



from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


lowerCAmelCase					:				Union[str, Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class 	_a			(      snake_case_  ):
			_UpperCamelCase:   torch.FloatTensor






class 	_a			(      snake_case_     , snake_case_  ):
			_UpperCamelCase:   List[Any]	     =		1






			@register_to_config
			def __init__(    self    ,  lowercase_ = 40    ,  lowercase_ = 0.0_0_2    ,  lowercase_ = 8_0.0    ,  lowercase_ = 0.5    ,  lowercase_ = 1.0    ,  lowercase_ = 7.0    ,  lowercase_ = True    ,  )	->					Any:
									# standard deviation of the initial noise distribution
									lowerCAmelCase	:   List[Any]					     =					sigma_max

									lowerCAmelCase	:   Union[str, Any]					     =					np.linspace(0    ,  1    ,  lowercase_ )
									lowerCAmelCase	:   Tuple					     =					self._convert_to_karras(lowercase_ )
									lowerCAmelCase	:   List[Any]					     =					self.sigma_to_t(lowercase_ )

									# setable values
									lowerCAmelCase	:   Optional[int]					     =					None
									lowerCAmelCase	:   Dict					     =					torch.from_numpy(lowercase_ )
									lowerCAmelCase	:   Optional[Any]					     =					torch.from_numpy(lowercase_ )
									lowerCAmelCase	:   Optional[int]					     =					False
									lowerCAmelCase	:   List[str]					     =					False






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_=None )	->					Optional[Any]:
									if schedule_timesteps is None:
															lowerCAmelCase	:   Dict					     =					self.timesteps

									lowerCAmelCase	:   int					     =					(schedule_timesteps == timestep).nonzero()
									return indices.item()






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					torch.FloatTensor:
									# Get sigma corresponding to timestep
									if isinstance(lowercase_    ,  torch.Tensor ):
															lowerCAmelCase	:   Optional[Any]					     =					timestep.to(self.timesteps.device )
									lowerCAmelCase	:   Dict					     =					self.index_for_timestep(lowercase_ )
									lowerCAmelCase	:   Any					     =					self.sigmas[step_idx]

									lowerCAmelCase	:   Tuple					     =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									lowerCAmelCase	:   Dict					     =					True
									return sample






			def 							_snake_case   (    self    ,  lowercase_ )	->					Tuple:
									if not isinstance(lowercase_    ,  np.ndarray ):
															lowerCAmelCase	:   Optional[int]					     =					np.array(lowercase_    ,  dtype=np.floataa )

									lowerCAmelCase	:   Tuple					     =					1000 * 0.2_5 * np.log(sigmas + 1e-44 )

									return timesteps






			def 							_snake_case   (    self    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = None    ,  )	->					str:
									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:
															raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`.""" )

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
															for i in range(1    ,  len(lowercase_ ) ):
																					if timesteps[i] >= timesteps[i - 1]:
																											raise ValueError("""`timesteps` must be in descending order.""" )

															if timesteps[0] >= self.config.num_train_timesteps:
																					raise ValueError(
																					    f"""`timesteps` must start before `self.config.train_timesteps`:"""
																					    f""" {self.config.num_train_timesteps}.""" )

															lowerCAmelCase	:   str					     =					np.array(lowercase_    ,  dtype=np.intaa )
															lowerCAmelCase	:   Optional[Any]					     =					True
									else:
															if num_inference_steps > self.config.num_train_timesteps:
																					raise ValueError(
																					    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																					    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																					    f""" maximal {self.config.num_train_timesteps} timesteps.""" )

															lowerCAmelCase	:   Dict					     =					num_inference_steps

															lowerCAmelCase	:   Any					     =					self.config.num_train_timesteps // self.num_inference_steps
															lowerCAmelCase	:   List[str]					     =					(np.arange(0    ,  lowercase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
															lowerCAmelCase	:   List[Any]					     =					False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									lowerCAmelCase	:   str					     =					self.config.num_train_timesteps
									lowerCAmelCase	:   int					     =					timesteps[::-1].copy()
									lowerCAmelCase	:   Tuple					     =					ramp / (num_train_timesteps - 1)
									lowerCAmelCase	:   Any					     =					self._convert_to_karras(lowercase_ )
									lowerCAmelCase	:   str					     =					self.sigma_to_t(lowercase_ )

									lowerCAmelCase	:   int					     =					np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
									lowerCAmelCase	:   List[Any]					     =					torch.from_numpy(lowercase_ ).to(device=lowercase_ )

									if str(lowercase_ ).startswith("""mps""" ):
															# mps does not support float64
															lowerCAmelCase	:   List[Any]					     =					torch.from_numpy(lowercase_ ).to(lowercase_    ,  dtype=torch.floataa )
									else:
															lowerCAmelCase	:   str					     =					torch.from_numpy(lowercase_ ).to(device=lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_ )	->					Union[str, Any]:
									lowerCAmelCase	:   float					     =					self.config.sigma_min
									lowerCAmelCase	:   float					     =					self.config.sigma_max

									lowerCAmelCase	:   int					     =					self.config.rho
									lowerCAmelCase	:   List[Any]					     =					sigma_min ** (1 / rho)
									lowerCAmelCase	:   Any					     =					sigma_max ** (1 / rho)
									lowerCAmelCase	:   Optional[Any]					     =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									lowerCAmelCase	:   List[str]					     =					self.config.sigma_data

									lowerCAmelCase	:   Union[str, Any]					     =					sigma_data**2 / (sigma**2 + sigma_data**2)
									lowerCAmelCase	:   Dict					     =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out






			def 							_snake_case   (    self    ,  lowercase_ )	->					Union[str, Any]:
									lowerCAmelCase	:   List[str]					     =					self.config.sigma_min
									lowerCAmelCase	:   Any					     =					self.config.sigma_data

									lowerCAmelCase	:   str					     =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									lowerCAmelCase	:   Tuple					     =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ = None    ,  lowercase_ = True    ,  )	->					Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									if (
									    isinstance(lowercase_    ,  lowercase_ )
									    or isinstance(lowercase_    ,  torch.IntTensor )
									    or isinstance(lowercase_    ,  torch.LongTensor )
									):
															raise ValueError(
															    (
															        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
															        f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
															        """ one of the `scheduler.timesteps` as a timestep."""
															    )    ,  )

									if not self.is_scale_input_called:
															logger.warning(
															    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
															    """See `StableDiffusionPipeline` for a usage example.""" )

									if isinstance(lowercase_    ,  torch.Tensor ):
															lowerCAmelCase	:   Dict					     =					timestep.to(self.timesteps.device )

									lowerCAmelCase	:   Union[str, Any]					     =					self.config.sigma_min
									lowerCAmelCase	:   Any					     =					self.config.sigma_max

									lowerCAmelCase	:   int					     =					self.index_for_timestep(lowercase_ )

									# sigma_next corresponds to next_t in original implementation
									lowerCAmelCase	:   Union[str, Any]					     =					self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
															lowerCAmelCase	:   Union[str, Any]					     =					self.sigmas[step_index + 1]
									else:
															# Set sigma_next to sigma_min
															lowerCAmelCase	:   Optional[int]					     =					self.sigmas[-1]

									# Get scalings for boundary conditions
									lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					self.get_scalings_for_boundary_condition(lowercase_ )

									# 1. Denoise model output using boundary conditions
									lowerCAmelCase	:   int					     =					c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
															lowerCAmelCase	:   Optional[int]					     =					denoised.clamp(-1    ,  1 )

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps ) > 1:
															lowerCAmelCase	:   List[str]					     =					randn_tensor(
															    model_output.shape    ,  dtype=model_output.dtype    ,  device=model_output.device    ,  generator=lowercase_ )
									else:
															lowerCAmelCase	:   List[str]					     =					torch.zeros_like(lowercase_ )
									lowerCAmelCase	:   Union[str, Any]					     =					noise * self.config.s_noise

									lowerCAmelCase	:   List[Any]					     =					sigma_next.clamp(min=lowercase_    ,  max=lowercase_ )

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									lowerCAmelCase	:   int					     =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
															return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )	->					torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									lowerCAmelCase	:   List[str]					     =					self.sigmas.to(device=original_samples.device    ,  dtype=original_samples.dtype )
									if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
															# mps does not support float64
															lowerCAmelCase	:   List[Any]					     =					self.timesteps.to(original_samples.device    ,  dtype=torch.floataa )
															lowerCAmelCase	:   Tuple					     =					timesteps.to(original_samples.device    ,  dtype=torch.floataa )
									else:
															lowerCAmelCase	:   Any					     =					self.timesteps.to(original_samples.device )
															lowerCAmelCase	:   Dict					     =					timesteps.to(original_samples.device )

									lowerCAmelCase	:   Any					     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									lowerCAmelCase	:   int					     =					sigmas[step_indices].flatten()
									while len(sigma.shape ) < len(original_samples.shape ):
															lowerCAmelCase	:   List[Any]					     =					sigma.unsqueeze(-1 )

									lowerCAmelCase	:   int					     =					original_samples + noise * sigma
									return noisy_samples






			def __len__(    self )	->					Union[str, Any]:
									return self.config.num_train_timesteps






import bsa
import requests







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						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  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__ = 5  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					int(float(SCREAMING_SNAKE_CASE__  )  )
						if num_movies < 1:
												return ()
						lowerCAmelCase	:   List[str]					     =					(
						    """https://www.imdb.com/search/title?title_type="""
						    F"""feature&sort=num_votes,desc&count={num_movies}"""
						)
						lowerCAmelCase	:   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

		lowerCAmelCase					:				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)
		    )
		)

