



'''simple docstring'''





import bsa
import requests

def 	a_					(							_lowerCAmelCase	)				->				Union[str, Any]:
  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 	a_					(							_lowerCAmelCase = 5	)				->				Tuple:
  __lowerCamelCase	:	Union[str, Any]   	=			int(float(lowerCamelCase_	)	)
  if num_movies < 1:
    return ()
  __lowerCamelCase	:	Optional[Any]   	=			(
      'https://www.imdb.com/search/title?title_type='
      F'feature&sort=num_votes,desc&count={num_movies}'
  )
  __lowerCamelCase	:	Dict   	=			bsa.BeautifulSoup(requests.get(lowerCamelCase_	).content	,'html.parser'	)
  return tuple(
      get_movie_data_from_soup(lowerCamelCase_	)
      for movie in source.find_all('div'	,class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
   import json

   _UpperCamelCase							    =						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)
       )
   )











'''simple docstring'''





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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =42
  a_							 =None




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=0.999	,_lowerCAmelCase="cosine"	,)				->				Union[str, Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_lowerCAmelCase	):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_lowerCAmelCase	):
      return math.exp(t * -12.0	)

  else:
    raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'	)

  __lowerCamelCase	:	Union[str, Any]   	=			[]
  for i in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			i / num_diffusion_timesteps
    __lowerCamelCase	:	Optional[int]   	=			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase	) / alpha_bar_fn(_lowerCAmelCase	)	,_lowerCAmelCase	)	)
  return torch.tensor(_lowerCAmelCase	,dtype=torch.floataa	)






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =[e.name for e in KarrasDiffusionSchedulers]
  a_							 =1




  @register_to_config
  def __init__(			self				:     List[Any]      ,     _a				:     int = 1000      ,     _a				:     float = 0.0001      ,     _a				:     float = 0.02      ,     _a				:     str = "linear"      ,     _a				:     Optional[Union[np.ndarray, List[float]]] = None      ,     _a				:     Optional[bool] = False      ,     _a				:     str = "epsilon"      ,     _a				:     str = "linspace"      ,     _a				:     int = 0      ,     ) -> Tuple:
    if trained_betas is not None:
      __lowerCamelCase	:	Optional[int]   	=			torch.tensor(_a      ,     dtype=torch.floataa       )
    elif beta_schedule == "linear":
      __lowerCamelCase	:	List[str]   	=			torch.linspace(_a      ,     _a      ,     _a      ,     dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __lowerCamelCase	:	Optional[int]   	=			(
          torch.linspace(beta_start**0.5      ,     beta_end**0.5      ,     _a      ,     dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __lowerCamelCase	:	Any   	=			betas_for_alpha_bar(_a       )
    else:
      raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

    __lowerCamelCase	:	Optional[Any]   	=			1.0 - self.betas
    __lowerCamelCase	:	Optional[int]   	=			torch.cumprod(self.alphas      ,     dim=0       )

    __lowerCamelCase	:	str   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       )

    # setable values
    __lowerCamelCase	:	List[str]   	=			None
    __lowerCamelCase	:	int   	=			use_karras_sigmas
    self.set_timesteps(_a      ,     _a       )
    __lowerCamelCase	:	int   	=			[]
    __lowerCamelCase	:	Dict   	=			False




  @property
  def 						_lowercase					(			self				:     Optional[Any]       ) -> Any:
    # standard deviation of the initial noise distribution
    if self.config.timestep_spacing in ["linspace", "trailing"]:
      return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]       ) -> torch.FloatTensor:
    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	List[Any]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	int   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Any   	=			self.sigmas[step_index]
    __lowerCamelCase	:	List[str]   	=			sample / ((sigma**2 + 1) ** 0.5)
    __lowerCamelCase	:	Any   	=			True
    return sample




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     str      ,     _a				:     List[str]      ,     _a				:     Union[str, Any]       ) -> Optional[Any]:

    def lms_derivative(_a				:     List[str]       ):
      __lowerCamelCase	:	Any   	=			1.0
      for k in range(_a       ):
        if current_order == k:
          continue
        prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
      return prod

    __lowerCamelCase	:	Any   	=			integrate.quad(_a      ,     self.sigmas[t]      ,     self.sigmas[t + 1]      ,     epsrel=1e-4       )[0]

    return integrated_coeff




  def 						_lowercase					(			self				:     int      ,     _a				:     int      ,     _a				:     Union[str, torch.device] = None       ) -> str:
    __lowerCamelCase	:	Optional[Any]   	=			num_inference_steps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
      __lowerCamelCase	:	Any   	=			np.linspace(0      ,     self.config.num_train_timesteps - 1      ,     _a      ,     dtype=_a       )[
          ::-1
      ].copy()
    elif self.config.timestep_spacing == "leading":
      __lowerCamelCase	:	Union[str, Any]   	=			self.config.num_train_timesteps // self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __lowerCamelCase	:	Union[str, Any]   	=			(np.arange(0      ,     _a       ) * step_ratio).round()[::-1].copy().astype(_a       )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      __lowerCamelCase	:	int   	=			self.config.num_train_timesteps / self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __lowerCamelCase	:	str   	=			(np.arange(self.config.num_train_timesteps      ,     0      ,     -step_ratio       )).round().copy().astype(_a       )
      timesteps -= 1
    else:
      raise ValueError(
          f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'       )

    __lowerCamelCase	:	Optional[Any]   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	List[Any]   	=			np.log(_a       )
    __lowerCamelCase	:	Optional[int]   	=			np.interp(_a      ,     np.arange(0      ,     len(_a       )       )      ,     _a       )

    if self.use_karras_sigmas:
      __lowerCamelCase	:	int   	=			self._convert_to_karras(in_sigmas=_a       )
      __lowerCamelCase	:	str   	=			np.array([self._sigma_to_t(_a      ,     _a       ) for sigma in sigmas]       )

    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       ).to(device=_a       )
    if str(_a       ).startswith('mps'       ):
      # mps does not support float64
      __lowerCamelCase	:	List[str]   	=			torch.from_numpy(_a       ).to(_a      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	str   	=			torch.from_numpy(_a       ).to(device=_a       )

    __lowerCamelCase	:	List[Any]   	=			[]




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     List[Any]      ,     _a				:     int       ) -> Tuple:
    # get log sigma
    __lowerCamelCase	:	Optional[int]   	=			np.log(_a       )

    # get distribution
    __lowerCamelCase	:	int   	=			log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    __lowerCamelCase	:	Dict   	=			np.cumsum((dists >= 0)      ,     axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
    __lowerCamelCase	:	List[Any]   	=			low_idx + 1

    __lowerCamelCase	:	Optional[Any]   	=			log_sigmas[low_idx]
    __lowerCamelCase	:	str   	=			log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase	:	List[str]   	=			(low - log_sigma) / (low - high)
    __lowerCamelCase	:	List[str]   	=			np.clip(_a      ,     0      ,     1       )

    # transform interpolation to time range
    __lowerCamelCase	:	Dict   	=			(1 - w) * low_idx + w * high_idx
    __lowerCamelCase	:	List[str]   	=			t.reshape(sigma.shape       )
    return t




  def 						_lowercase					(			self				:     int      ,     _a				:     torch.FloatTensor       ) -> torch.FloatTensor:
    __lowerCamelCase	:	float   	=			in_sigmas[-1].item()
    __lowerCamelCase	:	float   	=			in_sigmas[0].item()

    __lowerCamelCase	:	Optional[Any]   	=			7.0  # 7.0 is the value used in the paper
    __lowerCamelCase	:	Any   	=			np.linspace(0      ,     1      ,     self.num_inference_steps       )
    __lowerCamelCase	:	Optional[Any]   	=			sigma_min ** (1 / rho)
    __lowerCamelCase	:	Tuple   	=			sigma_max ** (1 / rho)
    __lowerCamelCase	:	str   	=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas




  def 						_lowercase					(			self				:     Dict      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]      ,     _a				:     torch.FloatTensor      ,     _a				:     int = 4      ,     _a				:     bool = True      ,     ) -> Union[LMSDiscreteSchedulerOutput, Tuple]:
    if not self.is_scale_input_called:
      warnings.warn(
          'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
          'See `StableDiffusionPipeline` for a usage example.'       )

    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	Optional[int]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	List[str]   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Optional[int]   	=			self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      __lowerCamelCase	:	Optional[Any]   	=			sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
      # * c_out + input * c_skip
      __lowerCamelCase	:	List[str]   	=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
      __lowerCamelCase	:	Union[str, Any]   	=			model_output
    else:
      raise ValueError(
          f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

    # 2. Convert to an ODE derivative
    __lowerCamelCase	:	str   	=			(sample - pred_original_sample) / sigma
    self.derivatives.append(_a       )
    if len(self.derivatives       ) > order:
      self.derivatives.pop(0       )

    # 3. Compute linear multistep coefficients
    __lowerCamelCase	:	List[str]   	=			min(step_index + 1      ,     _a       )
    __lowerCamelCase	:	int   	=			[self.get_lms_coefficient(_a      ,     _a      ,     _a       ) for curr_order in range(_a       )]

    # 4. Compute previous sample based on the derivatives path
    __lowerCamelCase	:	Optional[Any]   	=			sample + sum(
        coeff * derivative for coeff, derivative in zip(_a      ,     reversed(self.derivatives       )       )       )

    if not return_dict:
      return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=_a      ,     pred_original_sample=_a       )




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

    __lowerCamelCase	:	List[str]   	=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    __lowerCamelCase	:	Union[str, Any]   	=			sigmas[step_indices].flatten()
    while len(sigma.shape       ) < len(original_samples.shape       ):
      __lowerCamelCase	:	List[Any]   	=			sigma.unsqueeze(-1       )

    __lowerCamelCase	:	Optional[Any]   	=			original_samples + noise * sigma
    return noisy_samples




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






