



"""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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=0.9_99				,      lowerCamelCase__="cosine"				,      ) ->							int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__			):
																					return math.cos((t + 0.0_08) / 1.0_08 * 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}"""			)

							A         =					[]
							for i in range(lowerCamelCase__			):
														A         =					i / num_diffusion_timesteps
														A         =					(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      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase     ):
		lowerCAmelCase_       :	str 							=     [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_       :	Optional[int] 							=     2



		@register_to_config
		def __init__( self  :    Any      ,							snake_case  :    int = 1_000      ,							snake_case  :    float = 0.00085      ,							snake_case  :    float = 0.012      ,							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      ,							)			->						List[Any]:






									'''simple docstring'''

									if trained_betas is not None:
																A         =					torch.tensor(snake_case      ,							dtype=torch.floataa				)
									elif beta_schedule == "linear":
																A         =					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.
																A         =					(
																    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
																A         =					betas_for_alpha_bar(snake_case				)
									else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

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

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



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Union[str, Any]=None				)			->						Union[str, Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A         =					self.timesteps

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

									return indices[pos].item()



		@property
		def 					A_		( self  :    int				)			->						str:






									'''simple docstring'''

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

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



		def 					A_		( self  :    Any      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    Union[float, torch.FloatTensor]      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					self.index_for_timestep(snake_case				)

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

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



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    Union[str, torch.device] = None      ,							snake_case  :    Optional[int] = None      ,							)			->						List[str]:






									'''simple docstring'''

									A         =					num_inference_steps

									A         =					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         =					np.linspace(0      ,							num_train_timesteps - 1      ,							snake_case      ,							dtype=snake_case				)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																A         =					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         =					(np.arange(0      ,							snake_case				) * step_ratio).round()[::-1].copy().astype(snake_case				)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A         =					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         =					(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'."""				)

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

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

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

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

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

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

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

									A         =					None

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



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					sigma.log()

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

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

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

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

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



		@property
		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									return self.sample is None



		def 					A_		( self  :    Any      ,							snake_case  :    Union[torch.FloatTensor, np.ndarray]      ,							snake_case  :    Union[float, torch.FloatTensor]      ,							snake_case  :    Union[torch.FloatTensor, np.ndarray]      ,							snake_case  :    bool = True      ,							)			->						Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A         =					self.index_for_timestep(snake_case				)

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

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

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

																# 3. delta timestep
																A         =					sigma_next - sigma_hat

																A         =					self.sample
																A         =					None

									A         =					sample + derivative * dt

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case				)



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					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
																A         =					self.timesteps.to(original_samples.device      ,							dtype=torch.floataa				)
																A         =					timesteps.to(original_samples.device      ,							dtype=torch.floataa				)
									else:
																A         =					self.timesteps.to(original_samples.device				)
																A         =					timesteps.to(original_samples.device				)

									A         =					[self.index_for_timestep(snake_case      ,							snake_case				) for t in timesteps]

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

									A         =					original_samples + noise * sigma
									return noisy_samples



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






									'''simple docstring'''

									return self.config.num_train_timesteps








"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



