




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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def   A		(__A  :       int  ,							__A  :       Dict=0.999  ,							__A  :       str="cosine"  ,							)  -> Tuple:







     """simple docstring"""


     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

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







class __snake_case     (     a					,    a						):
   UpperCAmelCase__    :       List[Any]		=    [e.name for e in KarrasDiffusionSchedulers]
   UpperCAmelCase__    :       Tuple		=    2







   @register_to_config
   def __init__( self						:		List[Any]   ,		_snake_case						:		int = 1000   ,		_snake_case						:		float = 0.0_0_0_8_5   ,		_snake_case						:		float = 0.0_1_2   ,		_snake_case						:		str = "linear"   ,		_snake_case						:		Optional[Union[np.ndarray, List[float]]] = None   ,		_snake_case						:		str = "epsilon"   ,		_snake_case						:		str = "linspace"   ,		_snake_case						:		int = 0   ,		):

        """simple docstring"""


        if trained_betas is not None:
             UpperCAmelCase_		=				torch.tensor(_snake_case   ,		dtype=torch.floataa)
        elif beta_schedule == "linear":
             UpperCAmelCase_		=				torch.linspace(_snake_case   ,		_snake_case   ,		_snake_case   ,		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   ,		_snake_case   ,		dtype=torch.floataa) ** 2
             )
        elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCAmelCase_		=				betas_for_alpha_bar(_snake_case)
        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(_snake_case   ,		_snake_case   ,		_snake_case)







   def lowerCamelCase		( self						:		Dict   ,		_snake_case						:		Any   ,		_snake_case						:		Any=None):

        """simple docstring"""


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

        return indices[pos].item()







   @property
   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


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

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







   def lowerCamelCase		( self						:		Dict   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		Union[float, torch.FloatTensor]   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				self.index_for_timestep(_snake_case)

        if self.state_in_first_order:
             UpperCAmelCase_		=				self.sigmas[step_index]
        else:
             UpperCAmelCase_		=				self.sigmas_interpol[step_index - 1]

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







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		int   ,		_snake_case						:		Union[str, torch.device] = None   ,		_snake_case						:		Optional[int] = None   ,		):

        """simple docstring"""


        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   ,		_snake_case   ,		dtype=_snake_case)[::-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   ,		_snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
             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(_snake_case   ,		0   ,		-step_ratio)).round().copy().astype(_snake_case)
             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(_snake_case)).to(_snake_case)

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

        # compute up and down sigmas
        UpperCAmelCase_		=				sigmas.roll(-1)
        UpperCAmelCase_		=				0.0
        UpperCAmelCase_		=				(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
        UpperCAmelCase_		=				(sigmas_next**2 - sigmas_up**2) ** 0.5
        UpperCAmelCase_		=				0.0

        # compute interpolated sigmas
        UpperCAmelCase_		=				sigmas.log().lerp(sigmas_down.log()   ,		0.5).exp()
        UpperCAmelCase_		=				0.0

        # set sigmas
        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:]])
        UpperCAmelCase_		=				torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
        UpperCAmelCase_		=				torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

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

        UpperCAmelCase_		=				self.sigma_to_t(_snake_case).to(_snake_case   ,		dtype=timesteps.dtype)
        UpperCAmelCase_		=				torch.stack((timesteps_interpol[:-2, 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(_snake_case)







   def lowerCamelCase		( self						:		List[str]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        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 lowerCamelCase		( self						:		str):

        """simple docstring"""


        return self.sample is None







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		Union[torch.FloatTensor, np.ndarray]   ,		_snake_case						:		Union[float, torch.FloatTensor]   ,		_snake_case						:		Union[torch.FloatTensor, np.ndarray]   ,		_snake_case						:		Optional[torch.Generator] = None   ,		_snake_case						:		bool = True   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				self.index_for_timestep(_snake_case)

        # advance index counter by 1
        UpperCAmelCase_		=				timestep.cpu().item() if torch.is_tensor(_snake_case) 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]
             UpperCAmelCase_		=				self.sigmas_up[step_index]
             UpperCAmelCase_		=				self.sigmas_down[step_index - 1]
        else:
             # 2nd order / KPDM2's method
             UpperCAmelCase_		=				self.sigmas[step_index - 1]
             UpperCAmelCase_		=				self.sigmas_interpol[step_index - 1]
             UpperCAmelCase_		=				self.sigmas_up[step_index - 1]
             UpperCAmelCase_		=				self.sigmas_down[step_index - 1]

        # 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

        UpperCAmelCase_		=				model_output.device
        UpperCAmelCase_		=				randn_tensor(model_output.shape   ,		dtype=model_output.dtype   ,		device=_snake_case   ,		generator=_snake_case)

        # 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
             UpperCAmelCase_		=				dt
             UpperCAmelCase_		=				sample + derivative * dt
        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_down - sigma_hat

             UpperCAmelCase_		=				self.sample
             UpperCAmelCase_		=				None

             UpperCAmelCase_		=				sample + derivative * dt
             UpperCAmelCase_		=				prev_sample + noise * sigma_up

        if not return_dict:
             return (prev_sample,)

        return SchedulerOutput(prev_sample=_snake_case)







   def lowerCamelCase		( self						:		str   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		torch.FloatTensor   ,		_snake_case						:		torch.FloatTensor   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				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
             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(_snake_case   ,		_snake_case) 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						:		Union[str, Any]):

        """simple docstring"""


        return self.config.num_train_timesteps












from .stack import Stack


def   A		(__A  :       str			)  -> bool:







     """simple docstring"""


     UpperCAmelCase_		=				Stack()
     UpperCAmelCase_		=				{'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__A			)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    snake_case_					:					Dict       						=			["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        snake_case_					:					Optional[int]       						=			"" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")






