




"""simple docstring"""




a_        =       '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

a_        =       [{'type': 'code', 'content': INSTALL_CONTENT}]
a_        =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}






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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=0.999			,						SCREAMING_SNAKE_CASE_="cosine"			,						)->						int:



    """simple docstring"""






    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

    else:
        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

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






class 						__magic_name__					(							snake_case			,      snake_case				):
     UpperCamelCase_	:str										=      [e.name for e in KarrasDiffusionSchedulers]
     UpperCamelCase_	:Tuple										=      2






     @register_to_config
     def __init__(				self		,						_lowercase = 1_000		,						_lowercase = 0.00_085		,						_lowercase = 0.012		,						_lowercase = "linear"		,						_lowercase = None		,						_lowercase = "epsilon"		,						_lowercase = "linspace"		,						_lowercase = 0		,						)->     List[Any]:
         if trained_betas is not None:
             UpperCamelCase_			  =						torch.tensor(_lowercase		,						dtype=torch.floataa	)
         elif beta_schedule == "linear":
             UpperCamelCase_			  =						torch.linspace(_lowercase		,						_lowercase		,						_lowercase		,						dtype=torch.floataa	)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase_			  =						(
                 torch.linspace(beta_start**0.5		,						beta_end**0.5		,						_lowercase		,						dtype=torch.floataa	) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase_			  =						betas_for_alpha_bar(_lowercase	)
         else:
             raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

         UpperCamelCase_			  =						1.0 - self.betas
         UpperCamelCase_			  =						torch.cumprod(self.alphas		,						dim=0	)

         #  set all values
         self.set_timesteps(_lowercase		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase=None	)->     Union[str, Any]:
         if schedule_timesteps is None:
             UpperCamelCase_			  =						self.timesteps

         UpperCamelCase_			  =						(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:
             UpperCamelCase_			  =						1 if len(_lowercase	) > 1 else 0
         else:
             UpperCamelCase_			  =						timestep.cpu().item() if torch.is_tensor(_lowercase	) else timestep
             UpperCamelCase_			  =						self._index_counter[timestep_int]

         return indices[pos].item()






     @property
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         # 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 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						)->     torch.FloatTensor:
         UpperCamelCase_			  =						self.index_for_timestep(_lowercase	)

         if self.state_in_first_order:
             UpperCamelCase_			  =						self.sigmas[step_index]
         else:
             UpperCamelCase_			  =						self.sigmas_interpol[step_index]

         UpperCamelCase_			  =						sample / ((sigma**2 + 1) ** 0.5)
         return sample






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = None		,						)->     Tuple:
         UpperCamelCase_			  =						num_inference_steps

         UpperCamelCase_			  =						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":
             UpperCamelCase_			  =						np.linspace(0		,						num_train_timesteps - 1		,						_lowercase		,						dtype=_lowercase	)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             UpperCamelCase_			  =						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
             UpperCamelCase_			  =						(np.arange(0		,						_lowercase	) * step_ratio).round()[::-1].copy().astype(_lowercase	)
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             UpperCamelCase_			  =						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
             UpperCamelCase_			  =						(np.arange(_lowercase		,						0		,						-step_ratio	)).round().copy().astype(_lowercase	)
             timesteps -= 1
         else:
             raise ValueError(
                 F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

         UpperCamelCase_			  =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         UpperCamelCase_			  =						torch.from_numpy(np.log(_lowercase	)	).to(_lowercase	)

         UpperCamelCase_			  =						np.interp(_lowercase		,						np.arange(0		,						len(_lowercase	)	)		,						_lowercase	)
         UpperCamelCase_			  =						np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
         UpperCamelCase_			  =						torch.from_numpy(_lowercase	).to(device=_lowercase	)

         # interpolate sigmas
         UpperCamelCase_			  =						sigmas.log().lerp(sigmas.roll(1	).log()		,						0.5	).exp()

         UpperCamelCase_			  =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
         UpperCamelCase_			  =						torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)

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

         # interpolate timesteps
         UpperCamelCase_			  =						self.sigma_to_t(_lowercase	).to(_lowercase		,						dtype=timesteps.dtype	)
         UpperCamelCase_			  =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,						dim=-1	).flatten()

         UpperCamelCase_			  =						torch.cat([timesteps[:1], interleaved_timesteps]	)

         UpperCamelCase_			  =						None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         UpperCamelCase_			  =						defaultdict(_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Any:
         # get log sigma
         UpperCamelCase_			  =						sigma.log()

         # get distribution
         UpperCamelCase_			  =						log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         UpperCamelCase_			  =						dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
         UpperCamelCase_			  =						low_idx + 1

         UpperCamelCase_			  =						self.log_sigmas[low_idx]
         UpperCamelCase_			  =						self.log_sigmas[high_idx]

         # interpolate sigmas
         UpperCamelCase_			  =						(low - log_sigma) / (low - high)
         UpperCamelCase_			  =						w.clamp(0		,						1	)

         # transform interpolation to time range
         UpperCamelCase_			  =						(1 - w) * low_idx + w * high_idx
         UpperCamelCase_			  =						t.view(sigma.shape	)
         return t






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         return self.sample is None






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase = True		,						)->     Union[SchedulerOutput, Tuple]:
         UpperCamelCase_			  =						self.index_for_timestep(_lowercase	)

         # advance index counter by 1
         UpperCamelCase_			  =						timestep.cpu().item() if torch.is_tensor(_lowercase	) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             UpperCamelCase_			  =						self.sigmas[step_index]
             UpperCamelCase_			  =						self.sigmas_interpol[step_index + 1]
             UpperCamelCase_			  =						self.sigmas[step_index + 1]
         else:
             # 2nd order / KDPM2's method
             UpperCamelCase_			  =						self.sigmas[step_index - 1]
             UpperCamelCase_			  =						self.sigmas_interpol[step_index]
             UpperCamelCase_			  =						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
         UpperCamelCase_			  =						0
         UpperCamelCase_			  =						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":
             UpperCamelCase_			  =						sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase_			  =						sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             UpperCamelCase_			  =						sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase_			  =						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             raise NotImplementedError("prediction_type not implemented yet: sample"	)
         else:
             raise ValueError(
                 F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             UpperCamelCase_			  =						(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase_			  =						sigma_interpol - sigma_hat

             # store for 2nd order step
             UpperCamelCase_			  =						sample
         else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase_			  =						(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase_			  =						sigma_next - sigma_hat

             UpperCamelCase_			  =						self.sample
             UpperCamelCase_			  =						None

         UpperCamelCase_			  =						sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase		,						)->     torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase_			  =						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
             UpperCamelCase_			  =						self.timesteps.to(original_samples.device		,						dtype=torch.floataa	)
             UpperCamelCase_			  =						timesteps.to(original_samples.device		,						dtype=torch.floataa	)
         else:
             UpperCamelCase_			  =						self.timesteps.to(original_samples.device	)
             UpperCamelCase_			  =						timesteps.to(original_samples.device	)

         UpperCamelCase_			  =						[self.index_for_timestep(_lowercase		,						_lowercase	) for t in timesteps]

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

         UpperCamelCase_			  =						original_samples + noise * sigma
         return noisy_samples






     def __len__(				self	)->     Dict:
         return self.config.num_train_timesteps
