





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : List[Any]=0.9_9_9    ,       UpperCamelCase__    : Tuple="cosine"    ,       )		->  Any:






    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCamelCase__    : Any		):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCamelCase__    : Optional[int]		):
            return math.exp(t * -1_2.0		)

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

    A       :       Tuple							=						[]
    for i in range(_UpperCamelCase		):
        A       :       Dict							=						i / num_diffusion_timesteps
        A       :       Dict							=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_UpperCamelCase		) / alpha_bar_fn(_UpperCamelCase		)    ,       _UpperCamelCase		)		)
    return torch.tensor(_UpperCamelCase    ,       dtype=torch.floataa		)





class   _lowercase						(							a		,					a				):
   _UpperCamelCase       		=   [e.name for e in KarrasDiffusionSchedulers]
   _UpperCamelCase       		=   2


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase = 1_000    ,   _UpperCAmelCase = 0.00085    ,   _UpperCAmelCase = 0.012    ,   _UpperCAmelCase = "linear"    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = "epsilon"    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = 1.0    ,   _UpperCAmelCase = "linspace"    ,   _UpperCAmelCase = 0    ,   ):

       if trained_betas is not None:
           A       :       Any							=						torch.tensor(_UpperCAmelCase    ,   dtype=torch.floataa					)
       elif beta_schedule == "linear":
           A       :       Optional[int]							=						torch.linspace(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   dtype=torch.floataa					)
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           A       :       Any							=						(
               torch.linspace(beta_start**0.5    ,   beta_end**0.5    ,   _UpperCAmelCase    ,   dtype=torch.floataa					) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           A       :       Tuple							=						betas_for_alpha_bar(_UpperCAmelCase    ,   alpha_transform_type='''cosine'''					)
       elif beta_schedule == "exp":
           A       :       Optional[int]							=						betas_for_alpha_bar(_UpperCAmelCase    ,   alpha_transform_type='''exp'''					)
       else:
           raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

       #  set all values
       self.set_timesteps(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)
       A       :       Optional[Any]							=						use_karras_sigmas


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):

       if schedule_timesteps is None:
           A       :       Optional[int]							=						self.timesteps

       A       :       List[str]							=						(schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter					) == 0:
           A       :       List[Any]							=						1 if len(_UpperCAmelCase					) > 1 else 0
       else:
           A       :       List[str]							=						timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase					) else timestep
           A       :       Tuple							=						self._index_counter[timestep_int]

       return indices[pos].item()


   @property
   def 					snake_case				( self					):

       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       str							=						self.index_for_timestep(_UpperCAmelCase					)

       A       :       List[Any]							=						self.sigmas[step_index]
       A       :       str							=						sample / ((sigma**2 + 1) ** 0.5)
       return sample


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   ):
       A       :       Optional[Any]							=						num_inference_steps

       A       :       Union[str, Any]							=						num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
           A       :       List[str]							=						np.linspace(0    ,   num_train_timesteps - 1    ,   _UpperCAmelCase    ,   dtype=_UpperCAmelCase					)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
           A       :       int							=						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
           A       :       int							=						(np.arange(0    ,   _UpperCAmelCase					) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase					)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           A       :       List[Any]							=						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
           A       :       Dict							=						(np.arange(_UpperCAmelCase    ,   0    ,   -step_ratio					)).round().copy().astype(_UpperCAmelCase					)
           timesteps -= 1
       else:
           raise ValueError(
               f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

       A       :       Optional[Any]							=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       A       :       List[str]							=						np.log(_UpperCAmelCase					)
       A       :       Union[str, Any]							=						np.interp(_UpperCAmelCase    ,   np.arange(0    ,   len(_UpperCAmelCase					)					)    ,   _UpperCAmelCase					)

       if self.config.use_karras_sigmas:
           A       :       str							=						self._convert_to_karras(in_sigmas=_UpperCAmelCase    ,   num_inference_steps=self.num_inference_steps					)
           A       :       List[Any]							=						np.array([self._sigma_to_t(_UpperCAmelCase    ,   _UpperCAmelCase					) for sigma in sigmas]					)

       A       :       Dict							=						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
       A       :       Optional[Any]							=						torch.from_numpy(_UpperCAmelCase					).to(device=_UpperCAmelCase					)
       A       :       List[str]							=						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

       A       :       Optional[int]							=						torch.from_numpy(_UpperCAmelCase					)
       A       :       str							=						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

       if str(_UpperCAmelCase					).startswith('''mps'''					):
           # mps does not support float64
           A       :       Optional[Any]							=						timesteps.to(_UpperCAmelCase    ,   dtype=torch.floataa					)
       else:
           A       :       int							=						timesteps.to(device=_UpperCAmelCase					)

       # empty dt and derivative
       A       :       Any							=						None
       A       :       Any							=						None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       A       :       Union[str, Any]							=						defaultdict(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						np.log(_UpperCAmelCase					)

       # get distribution
       A       :       List[Any]							=						log_sigma - log_sigmas[:, np.newaxis]

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

       A       :       Tuple							=						log_sigmas[low_idx]
       A       :       Tuple							=						log_sigmas[high_idx]

       # interpolate sigmas
       A       :       Optional[Any]							=						(low - log_sigma) / (low - high)
       A       :       Any							=						np.clip(_UpperCAmelCase    ,   0    ,   1					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       int							=						in_sigmas[-1].item()
       A       :       Optional[int]							=						in_sigmas[0].item()

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


   @property
   def 					snake_case				( self					):
       return self.dt is None


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True    ,   ):
       A       :       List[str]							=						self.index_for_timestep(_UpperCAmelCase					)

       # advance index counter by 1
       A       :       Any							=						timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase					) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
           A       :       Union[str, Any]							=						self.sigmas[step_index]
           A       :       str							=						self.sigmas[step_index + 1]
       else:
           # 2nd order / Heun's method
           A       :       Tuple							=						self.sigmas[step_index - 1]
           A       :       Dict							=						self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       A       :       Optional[Any]							=						0
       A       :       Dict							=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           A       :       Optional[int]							=						sigma_hat if self.state_in_first_order else sigma_next
           A       :       Tuple							=						sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           A       :       Any							=						sigma_hat if self.state_in_first_order else sigma_next
           A       :       List[str]							=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
       elif self.config.prediction_type == "sample":
           A       :       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`'''					)

       if self.config.clip_sample:
           A       :       List[Any]							=						pred_original_sample.clamp(
               -self.config.clip_sample_range    ,   self.config.clip_sample_range					)

       if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           A       :       int							=						(sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           A       :       Optional[int]							=						sigma_next - sigma_hat

           # store for 2nd order step
           A       :       Tuple							=						derivative
           A       :       Any							=						dt
           A       :       Dict							=						sample
       else:
           # 2. 2nd order / Heun's method
           A       :       int							=						(sample - pred_original_sample) / sigma_next
           A       :       Tuple							=						(self.prev_derivative + derivative) / 2

           # 3. take prev timestep & sample
           A       :       str							=						self.dt
           A       :       List[Any]							=						self.sample

           # free dt and derivative
           # Note, this puts the scheduler in "first order mode"
           A       :       Tuple							=						None
           A       :       Tuple							=						None
           A       :       Optional[Any]							=						None

       A       :       Optional[Any]							=						sample + derivative * dt

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       Any							=						self.sigmas.to(device=original_samples.device    ,   dtype=original_samples.dtype					)
       if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase					):
           # mps does not support float64
           A       :       List[Any]							=						self.timesteps.to(original_samples.device    ,   dtype=torch.floataa					)
           A       :       Union[str, Any]							=						timesteps.to(original_samples.device    ,   dtype=torch.floataa					)
       else:
           A       :       List[Any]							=						self.timesteps.to(original_samples.device					)
           A       :       Any							=						timesteps.to(original_samples.device					)

       A       :       Optional[Any]							=						[self.index_for_timestep(_UpperCAmelCase    ,   _UpperCAmelCase					) for t in timesteps]

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

       A       :       Tuple							=						original_samples + noise * sigma
       return noisy_samples


   def __len__( self					):
       return self.config.num_train_timesteps







'''simple docstring'''






import requests

snake_case_     =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  None:
    # fetching a list of articles in json format
    A       :       Any							=						requests.get(_NEWS_API + bbc_news_api_key		).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page['''articles''']    ,       1		):
        print(F'''{i}.) {article['title']}'''		)


if __name__ == "__main__":
     fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
