





"""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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[Any]    ,       _lowercase						:    Any=0.999    ,       _lowercase						:    Optional[int]="cosine"    ,       ) ->Dict:






	'''simple docstring'''






	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	a      :		Any	=				[]
	for i in range(_lowercase					):
		a      :		Union[str, Any]	=				i / num_diffusion_timesteps
		a      :		List[Any]	=				(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        __UpperCamelCase				(						a__		,       a__						):
							lowerCamelCase   :					List[Any]      					=[e.name for e in KarrasDiffusionSchedulers]
							lowerCamelCase   :					int      					=2






							@register_to_config
							def __init__(   self      ,					lowerCAmelCase__ = 1000      ,					lowerCAmelCase__ = 0.00_085      ,					lowerCAmelCase__ = 0.012      ,					lowerCAmelCase__ = "linear"      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = "epsilon"      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = 1.0      ,					lowerCAmelCase__ = "linspace"      ,					lowerCAmelCase__ = 0      ,					)	-> Optional[int]:
								if trained_betas is not None:
									a      :		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      :		Union[str, Any]	=				(
									    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      :		Optional[Any]	=				betas_for_alpha_bar(lowerCAmelCase__      ,					alpha_transform_type="cosine"      )
								elif beta_schedule == "exp":
									a      :		Union[str, Any]	=				betas_for_alpha_bar(lowerCAmelCase__      ,					alpha_transform_type="exp"      )
								else:
									raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""      )

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

								#  set all values
								self.set_timesteps(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				use_karras_sigmas






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__=None      )	-> List[str]:
								if schedule_timesteps is None:
									a      :		int	=				self.timesteps

								a      :		int	=				(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      :		Optional[Any]	=				1 if len(lowerCAmelCase__      ) > 1 else 0
								else:
									a      :		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     __a   (   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     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> torch.FloatTensor:
								a      :		str	=				self.index_for_timestep(lowerCAmelCase__      )

								a      :		Union[str, Any]	=				self.sigmas[step_index]
								a      :		Tuple	=				sample / ((sigma**2 + 1) ** 0.5)
								return sample






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					)	-> Union[str, Any]:
								a      :		Optional[int]	=				num_inference_steps

								a      :		List[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      :		List[Any]	=				np.linspace(0      ,					num_train_timesteps - 1      ,					lowerCAmelCase__      ,					dtype=lowerCAmelCase__      )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
									a      :		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
									a      :		Any	=				(np.arange(0      ,					lowerCAmelCase__      ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__      )
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									a      :		List[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
									a      :		List[Any]	=				(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      :		List[str]	=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
								a      :		Union[str, Any]	=				np.log(lowerCAmelCase__      )
								a      :		Optional[Any]	=				np.interp(lowerCAmelCase__      ,					np.arange(0      ,					len(lowerCAmelCase__      )      )      ,					lowerCAmelCase__      )

								if self.config.use_karras_sigmas:
									a      :		Any	=				self._convert_to_karras(in_sigmas=lowerCAmelCase__      ,					num_inference_steps=self.num_inference_steps      )
									a      :		Optional[Any]	=				np.array([self._sigma_to_t(lowerCAmelCase__      ,					lowerCAmelCase__      ) for sigma in sigmas]      )

								a      :		Dict	=				np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
								a      :		Dict	=				torch.from_numpy(lowerCAmelCase__      ).to(device=lowerCAmelCase__      )
								a      :		Tuple	=				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

								a      :		Union[str, Any]	=				torch.from_numpy(lowerCAmelCase__      )
								a      :		Dict	=				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

								if str(lowerCAmelCase__      ).startswith("mps"      ):
									# mps does not support float64
									a      :		Dict	=				timesteps.to(lowerCAmelCase__      ,					dtype=torch.floataa      )
								else:
									a      :		List[str]	=				timesteps.to(device=lowerCAmelCase__      )

								# empty dt and derivative
								a      :		Dict	=				None
								a      :		Union[str, Any]	=				None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								a      :		Optional[Any]	=				defaultdict(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> List[Any]:
								# get log sigma
								a      :		List[Any]	=				np.log(lowerCAmelCase__      )

								# get distribution
								a      :		Dict	=				log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								a      :		str	=				np.cumsum((dists >= 0)      ,					axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
								a      :		Optional[int]	=				low_idx + 1

								a      :		Union[str, Any]	=				log_sigmas[low_idx]
								a      :		Any	=				log_sigmas[high_idx]

								# interpolate sigmas
								a      :		List[Any]	=				(low - log_sigma) / (low - high)
								a      :		Any	=				np.clip(lowerCAmelCase__      ,					0      ,					1      )

								# transform interpolation to time range
								a      :		int	=				(1 - w) * low_idx + w * high_idx
								a      :		str	=				t.reshape(sigma.shape      )
								return t






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> torch.FloatTensor:
								a      :		float	=				in_sigmas[-1].item()
								a      :		float	=				in_sigmas[0].item()

								a      :		int	=				7.0  # 7.0 is the value used in the paper
								a      :		List[str]	=				np.linspace(0      ,					1      ,					lowerCAmelCase__      )
								a      :		Optional[Any]	=				sigma_min ** (1 / rho)
								a      :		Optional[int]	=				sigma_max ** (1 / rho)
								a      :		int	=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas






							@property
							def     __a   (   self      )	-> int:
								return self.dt is None






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = True      ,					)	-> Union[SchedulerOutput, Tuple]:
								a      :		List[str]	=				self.index_for_timestep(lowerCAmelCase__      )

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

								if self.state_in_first_order:
									a      :		List[str]	=				self.sigmas[step_index]
									a      :		List[Any]	=				self.sigmas[step_index + 1]
								else:
									# 2nd order / Heun's method
									a      :		Tuple	=				self.sigmas[step_index - 1]
									a      :		List[str]	=				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      :		int	=				0
								a      :		List[Any]	=				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      :		Tuple	=				sigma_hat if self.state_in_first_order else sigma_next
									a      :		Union[str, Any]	=				sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
									a      :		Optional[Any]	=				sigma_hat if self.state_in_first_order else sigma_next
									a      :		Union[str, Any]	=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**2 + 1)
									)
								elif self.config.prediction_type == "sample":
									a      :		Dict	=				model_output
								else:
									raise ValueError(
									    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""      )

								if self.config.clip_sample:
									a      :		Tuple	=				pred_original_sample.clamp(
									    -self.config.clip_sample_range      ,					self.config.clip_sample_range      )

								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      :		Any	=				sigma_next - sigma_hat

									# store for 2nd order step
									a      :		Optional[int]	=				derivative
									a      :		Any	=				dt
									a      :		Union[str, Any]	=				sample
								else:
									# 2. 2nd order / Heun's method
									a      :		Dict	=				(sample - pred_original_sample) / sigma_next
									a      :		str	=				(self.prev_derivative + derivative) / 2

									# 3. take prev timestep & sample
									a      :		List[str]	=				self.dt
									a      :		Optional[Any]	=				self.sample

									# free dt and derivative
									# Note, this puts the scheduler in "first order mode"
									a      :		Union[str, Any]	=				None
									a      :		Union[str, Any]	=				None
									a      :		List[str]	=				None

								a      :		Dict	=				sample + derivative * dt

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								a      :		str	=				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      :		List[str]	=				self.timesteps.to(original_samples.device      ,					dtype=torch.floataa      )
									a      :		int	=				timesteps.to(original_samples.device      ,					dtype=torch.floataa      )
								else:
									a      :		List[str]	=				self.timesteps.to(original_samples.device      )
									a      :		Dict	=				timesteps.to(original_samples.device      )

								a      :		int	=				[self.index_for_timestep(lowerCAmelCase__      ,					lowerCAmelCase__      ) for t in timesteps]

								a      :		Union[str, Any]	=				sigmas[step_indices].flatten()
								while len(sigma.shape      ) < len(original_samples.shape      ):
									a      :		Union[str, Any]	=				sigma.unsqueeze(-1      )

								a      :		List[str]	=				original_samples + noise * sigma
								return noisy_samples






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









"""simple docstring"""




# Copyright 2021 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 argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	a      :		Any	=				ArgumentParser("Accelerate CLI tool"    ,       usage="accelerate <command> [<args>]"    ,       allow_abbrev=_lowercase					)
	a      :		List[Any]	=				parser.add_subparsers(help="accelerate command helpers"					)

	# Register commands
	get_config_parser(subparsers=_lowercase					)
	env_command_parser(subparsers=_lowercase					)
	launch_command_parser(subparsers=_lowercase					)
	tpu_command_parser(subparsers=_lowercase					)
	test_command_parser(subparsers=_lowercase					)

	# Let's go
	a      :		List[Any]	=				parser.parse_args()

	if not hasattr(_lowercase    ,       "func"					):
		parser.print_help()
		exit(1					)

	# Run
	args.func(_lowercase					)


if __name__ == "__main__":
		main()


