






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					    =	logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    42






class        snake_case_				(							lowercase_   ,      lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    1

   @register_to_config
   def __init__(     self:						int ,							snake_case__:						int = 40 ,							snake_case__:						float = 0.0_02 ,							snake_case__:						float = 80.0 ,							snake_case__:						float = 0.5 ,							snake_case__:						float = 1.0 ,							snake_case__:						float = 7.0 ,							snake_case__:						bool = True ,							)	->		int:


       """simple docstring"""



       _lowercase     			=						sigma_max

       _lowercase     			=						np.linspace(0 ,							1 ,							snake_case__						)
       _lowercase     			=						self._convert_to_karras(snake_case__						)
       _lowercase     			=						self.sigma_to_t(snake_case__						)

       # setable values
       _lowercase     			=						None
       _lowercase     			=						torch.from_numpy(snake_case__						)
       _lowercase     			=						torch.from_numpy(snake_case__						)
       _lowercase     			=						False
       _lowercase     			=						False

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						Union[str, Any] ,							snake_case__:						Dict=None						)	->		List[str]:


       """simple docstring"""



       if schedule_timesteps is None:
           _lowercase     			=						self.timesteps

       _lowercase     			=						(schedule_timesteps == timestep).nonzero()
       return indices.item()

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						torch.FloatTensor ,							snake_case__:						Union[float, torch.FloatTensor]						)	->		torch.FloatTensor:


       """simple docstring"""



       if isinstance(snake_case__ ,							torch.Tensor						):
           _lowercase     			=						timestep.to(self.timesteps.device						)
       _lowercase     			=						self.index_for_timestep(snake_case__						)
       _lowercase     			=						self.sigmas[step_idx]

       _lowercase     			=						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       _lowercase     			=						True
       return sample

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						Union[float, np.ndarray]						)	->		Optional[Any]:


       """simple docstring"""



       if not isinstance(snake_case__ ,							np.ndarray						):
           _lowercase     			=						np.array(snake_case__ ,							dtype=np.floataa						)

       _lowercase     			=						1_000 * 0.25 * np.log(sigmas + 1E-44						)

       return timesteps

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						Optional[int] = None ,							snake_case__:						Union[str, torch.device] = None ,							snake_case__:						Optional[List[int]] = None ,							)	->		Dict:


       """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:
           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(snake_case__						)						):
               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}.'''						)

           _lowercase     			=						np.array(snake_case__ ,							dtype=np.intaa						)
           _lowercase     			=						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.'''						)

           _lowercase     			=						num_inference_steps

           _lowercase     			=						self.config.num_train_timesteps // self.num_inference_steps
           _lowercase     			=						(np.arange(0 ,							snake_case__						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
           _lowercase     			=						False

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

       _lowercase     			=						np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
       _lowercase     			=						torch.from_numpy(snake_case__						).to(device=snake_case__						)

       if str(snake_case__						).startswith('mps'						):
           # mps does not support float64
           _lowercase     			=						torch.from_numpy(snake_case__						).to(snake_case__ ,							dtype=torch.floataa						)
       else:
           _lowercase     			=						torch.from_numpy(snake_case__						).to(device=snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict ,							snake_case__:						Any						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.config.sigma_min
       _lowercase     			=						self.config.sigma_max

       _lowercase     			=						self.config.rho
       _lowercase     			=						sigma_min ** (1 / rho)
       _lowercase     			=						sigma_max ** (1 / rho)
       _lowercase     			=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						Optional[Any]						)	->		Any:


       """simple docstring"""



       _lowercase     			=						self.config.sigma_data

       _lowercase     			=						sigma_data**2 / (sigma**2 + sigma_data**2)
       _lowercase     			=						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						Tuple						)	->		int:


       """simple docstring"""



       _lowercase     			=						self.config.sigma_min
       _lowercase     			=						self.config.sigma_data

       _lowercase     			=						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       _lowercase     			=						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						torch.FloatTensor ,							snake_case__:						Union[float, torch.FloatTensor] ,							snake_case__:						torch.FloatTensor ,							snake_case__:						Optional[torch.Generator] = None ,							snake_case__:						bool = True ,							)	->		Union[CMStochasticIterativeSchedulerOutput, Tuple]:


       """simple docstring"""



       if (
           isinstance(snake_case__ ,							snake_case__						)
           or isinstance(snake_case__ ,							torch.IntTensor						)
           or isinstance(snake_case__ ,							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(snake_case__ ,							torch.Tensor						):
           _lowercase     			=						timestep.to(self.timesteps.device						)

       _lowercase     			=						self.config.sigma_min
       _lowercase     			=						self.config.sigma_max

       _lowercase     			=						self.index_for_timestep(snake_case__						)

       # sigma_next corresponds to next_t in original implementation
       _lowercase     			=						self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
           _lowercase     			=						self.sigmas[step_index + 1]
       else:
           # Set sigma_next to sigma_min
           _lowercase     			=						self.sigmas[-1]

       # Get scalings for boundary conditions
       _lowercase			,     _lowercase     			=						self.get_scalings_for_boundary_condition(snake_case__						)

       # 1. Denoise model output using boundary conditions
       _lowercase     			=						c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
           _lowercase     			=						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:
           _lowercase     			=						randn_tensor(
               model_output.shape ,							dtype=model_output.dtype ,							device=model_output.device ,							generator=snake_case__						)
       else:
           _lowercase     			=						torch.zeros_like(snake_case__						)
       _lowercase     			=						noise * self.config.s_noise

       _lowercase     			=						sigma_next.clamp(min=snake_case__ ,							max=snake_case__						)

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

       if not return_dict:
           return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						torch.FloatTensor ,							snake_case__:						torch.FloatTensor ,							snake_case__:						torch.FloatTensor ,							)	->		torch.FloatTensor:


       """simple docstring"""



       _lowercase     			=						self.sigmas.to(device=original_samples.device ,							dtype=original_samples.dtype						)
       if original_samples.device.type == "mps" and torch.is_floating_point(snake_case__						):
           # mps does not support float64
           _lowercase     			=						self.timesteps.to(original_samples.device ,							dtype=torch.floataa						)
           _lowercase     			=						timesteps.to(original_samples.device ,							dtype=torch.floataa						)
       else:
           _lowercase     			=						self.timesteps.to(original_samples.device						)
           _lowercase     			=						timesteps.to(original_samples.device						)

       _lowercase     			=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       _lowercase     			=						original_samples + noise * sigma
       return noisy_samples

   def __len__(     self:						Any						)	->		Optional[int]:


       """simple docstring"""



       return self.config.num_train_timesteps








import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    class        snake_case_				:







       """simple docstring"""



       def __init__(     self:						Tuple ,							snake_case__:						Optional[int]						)	->		None:


           """simple docstring"""



           assert isinstance(snake_case__ ,							(bytes, str)						)
           _lowercase     			=						content

       def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		str:


           """simple docstring"""



           return json.loads(self.content						)

    def mock_response(*_lowercase,   **_lowercase   ):
        assert args[0] == AUTHENTICATED_USER_ENDPOINT
        assert "Authorization" in kwargs["headers"]
        assert kwargs["headers"]["Authorization"].startswith('token '   )
        assert "Accept" in kwargs["headers"]
        return FakeResponse(b'{"login":"test","id":1}'   )

    monkeypatch.setattr(_lowercase,   'get',   _lowercase   )
    _lowercase     			=						fetch_github_info('token'   )
    assert result["login"] == "test"
    assert result["id"] == 1
