


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 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Union[str, Any]   ,    lowercase__	:Any=0.9_9_9   ,    lowercase__	:int="cosine"   ,    )							->	Optional[Any]:
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase__	:Dict					):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowercase__	:Union[str, Any]					):
																		return math.exp(t * -1_2.0					)

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

						__magic_name__					:				List[str]				=   []
						for i in range(lowercase__					):
												__magic_name__					:				int				=   i / num_diffusion_timesteps
												__magic_name__					:				List[str]				=   (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowercase__					) / alpha_bar_fn(lowercase__					)   ,    lowercase__					)					)
						return torch.tensor(lowercase__   ,    dtype=torch.floataa					)



class      snake_case__	(   _A    ,							_A	):
				__lowerCAmelCase		=   [e.name for e in KarrasDiffusionSchedulers]
				__lowerCAmelCase		=   2
				@register_to_config
				def __init__(	self  ,   _UpperCAmelCase = 1000  ,   _UpperCAmelCase = 0.00085  ,   _UpperCAmelCase = 0.012  ,   _UpperCAmelCase = "linear"  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = "epsilon"  ,   _UpperCAmelCase = "linspace"  ,   _UpperCAmelCase = 0  ,   ):




										"""simple docstring"""




										if trained_betas is not None:
																__magic_name__					:				str				=   torch.tensor(_UpperCAmelCase  ,   dtype=torch.floataa   )
										elif beta_schedule == "linear":
																__magic_name__					:				Any				=   torch.linspace(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   dtype=torch.floataa   )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__magic_name__					:				List[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
																__magic_name__					:				List[Any]				=   betas_for_alpha_bar(_UpperCAmelCase   )
										else:
																raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

										__magic_name__					:				Any				=   1.0 - self.betas
										__magic_name__					:				int				=   torch.cumprod(self.alphas  ,   dim=0   )

										#  set all values
										self.set_timesteps(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=None   ):




										"""simple docstring"""




										if schedule_timesteps is None:
																__magic_name__					:				str				=   self.timesteps

										__magic_name__					:				List[Any]				=   (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:
																__magic_name__					:				str				=   1 if len(_UpperCAmelCase   ) > 1 else 0
										else:
																__magic_name__					:				Any				=   timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase   ) else timestep
																__magic_name__					:				Dict				=   self._index_counter[timestep_int]

										return indices[pos].item()
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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

										return (self.sigmas.max() ** 2 + 1) ** 0.5
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.index_for_timestep(_UpperCAmelCase   )

										if self.state_in_first_order:
																__magic_name__					:				List[str]				=   self.sigmas[step_index]
										else:
																__magic_name__					:				Union[str, Any]				=   self.sigmas_interpol[step_index - 1]

										__magic_name__					:				Optional[int]				=   sample / ((sigma**2 + 1) ** 0.5)
										return sample
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										__magic_name__					:				str				=   num_inference_steps

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

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

										__magic_name__					:				List[Any]				=   np.interp(_UpperCAmelCase  ,   np.arange(0  ,   len(_UpperCAmelCase   )   )  ,   _UpperCAmelCase   )
										__magic_name__					:				Union[str, Any]				=   np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
										__magic_name__					:				Union[str, Any]				=   torch.from_numpy(_UpperCAmelCase   ).to(device=_UpperCAmelCase   )

										# compute up and down sigmas
										__magic_name__					:				Tuple				=   sigmas.roll(-1   )
										__magic_name__					:				Dict				=   0.0
										__magic_name__					:				int				=   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
										__magic_name__					:				Any				=   (sigmas_next**2 - sigmas_up**2) ** 0.5
										__magic_name__					:				int				=   0.0

										# compute interpolated sigmas
										__magic_name__					:				str				=   sigmas.log().lerp(sigmas_down.log()  ,   0.5   ).exp()
										__magic_name__					:				Union[str, Any]				=   0.0

										# set sigmas
										__magic_name__					:				Optional[Any]				=   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
										__magic_name__					:				Dict				=   torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
										__magic_name__					:				Union[str, Any]				=   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
										__magic_name__					:				Dict				=   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

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

										__magic_name__					:				Any				=   self.sigma_to_t(_UpperCAmelCase   ).to(_UpperCAmelCase  ,   dtype=timesteps.dtype   )
										__magic_name__					:				Any				=   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,   dim=-1   ).flatten()

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

										__magic_name__					:				Dict				=   None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										__magic_name__					:				Tuple				=   defaultdict(_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   sigma.log()

										# get distribution
										__magic_name__					:				Optional[int]				=   log_sigma - self.log_sigmas[:, None]

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

										__magic_name__					:				Dict				=   self.log_sigmas[low_idx]
										__magic_name__					:				List[Any]				=   self.log_sigmas[high_idx]

										# interpolate sigmas
										__magic_name__					:				Optional[int]				=   (low - log_sigma) / (low - high)
										__magic_name__					:				List[str]				=   w.clamp(0  ,   1   )

										# transform interpolation to time range
										__magic_name__					:				Tuple				=   (1 - w) * low_idx + w * high_idx
										__magic_name__					:				Optional[int]				=   t.view(sigma.shape   )
										return t
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return self.sample is None
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = True  ,   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.index_for_timestep(_UpperCAmelCase   )

										# advance index counter by 1
										__magic_name__					:				Union[str, Any]				=   timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase   ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																__magic_name__					:				Optional[int]				=   self.sigmas[step_index]
																__magic_name__					:				List[str]				=   self.sigmas_interpol[step_index]
																__magic_name__					:				Optional[Any]				=   self.sigmas_up[step_index]
																__magic_name__					:				List[Any]				=   self.sigmas_down[step_index - 1]
										else:
																# 2nd order / KPDM2's method
																__magic_name__					:				str				=   self.sigmas[step_index - 1]
																__magic_name__					:				Optional[int]				=   self.sigmas_interpol[step_index - 1]
																__magic_name__					:				Tuple				=   self.sigmas_up[step_index - 1]
																__magic_name__					:				Tuple				=   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
										__magic_name__					:				Union[str, Any]				=   0
										__magic_name__					:				str				=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										__magic_name__					:				str				=   model_output.device
										__magic_name__					:				str				=   randn_tensor(model_output.shape  ,   dtype=model_output.dtype  ,   device=_UpperCAmelCase  ,   generator=_UpperCAmelCase   )

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																__magic_name__					:				Dict				=   sigma_hat if self.state_in_first_order else sigma_interpol
																__magic_name__					:				Optional[Any]				=   sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																__magic_name__					:				Union[str, Any]				=   sigma_hat if self.state_in_first_order else sigma_interpol
																__magic_name__					:				Tuple				=   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
																__magic_name__					:				Tuple				=   (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																__magic_name__					:				Any				=   sigma_interpol - sigma_hat

																# store for 2nd order step
																__magic_name__					:				int				=   sample
																__magic_name__					:				Optional[int]				=   dt
																__magic_name__					:				str				=   sample + derivative * dt
										else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																__magic_name__					:				List[str]				=   (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																__magic_name__					:				int				=   sigma_down - sigma_hat

																__magic_name__					:				Any				=   self.sample
																__magic_name__					:				List[str]				=   None

																__magic_name__					:				List[str]				=   sample + derivative * dt
																__magic_name__					:				Any				=   prev_sample + noise * sigma_up

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				int				=   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
																__magic_name__					:				List[Any]				=   self.timesteps.to(original_samples.device  ,   dtype=torch.floataa   )
																__magic_name__					:				int				=   timesteps.to(original_samples.device  ,   dtype=torch.floataa   )
										else:
																__magic_name__					:				Tuple				=   self.timesteps.to(original_samples.device   )
																__magic_name__					:				str				=   timesteps.to(original_samples.device   )

										__magic_name__					:				Tuple				=   [self.index_for_timestep(_UpperCAmelCase  ,   _UpperCAmelCase   ) for t in timesteps]

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

										__magic_name__					:				Tuple				=   original_samples + noise * sigma
										return noisy_samples





				def __len__(	self   ):




										"""simple docstring"""




										return self.config.num_train_timesteps






# 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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


