'''simple docstring'''




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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__=0.999							,    lowerCamelCase__="cosine"							,    )					->       Dict:





       """simple docstring"""


       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

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






class 		__A       (__magic_name__ ,					__magic_name__   ):
       snake_case       :Optional[int]				    =					[e.name for e in KarrasDiffusionSchedulers]
       snake_case       :Dict				    =					2






       @register_to_config
       def __init__(      self				,     UpperCamelCase_ = 10_00				,     UpperCamelCase_ = 0.0_0_0_8_5				,     UpperCamelCase_ = 0.0_1_2				,     UpperCamelCase_ = "linear"				,     UpperCamelCase_ = None				,     UpperCamelCase_ = "epsilon"				,     UpperCamelCase_ = "linspace"				,     UpperCamelCase_ = 0				,     ):
              if trained_betas is not None:
                     __UpperCAmelCase  :				List[str]       				=		torch.tensor(UpperCamelCase_				,     dtype=torch.floataa      )
              elif beta_schedule == "linear":
                     __UpperCAmelCase  :				int       				=		torch.linspace(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     dtype=torch.floataa      )
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     __UpperCAmelCase  :				Optional[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
                     __UpperCAmelCase  :				Tuple       				=		betas_for_alpha_bar(UpperCamelCase_      )
              else:
                     raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""      )

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=None      ):
              if schedule_timesteps is None:
                     __UpperCAmelCase  :				int       				=		self.timesteps

              __UpperCAmelCase  :				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:
                     __UpperCAmelCase  :				Union[str, Any]       				=		1 if len(UpperCamelCase_      ) > 1 else 0
              else:
                     __UpperCAmelCase  :				Union[str, Any]       				=		timestep.cpu().item() if torch.is_tensor(UpperCamelCase_      ) else timestep
                     __UpperCAmelCase  :				Dict       				=		self._index_counter[timestep_int]

              return indices[pos].item()






       @property
       def 			_snake_case						(      self      ):
              # 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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Tuple       				=		self.index_for_timestep(UpperCamelCase_      )

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

              __UpperCAmelCase  :				Any       				=		sample / ((sigma**2 + 1) ** 0.5)
              return sample






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     ):
              __UpperCAmelCase  :				Union[str, Any]       				=		num_inference_steps

              __UpperCAmelCase  :				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":
                     __UpperCAmelCase  :				int       				=		np.linspace(0				,     num_train_timesteps - 1				,     UpperCamelCase_				,     dtype=UpperCamelCase_      )[::-1].copy()
              elif self.config.timestep_spacing == "leading":
                     __UpperCAmelCase  :				Union[str, 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
                     __UpperCAmelCase  :				Tuple       				=		(np.arange(0				,     UpperCamelCase_      ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_      )
                     timesteps += self.config.steps_offset
              elif self.config.timestep_spacing == "trailing":
                     __UpperCAmelCase  :				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
                     __UpperCAmelCase  :				Union[str, Any]       				=		(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'."""      )

              __UpperCAmelCase  :				Union[str, Any]       				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
              __UpperCAmelCase  :				str       				=		torch.from_numpy(np.log(UpperCamelCase_      )      ).to(UpperCamelCase_      )

              __UpperCAmelCase  :				List[Any]       				=		np.interp(UpperCamelCase_				,     np.arange(0				,     len(UpperCamelCase_      )      )				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
              __UpperCAmelCase  :				int       				=		torch.from_numpy(UpperCamelCase_      ).to(device=UpperCamelCase_      )

              # interpolate sigmas
              __UpperCAmelCase  :				Tuple       				=		sigmas.log().lerp(sigmas.roll(1      ).log()				,     0.5      ).exp()

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

              if str(UpperCamelCase_      ).startswith("mps"      ):
                     # mps does not support float64
                     __UpperCAmelCase  :				Union[str, Any]       				=		torch.from_numpy(UpperCamelCase_      ).to(UpperCamelCase_				,     dtype=torch.floataa      )
              else:
                     __UpperCAmelCase  :				Tuple       				=		torch.from_numpy(UpperCamelCase_      ).to(UpperCamelCase_      )

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

              __UpperCAmelCase  :				List[str]       				=		torch.cat([timesteps[:1], interleaved_timesteps]      )

              __UpperCAmelCase  :				str       				=		None

              # for exp beta schedules, such as the one for `pipeline_shap_e.py`
              # we need an index counter
              __UpperCAmelCase  :				List[str]       				=		defaultdict(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              # get log sigma
              __UpperCAmelCase  :				Union[str, Any]       				=		sigma.log()

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

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

              __UpperCAmelCase  :				Union[str, Any]       				=		self.log_sigmas[low_idx]
              __UpperCAmelCase  :				List[Any]       				=		self.log_sigmas[high_idx]

              # interpolate sigmas
              __UpperCAmelCase  :				Tuple       				=		(low - log_sigma) / (low - high)
              __UpperCAmelCase  :				int       				=		w.clamp(0				,     1      )

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






       @property
       def 			_snake_case						(      self      ):
              return self.sample is None






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = True				,     ):
              __UpperCAmelCase  :				Optional[int]       				=		self.index_for_timestep(UpperCamelCase_      )

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

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

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

                     # 3. delta timestep
                     __UpperCAmelCase  :				Any       				=		sigma_next - sigma_hat

                     __UpperCAmelCase  :				Dict       				=		self.sample
                     __UpperCAmelCase  :				str       				=		None

              __UpperCAmelCase  :				List[Any]       				=		sample + derivative * dt

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              # Make sure sigmas and timesteps have the same device and dtype as original_samples
              __UpperCAmelCase  :				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
                     __UpperCAmelCase  :				int       				=		self.timesteps.to(original_samples.device				,     dtype=torch.floataa      )
                     __UpperCAmelCase  :				List[Any]       				=		timesteps.to(original_samples.device				,     dtype=torch.floataa      )
              else:
                     __UpperCAmelCase  :				Dict       				=		self.timesteps.to(original_samples.device      )
                     __UpperCAmelCase  :				Optional[Any]       				=		timesteps.to(original_samples.device      )

              __UpperCAmelCase  :				Tuple       				=		[self.index_for_timestep(UpperCamelCase_				,     UpperCamelCase_      ) for t in timesteps]

              __UpperCAmelCase  :				List[str]       				=		sigmas[step_indices].flatten()
              while len(sigma.shape      ) < len(original_samples.shape      ):
                     __UpperCAmelCase  :				Optional[int]       				=		sigma.unsqueeze(-1      )

              __UpperCAmelCase  :				Union[str, Any]       				=		original_samples + noise * sigma
              return noisy_samples







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







'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
   import torch






class 		__A       (TensorFormatter[Mapping, "torch.Tensor", Mapping]   ):






       def __init__(      self				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              super().__init__(features=UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch_tensor_kwargs
              import torch  # noqa import torch at initialization






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) and column:
                     if all(
                         isinstance(UpperCamelCase_				,     torch.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                         for x in column      ):
                            return torch.stack(UpperCamelCase_      )
              return column






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              if isinstance(UpperCamelCase_				,     (str, bytes, type(UpperCamelCase_      ))      ):
                     return value
              elif isinstance(UpperCamelCase_				,     (np.character, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.character      ):
                     return value.tolist()

              __UpperCAmelCase  :				int       				=		{}

              if isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.integer      ):
                     __UpperCAmelCase  :				Optional[int]       				=		{"dtype": torch.intaa}
              elif isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.floating      ):
                     __UpperCAmelCase  :				str       				=		{"dtype": torch.floataa}
              elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                     import PIL.Image

                     if isinstance(UpperCamelCase_				,     PIL.Image.Image      ):
                            __UpperCAmelCase  :				str       				=		np.asarray(UpperCamelCase_      )
              return torch.tensor(UpperCamelCase_				,     **{**default_dtype, **self.torch_tensor_kwargs}      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

              # support for torch, tf, jax etc.
              if hasattr(UpperCamelCase_				,     "__array__"      ) and not isinstance(UpperCamelCase_				,     torch.Tensor      ):
                     __UpperCAmelCase  :				Dict       				=		data_struct.__array__()
              # support for nested types like struct of list of struct
              if isinstance(UpperCamelCase_				,     np.ndarray      ):
                     if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                            return self._consolidate([self.recursive_tensorize(UpperCamelCase_      ) for substruct in data_struct]      )
              elif isinstance(UpperCamelCase_				,     (list, tuple)      ):
                     return self._consolidate([self.recursive_tensorize(UpperCamelCase_      ) for substruct in data_struct]      )
              return self._tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return map_nested(self._recursive_tensorize				,     UpperCamelCase_				,     map_list=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		self.numpy_arrow_extractor().extract_row(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.python_features_decoder.decode_row(UpperCamelCase_      )
              return self.recursive_tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.numpy_arrow_extractor().extract_column(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[Any]       				=		self.python_features_decoder.decode_column(UpperCamelCase_				,     pa_table.column_names[0]      )
              __UpperCAmelCase  :				List[Any]       				=		self.recursive_tensorize(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		self._consolidate(UpperCamelCase_      )
              return column







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		self.numpy_arrow_extractor().extract_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		self.python_features_decoder.decode_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		self.recursive_tensorize(UpperCamelCase_      )
              for column_name in batch:
                     __UpperCAmelCase  :				Tuple       				=		self._consolidate(batch[column_name]      )
              return batch






