,					device=_lowerCamelCase       ,					dtype=_lowerCamelCase  )
  
          # get latents
          UpperCAmelCase__ :       List[str]						=						self.scheduler.add_noise(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[Any]						=						init_latents
  
          return latents
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0  ):
  
  
          """simple docstring"""
  
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError("""Please install accelerate via `pip install accelerate`"""  )
  
          UpperCAmelCase__ :       Optional[Any]						=						torch.device(F"""cuda:{gpu_id}"""  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0  ):
  
  
          """simple docstring"""
  
          if is_accelerate_available() and is_accelerate_version(""">="""       ,					"""0.17.0.dev0"""  ):
                from accelerate import cpu_offload_with_hook
          else:
                raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )
  
          UpperCAmelCase__ :       str						=						torch.device(F"""cuda:{gpu_id}"""  )
  
          if self.device.type != "cpu":
                self.to("""cpu"""       ,					silence_dtype_warnings=_lowerCamelCase  )
                torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
          UpperCAmelCase__ :       Union[str, Any]						=						None
          for cpu_offloaded_model in [self.unet, self.movq]:
                UpperCAmelCase__				,		UpperCAmelCase__ :       Union[str, Any]						=						cpu_offload_with_hook(_lowerCamelCase       ,					_lowerCamelCase       ,					prev_module_hook=_lowerCamelCase  )
  
          # We'll offload the last model manually.
          UpperCAmelCase__ :       List[str]						=						hook
  
  
  
    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          if not hasattr(self.unet       ,					"""_hf_hook"""  ):
                return self.device
          for module in self.unet.modules():
                if (
                    hasattr(_lowerCamelCase       ,					"""_hf_hook"""  )
                    and hasattr(module._hf_hook       ,					"""execution_device"""  )
                    and module._hf_hook.execution_device is not None
                ):
                      return torch.device(module._hf_hook.execution_device  )
          return self.device
  
  
  
  
    @torch.no_grad()
    @replace_example_docstring(_lowerCamelCase  )
    def __call__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 100       ,					_lowerCamelCase = 4.0       ,					_lowerCamelCase = 0.3       ,					_lowerCamelCase = 1       ,					_lowerCamelCase = None       ,					_lowerCamelCase = "pil"       ,					_lowerCamelCase = True       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self._execution_device
  
          UpperCAmelCase__ :       Dict						=						guidance_scale > 1.0
  
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Optional[int]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Union[str, Any]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Any						=						torch.cat(_lowerCamelCase       ,					dim=0  )
  
          UpperCAmelCase__ :       Tuple						=						image_embeds.shape[0]
  
          if do_classifier_free_guidance:
                UpperCAmelCase__ :       Union[str, Any]						=						image_embeds.repeat_interleave(_lowerCamelCase       ,					dim=0  )
                UpperCAmelCase__ :       int						=						negative_image_embeds.repeat_interleave(_lowerCamelCase       ,					dim=0  )
                UpperCAmelCase__ :       str						=						hint.repeat_interleave(_lowerCamelCase       ,					dim=0  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						torch.cat([negative_image_embeds, image_embeds]       ,					dim=0  ).to(dtype=self.unet.dtype       ,					device=_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[int]						=						torch.cat([hint, hint]       ,					dim=0  ).to(dtype=self.unet.dtype       ,					device=_lowerCamelCase  )
  
          if not isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Optional[int]						=						[image]
          if not all(isinstance(_lowerCamelCase       ,					(PIL.Image.Image, torch.Tensor)  ) for i in image  ):
                raise ValueError(
                    F"""Input is in incorrect format: {[type(_lowerCamelCase  ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""  )
  
          UpperCAmelCase__ :       Any						=						torch.cat([prepare_image(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ) for i in image]       ,					dim=0  )
          UpperCAmelCase__ :       Tuple						=						image.to(dtype=image_embeds.dtype       ,					device=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						self.movq.encode(_lowerCamelCase  )["""latents"""]
          UpperCAmelCase__ :       List[str]						=						latents.repeat_interleave(_lowerCamelCase       ,					dim=0  )
          self.scheduler.set_timesteps(_lowerCamelCase       ,					device=_lowerCamelCase  )
          UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						self.get_timesteps(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[Any]						=						timesteps[:1].repeat(batch_size * num_images_per_prompt  )
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						downscale_height_and_width(_lowerCamelCase       ,					_lowerCamelCase       ,					self.movq_scale_factor  )
          UpperCAmelCase__ :       List[Any]						=						self.prepare_latents(
              _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					image_embeds.dtype       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          for i, t in enumerate(self.progress_bar(_lowerCamelCase  )  ):
                # expand the latents if we are doing classifier free guidance
                UpperCAmelCase__ :       str						=						torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
  
                UpperCAmelCase__ :       List[Any]						=						{"""image_embeds""": image_embeds, """hint""": hint}
                UpperCAmelCase__ :       Optional[int]						=						self.unet(
                    sample=_lowerCamelCase       ,					timestep=_lowerCamelCase       ,					encoder_hidden_states=_lowerCamelCase       ,					added_cond_kwargs=_lowerCamelCase       ,					return_dict=_lowerCamelCase       ,					)[0]
  
                if do_classifier_free_guidance:
                      UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						noise_pred.split(latents.shape[1]       ,					dim=1  )
                      UpperCAmelCase__				,		UpperCAmelCase__ :       int						=						noise_pred.chunk(2  )
                      UpperCAmelCase__				,		UpperCAmelCase__ :       Union[str, Any]						=						variance_pred.chunk(2  )
                      UpperCAmelCase__ :       Dict						=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      UpperCAmelCase__ :       int						=						torch.cat([noise_pred, variance_pred_text]       ,					dim=1  )
  
                if not (
                    hasattr(self.scheduler.config       ,					"""variance_type"""  )
                    and self.scheduler.config.variance_type in ["learned", "learned_range"]
                ):
                      UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[Any]						=						noise_pred.split(latents.shape[1]       ,					dim=1  )
  
                # compute the previous noisy sample x_t -> x_t-1
  
                UpperCAmelCase__ :       Dict						=						self.scheduler.step(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					)[0]
  
          # post-processing
          UpperCAmelCase__ :       Dict						=						self.movq.decode(_lowerCamelCase       ,					force_not_quantize=_lowerCamelCase  )["""sample"""]
  
          if output_type not in ["pt", "np", "pil"]:
                raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""  )
  
          if output_type in ["np", "pil"]:
                UpperCAmelCase__ :       int						=						image * 0.5 + 0.5
                UpperCAmelCase__ :       List[str]						=						image.clamp(0       ,					1  )
                UpperCAmelCase__ :       Dict						=						image.cpu().permute(0       ,					2       ,					3       ,					1  ).float().numpy()
  
          if output_type == "pil":
                UpperCAmelCase__ :       int						=						self.numpy_to_pil(_lowerCamelCase  )
  
          if not return_dict:
                return (image,)
  
          return ImagePipelineOutput(images=_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  _A						=     {
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  _A						=     [
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
        parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
        parser.add_argument(
            """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
        parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
        parser.add_argument(
            """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
        parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
        parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()
  
        # Sanitize args
        if args.model_name in DEFAULT_MODELS:
              UpperCAmelCase__ :       Any						=						DEFAULT_MODELS[args.model_name]
              UpperCAmelCase__ :       Tuple						=						defaults["""model"""]
              if args.tokenizer_name is None:
                    UpperCAmelCase__ :       Optional[Any]						=						defaults.get("""tokenizer""" ,  args.model_name						)
              if args.is_causal is None:
                    UpperCAmelCase__ :       List[Any]						=						defaults["""is_causal"""]
              if args.model_revision is None:
                    UpperCAmelCase__ :       Optional[int]						=						defaults.get("""model_revision""" ,  """main"""						)
  
        if args.is_causal is None:
              raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""						)
        if args.tokenizer_name is None:
              UpperCAmelCase__ :       str						=						args.model_name
        if args.model_revision is None:
              UpperCAmelCase__ :       List[str]						=						"""main"""
  
        return args
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        transformers.utils.logging.set_verbosity_error()
        UpperCAmelCase__ :       Optional[int]						=						parse_args()
  
        if args.torch_dtype is None:
              UpperCAmelCase__ :       List[str]						=						AutoConfig.from_pretrained(args.model_name						)
              UpperCAmelCase__ :       Optional[Any]						=						getattr(lowerCAmelCase ,  """torch_dtype""" ,  torch.floataa						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						getattr(lowerCAmelCase ,  args.torch_dtype						)
        UpperCAmelCase__ :       List[Any]						=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
        UpperCAmelCase__ :       List[str]						=						{
            """torch_dtype""": torch_dtype,
            """revision""": args.model_revision,
        }
        if args.disk_offload:
              UpperCAmelCase__ :       List[str]						=						"""tmp_offload"""
              UpperCAmelCase__ :       int						=						True
  
        UpperCAmelCase__ :       Any						=						start_measure()
        UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model loading"""						)
  
        UpperCAmelCase__ :       List[str]						=						compute_module_sizes(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						{v: 0 for v in model.hf_device_map.values()}
        for module, device in model.hf_device_map.items():
              device_size[device] += module_sizes[module]
        UpperCAmelCase__ :       Optional[Any]						=						"""\n""".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
        print(F"""\nTheoretical use:\n{message}"""						)
  
        UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)
  
        UpperCAmelCase__ :       List[str]						=						start_measure()
        UpperCAmelCase__ :       List[Any]						=						[]
        UpperCAmelCase__ :       Tuple						=						[]
        UpperCAmelCase__ :       Optional[Any]						=						[]
        for prompt in PROMPTS:
              UpperCAmelCase__ :       Any						=						tokenizer(lowerCAmelCase ,  return_tensors="""pt"""						).to(0						)
              UpperCAmelCase__ :       List[str]						=						inputs["""input_ids"""][0].tolist()
              UpperCAmelCase__ :       Dict						=						time.time()
              UpperCAmelCase__ :       List[str]						=						model.generate(inputs["""input_ids"""]						)
              UpperCAmelCase__ :       str						=						time.time()
              UpperCAmelCase__ :       str						=						outputs[0].tolist()
              UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) if outputs[: len(lowerCAmelCase						)] != tokens else len(lowerCAmelCase						) - len(lowerCAmelCase						)
              UpperCAmelCase__ :       Tuple						=						after_generate - before_generate
  
              UpperCAmelCase__ :       Optional[Any]						=						tokenizer.decode(lowerCAmelCase ,  skip_special_tokens=lowerCAmelCase						)
              texts_outs.append(lowerCAmelCase						)
              generation_times.append(lowerCAmelCase						)
              gen_tokens.append(lowerCAmelCase						)
              print(F"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""						)
  
        UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model generation"""						)
  
        UpperCAmelCase__ :       Any						=						[gen / tok for gen, tok in zip(lowerCAmelCase ,  lowerCAmelCase						)]
        UpperCAmelCase__ :       List[Any]						=						sum(lowerCAmelCase						) / len(lowerCAmelCase						)
        print(F"""Average time of generation per token: {avg_gen:.2f}s"""						)
        print(F"""First generation (avg time per token): {generation_times_per_token[0]:.2f}s"""						)
        UpperCAmelCase__ :       Any						=						sum(generation_times_per_token[1:]						) / (len(lowerCAmelCase						) - 1)
        print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""						)
  
  
  if __name__ == "__main__":
      main()
  
  