


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 							_UpperCAmelCase				(UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Union[str, Any]=0.9_99				,							UpperCamelCase__     :  List[Any]="cosine"				,							):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCamelCase__     :  Tuple						):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCamelCase__     :  List[Any]						):
                     return math.exp(t * -12.0						)

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

       _A      :       Optional[int]				    = []
       for i in range(UpperCamelCase__						):
              _A      :       int				    = i / num_diffusion_timesteps
              _A      :       str				    = (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      lowerCAmelCase__       (				a     ,    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= [e.name for e in KarrasDiffusionSchedulers]
     __SCREAMING_SNAKE_CASE  			= 2


     @register_to_config
     def __init__(						self ,						__lowerCamelCase = 1_0_0_0 ,						__lowerCamelCase = 0.0_0_0_8_5 ,						__lowerCamelCase = 0.0_1_2 ,						__lowerCamelCase = "linear" ,						__lowerCamelCase = None ,						__lowerCamelCase = "epsilon" ,						__lowerCamelCase = "linspace" ,						__lowerCamelCase = 0 ,						)			->				Optional[int]:
            if trained_betas is not None:
                   _A      :       List[str]				    = torch.tensor(__lowerCamelCase ,						dtype=torch.floataa)
            elif beta_schedule == "linear":
                   _A      :       int				    = torch.linspace(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						dtype=torch.floataa)
            elif beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   _A      :       int				    = (
                       torch.linspace(beta_start**0.5 ,						beta_end**0.5 ,						__lowerCamelCase ,						dtype=torch.floataa) ** 2
                   )
            elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   _A      :       Dict				    = betas_for_alpha_bar(__lowerCamelCase)
            else:
                   raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}")

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

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase=None)			->				List[Any]:
            if schedule_timesteps is None:
                   _A      :       List[str]				    = self.timesteps

            _A      :       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      :       Any				    = 1 if len(__lowerCamelCase) > 1 else 0
            else:
                   _A      :       List[Any]				    = timestep.cpu().item() if torch.is_tensor(__lowerCamelCase) else timestep
                   _A      :       List[Any]				    = self._index_counter[timestep_int]

            return indices[pos].item()


     @property
     def  _lowerCamelCase (						self)			->				Union[str, 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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				torch.FloatTensor:
            _A      :       Optional[Any]				    = self.index_for_timestep(__lowerCamelCase)

            if self.state_in_first_order:
                   _A      :       Tuple				    = self.sigmas[step_index]
            else:
                   _A      :       Union[str, Any]				    = self.sigmas_interpol[step_index]

            _A      :       Dict				    = sample / ((sigma**2 + 1) ** 0.5)
            return sample


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						)			->				Any:
            _A      :       List[Any]				    = num_inference_steps

            _A      :       Optional[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      :       str				    = np.linspace(0 ,						num_train_timesteps - 1 ,						__lowerCamelCase ,						dtype=__lowerCamelCase)[::-1].copy()
            elif self.config.timestep_spacing == "leading":
                   _A      :       Optional[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      :       List[Any]				    = (np.arange(0 ,						__lowerCamelCase) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase)
                   timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                   _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      :       str				    = (np.arange(__lowerCamelCase ,						0 ,						-step_ratio)).round().copy().astype(__lowerCamelCase)
                   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      :       str				    = torch.from_numpy(np.log(__lowerCamelCase)).to(__lowerCamelCase)

            _A      :       str				    = np.interp(__lowerCamelCase ,						np.arange(0 ,						len(__lowerCamelCase)) ,						__lowerCamelCase)
            _A      :       Optional[Any]				    = np.concatenate([sigmas, [0.0]]).astype(np.floataa)
            _A      :       Dict				    = torch.from_numpy(__lowerCamelCase).to(device=__lowerCamelCase)

            # interpolate sigmas
            _A      :       Union[str, Any]				    = sigmas.log().lerp(sigmas.roll(1).log() ,						0.5).exp()

            _A      :       Optional[Any]				    = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
            _A      :       Union[str, Any]				    = torch.cat(
                [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

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

            # interpolate timesteps
            _A      :       Dict				    = self.sigma_to_t(__lowerCamelCase).to(__lowerCamelCase ,						dtype=timesteps.dtype)
            _A      :       Union[str, Any]				    = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,						dim=-1).flatten()

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

            _A      :       List[str]				    = None

            # for exp beta schedules, such as the one for `pipeline_shap_e.py`
            # we need an index counter
            _A      :       str				    = defaultdict(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            # get log sigma
            _A      :       Any				    = sigma.log()

            # get distribution
            _A      :       List[str]				    = log_sigma - self.log_sigmas[:, None]

            # get sigmas range
            _A      :       Union[str, Any]				    = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
            _A      :       Optional[Any]				    = low_idx + 1

            _A      :       List[str]				    = self.log_sigmas[low_idx]
            _A      :       Any				    = self.log_sigmas[high_idx]

            # interpolate sigmas
            _A      :       Optional[Any]				    = (low - log_sigma) / (low - high)
            _A      :       Dict				    = w.clamp(0 ,						1)

            # transform interpolation to time range
            _A      :       List[Any]				    = (1 - w) * low_idx + w * high_idx
            _A      :       Any				    = t.view(sigma.shape)
            return t


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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = True ,						)			->				Union[SchedulerOutput, Tuple]:
            _A      :       Optional[int]				    = self.index_for_timestep(__lowerCamelCase)

            # advance index counter by 1
            _A      :       Optional[int]				    = timestep.cpu().item() if torch.is_tensor(__lowerCamelCase) else timestep
            self._index_counter[timestep_int] += 1

            if self.state_in_first_order:
                   _A      :       Any				    = self.sigmas[step_index]
                   _A      :       Any				    = self.sigmas_interpol[step_index + 1]
                   _A      :       str				    = self.sigmas[step_index + 1]
            else:
                   # 2nd order / KDPM2's method
                   _A      :       Any				    = self.sigmas[step_index - 1]
                   _A      :       List[str]				    = self.sigmas_interpol[step_index]
                   _A      :       int				    = 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      :       Dict				    = 0
            _A      :       Optional[int]				    = 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[Any]				    = sigma_hat if self.state_in_first_order else sigma_interpol
                   _A      :       Union[str, Any]				    = sample - sigma_input * model_output
            elif self.config.prediction_type == "v_prediction":
                   _A      :       List[Any]				    = sigma_hat if self.state_in_first_order else sigma_interpol
                   _A      :       List[str]				    = 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
                   _A      :       Tuple				    = (sample - pred_original_sample) / sigma_hat
                   # 3. delta timestep
                   _A      :       Union[str, Any]				    = sigma_interpol - sigma_hat

                   # store for 2nd order step
                   _A      :       Union[str, Any]				    = sample
            else:
                   # DPM-Solver-2
                   # 2. Convert to an ODE derivative for 2nd order
                   _A      :       int				    = (sample - pred_original_sample) / sigma_interpol

                   # 3. delta timestep
                   _A      :       Union[str, Any]				    = sigma_next - sigma_hat

                   _A      :       Optional[Any]				    = self.sample
                   _A      :       Any				    = None

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

            if not return_dict:
                   return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCamelCase)


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

            _A      :       Dict				    = [self.index_for_timestep(__lowerCamelCase ,						__lowerCamelCase) for t in timesteps]

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

            _A      :       int				    = original_samples + noise * sigma
            return noisy_samples


     def __len__(						self)			->				List[Any]:
            return self.config.num_train_timesteps




# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"						)
       _A      :       Dict				    = formatter_cls
       for alias in set(aliases + [format_type]						):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
    from .torch_formatter import TorchFormatter

    _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
    lowerCAmelCase__      		= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
    _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
    from .tf_formatter import TFFormatter

    _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
    lowerCAmelCase__      		= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
    _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
    from .jax_formatter import JaxFormatter

    _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = get_format_type_from_alias(UpperCamelCase__						)
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**UpperCamelCase__						)
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"						)
