code: 
  
  
  '''simple docstring'''
  
  
  
  import bsa
  import requests
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     bsa.element.ResultSet     ):
  					return {
  					    "name": soup.ha.a.text,
  					    "genre": soup.find('span'		,   class_='genre'     ).text.strip(),
  					    "rating": soup.strong.text,
  					    "page_link": F"""https://www.imdb.com{soup.a.get("href"     )}""",
  					}
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 5     ):
  					__a    : Union[str, Any]      					=						int(float(_SCREAMING_SNAKE_CASE     )     )
  					if num_movies < 1:
  										return ()
  					__a    : str      					=						(
  					    'https://www.imdb.com/search/title?title_type='
  					    F"""feature&sort=num_votes,desc&count={num_movies}"""
  					)
  					__a    : Optional[Any]      					=						bsa.BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE     ).content		,   'html.parser'     )
  					return tuple(
  					    get_movie_data_from_soup(_SCREAMING_SNAKE_CASE     )
  					    for movie in source.find_all('div'		,   class_='lister-item mode-advanced'     )     )
  
  
  if __name__ == "__main__":
  	import json
  
  	__lowercase			:		Union[str, Any] =			int(input('How many movies would you like to see? '))
  	print(
  	    ', '.join(
  	        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
  	    )
  	)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, List, Optional, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import CMStochasticIterativeScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  __lowercase			:		List[str] =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __lowercase			:		Optional[int] =			'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __init__(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__()
  
  						self.register_modules(
  						    unet=__a		,       scheduler=__a		,       )
  
  						__a    : List[str]      					=						None
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if is_accelerate_available() and is_accelerate_version('>='		,       '0.14.0'							):
  											from accelerate import cpu_offload
  						else:
  											raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'							)
  
  						__a    : str      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						for cpu_offloaded_model in [self.unet]:
  											cpu_offload(__a		,       __a							)
  
  						if self.safety_checker is not None:
  											cpu_offload(self.safety_checker		,       execution_device=__a		,       offload_buffers=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=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.'							)
  
  						__a    : Tuple      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						__a    : List[Any]      					=						None
  						for cpu_offloaded_model in [self.unet]:
  											__a					,       __a    : List[str]      					=						cpu_offload_with_hook(__a		,       __a		,       prev_module_hook=__a							)
  
  						if self.safety_checker is not None:
  											__a					,       __a    : str      					=						cpu_offload_with_hook(self.safety_checker		,       __a		,       prev_module_hook=__a							)
  
  						# We'll offload the last model manually.
  						__a    : List[Any]      					=						hook
  
  
  
  
  	@property
  	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if not hasattr(self.unet		,       '_hf_hook'							):
  											return self.device
  						for module in self.unet.modules():
  											if (
  											    hasattr(__a		,       '_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
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						(batch_size, num_channels, height, width)
  						if isinstance(__a		,       __a							) and len(__a							) != batch_size:
  											raise ValueError(
  											    f"""You have passed a list of generators of length {len(__a							)}, but requested an effective batch"""
  											    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)
  
  						if latents is None:
  											__a    : Optional[Any]      					=						randn_tensor(__a		,       generator=__a		,       device=__a		,       dtype=__a							)
  						else:
  											__a    : Dict      					=						latents.to(device=__a		,       dtype=__a							)
  
  						# scale the initial noise by the standard deviation required by the scheduler
  						__a    : Union[str, Any]      					=						latents * self.scheduler.init_noise_sigma
  						return latents
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = "pil"							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if output_type not in ["pt", "np", "pil"]:
  											raise ValueError(
  											    f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""							)
  
  						# Equivalent to diffusers.VaeImageProcessor.denormalize
  						__a    : List[Any]      					=						(sample / 2 + 0.5).clamp(0		,       1							)
  						if output_type == "pt":
  											return sample
  
  						# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  						__a    : str      					=						sample.cpu().permute(0		,       2		,       3		,       1							).numpy()
  						if output_type == "np":
  											return sample
  
  						# Output_type must be 'pil'
  						__a    : Union[str, Any]      					=						self.numpy_to_pil(__a							)
  						return sample
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if self.unet.config.num_class_embeds is not None:
  											if isinstance(__a		,       __a							):
  																__a    : Tuple      					=						torch.tensor(__a		,       dtype=torch.int							)
  											elif isinstance(__a		,       __a							):
  																assert batch_size == 1, "Batch size must be 1 if classes is an int"
  																__a    : int      					=						torch.tensor([class_labels]		,       dtype=torch.int							)
  											elif class_labels is None:
  																# Randomly generate batch_size class labels
  																# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
  																__a    : str      					=						torch.randint(0		,       self.unet.config.num_class_embeds		,       size=(batch_size,)							)
  											__a    : str      					=						class_labels.to(__a							)
  						else:
  											__a    : Optional[int]      					=						None
  						return class_labels
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if num_inference_steps is None and timesteps is None:
  											raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'							)
  
  						if num_inference_steps is not None and timesteps is not None:
  											logger.warning(
  											    f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
  											    ' `timesteps` will be used over `num_inference_steps`.'							)
  
  						if latents is not None:
  											__a    : Optional[Any]      					=						(batch_size, 3, img_size, img_size)
  											if latents.shape != expected_shape:
  																raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""							)
  
  						if (callback_steps is None) or (
  						    callback_steps is not None and (not isinstance(__a		,       __a							) or callback_steps <= 0)
  						):
  											raise ValueError(
  											    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
  											    f""" {type(__a							)}."""							)
  
  
  
  
  
  	@torch.no_grad()
  	@replace_example_docstring(__a							)
  	def __call__(     self		,       __a = 1		,       __a = None		,       __a = 1		,       __a = None		,       __a = None		,       __a = None		,       __a = "pil"		,       __a = True		,       __a = None		,       __a = 1		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						self.unet.config.sample_size
  						__a    : Union[str, Any]      					=						self._execution_device
  
  						# 1. Check inputs
  						self.check_inputs(__a		,       __a		,       __a		,       __a		,       __a		,       __a							)
  
  						# 2. Prepare image latents
  						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
  						__a    : Union[str, Any]      					=						self.prepare_latents(
  						    batch_size=__a		,       num_channels=self.unet.config.in_channels		,       height=__a		,       width=__a		,       dtype=self.unet.dtype		,       device=__a		,       generator=__a		,       latents=__a		,       )
  
  						# 3. Handle class_labels for class-conditional models
  						__a    : Optional[Any]      					=						self.prepare_class_labels(__a		,       __a		,       class_labels=__a							)
  
  						# 4. Prepare timesteps
  						if timesteps is not None:
  											self.scheduler.set_timesteps(timesteps=__a		,       device=__a							)
  											__a    : Union[str, Any]      					=						self.scheduler.timesteps
  											__a    : Tuple      					=						len(__a							)
  						else:
  											self.scheduler.set_timesteps(__a							)
  											__a    : Tuple      					=						self.scheduler.timesteps
  
  						# 5. Denoising loop
  						# Multistep sampling: implements Algorithm 1 in the paper
  						with self.progress_bar(total=__a							) as progress_bar:
  											for i, t in enumerate(__a							):
  																__a    : Optional[int]      					=						self.scheduler.scale_model_input(__a		,       __a							)
  																__a    : List[str]      					=						self.unet(__a		,       __a		,       class_labels=__a		,       return_dict=__a							)[0]
  
  																__a    : int      					=						self.scheduler.step(__a		,       __a		,       __a		,       generator=__a							)[0]
  
  																# call the callback, if provided
  																progress_bar.update()
  																if callback is not None and i % callback_steps == 0:
  																					callback(__a		,       __a		,       __a							)
  
          # 6. Post-process image sample
  						__a    : Tuple      					=						self.postprocess_image(__a		,       output_type=__a							)
  
  						# Offload last model to CPU
  						if hasattr(self		,       'final_offload_hook'							) and self.final_offload_hook is not None:
  											self.final_offload_hook.offload()
  
  						if not return_dict:
  											return (image,)
  
  						return ImagePipelineOutput(images=__a							)
  
  
  
  