


"""simple docstring"""



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:torch.FloatTensor
			_UpperCAmelCase		:Optional[torch.FloatTensor]             =			None


def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Tuple=0.999    ,						lowerCamelCase__     :   Tuple="cosine"    ,						)    ->     Any:
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

							lowerCamelCase_	:							Dict									=[]
							for i in range(lowerCamelCase__  ):
														lowerCamelCase_	:							str									=i / num_diffusion_timesteps
														lowerCamelCase_	:							Tuple									=(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        lowercase__      (							snake_case__,       snake_case__       ):


			@register_to_config
			def __init__(				self					:  List[str]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  str = "fixed_small_log"       ,	snake_case__					:  bool = True       ,	snake_case__					:  Optional[float] = 1.0       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  str = "squaredcos_cap_v2"       ,	):
										if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )

										lowerCamelCase_	:							str									=betas_for_alpha_bar(snake_case__ )

										lowerCamelCase_	:							str									=1.0 - self.betas
										lowerCamelCase_	:							str									=torch.cumprod(self.alphas       ,	dim=0 )
										lowerCamelCase_	:							Union[str, Any]									=torch.tensor(1.0 )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=1.0

										# setable values
										lowerCamelCase_	:							int									=None
										lowerCamelCase_	:							str									=torch.from_numpy(np.arange(0       ,	snake_case__ )[::-1].copy() )

										lowerCamelCase_	:							List[str]									=variance_type


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, torch.device] = None ):
										lowerCamelCase_	:							List[str]									=num_inference_steps
										lowerCamelCase_	:							Union[str, Any]									=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										lowerCamelCase_	:							List[Any]									=(np.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
										lowerCamelCase_	:							Dict									=torch.from_numpy(snake_case__ ).to(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None       ,	snake_case__					:  Union[str, Any]=None       ,	snake_case__					:  str=None ):
										if prev_timestep is None:
																	lowerCamelCase_	:							Dict									=t - 1

										lowerCamelCase_	:							Tuple									=self.alphas_cumprod[t]
										lowerCamelCase_	:							str									=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										lowerCamelCase_	:							Optional[int]									=1 - alpha_prod_t
										lowerCamelCase_	:							List[str]									=1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
																	lowerCamelCase_	:							str									=self.betas[t]
										else:
																	lowerCamelCase_	:							Any									=1 - alpha_prod_t / alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										lowerCamelCase_	:							int									=beta_prod_t_prev / beta_prod_t * beta

										if variance_type is None:
																	lowerCamelCase_	:							str									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small_log":
																	lowerCamelCase_	:							List[Any]									=torch.log(torch.clamp(snake_case__       ,	min=1E-20 ) )
																	lowerCamelCase_	:							Dict									=torch.exp(0.5 * variance )
										elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	lowerCamelCase_	:							List[Any]									=variance.log()
																	lowerCamelCase_	:							Tuple									=beta.log()

																	lowerCamelCase_	:							List[str]									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							List[Any]									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  int       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  Optional[int] = None       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Dict									=timestep

										if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																	lowerCamelCase_	:							int									=torch.split(snake_case__       ,	sample.shape[1]       ,	dim=1 )
										else:
																	lowerCamelCase_	:							Dict									=None

										# 1. compute alphas, betas
										if prev_timestep is None:
																	lowerCamelCase_	:							List[str]									=t - 1

										lowerCamelCase_	:							int									=self.alphas_cumprod[t]
										lowerCamelCase_	:							Optional[int]									=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										lowerCamelCase_	:							str									=1 - alpha_prod_t
										lowerCamelCase_	:							str									=1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
																	lowerCamelCase_	:							str									=self.betas[t]
																	lowerCamelCase_	:							Any									=self.alphas[t]
										else:
																	lowerCamelCase_	:							List[Any]									=1 - alpha_prod_t / alpha_prod_t_prev
																	lowerCamelCase_	:							Optional[int]									=1 - beta

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																	lowerCamelCase_	:							List[str]									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							Any									=model_output
										else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																	    " for the UnCLIPScheduler." )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	lowerCamelCase_	:							Tuple									=torch.clamp(
																	    snake_case__       ,	-self.config.clip_sample_range       ,	self.config.clip_sample_range )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							str									=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
										lowerCamelCase_	:							int									=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Optional[int]									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										lowerCamelCase_	:							Optional[Any]									=0
										if t > 0:
																	lowerCamelCase_	:							Union[str, Any]									=randn_tensor(
																	    model_output.shape       ,	dtype=model_output.dtype       ,	generator=snake_case__       ,	device=model_output.device )

																	lowerCamelCase_	:							Any									=self._get_variance(
																	    snake_case__       ,	predicted_variance=snake_case__       ,	prev_timestep=snake_case__       ,	)

																	if self.variance_type == "fixed_small_log":
																								lowerCamelCase_	:							Tuple									=variance
																	elif self.variance_type == "learned_range":
																								lowerCamelCase_	:							Tuple									=(0.5 * variance).exp()
																	else:
																								raise ValueError(
																								    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																								    " for the UnCLIPScheduler." )

																	lowerCamelCase_	:							Dict									=variance * variance_noise

										lowerCamelCase_	:							List[Any]									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=snake_case__       ,	pred_original_sample=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  torch.IntTensor       ,	):
										# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
										lowerCamelCase_	:							Optional[Any]									=self.alphas_cumprod.to(device=original_samples.device       ,	dtype=original_samples.dtype )
										lowerCamelCase_	:							List[str]									=timesteps.to(original_samples.device )

										lowerCamelCase_	:							List[Any]									=alphas_cumprod[timesteps] ** 0.5
										lowerCamelCase_	:							Optional[Any]									=sqrt_alpha_prod.flatten()
										while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
																	lowerCamelCase_	:							List[str]									=sqrt_alpha_prod.unsqueeze(-1 )

										lowerCamelCase_	:							Any									=(1 - alphas_cumprod[timesteps]) ** 0.5
										lowerCamelCase_	:							Dict									=sqrt_one_minus_alpha_prod.flatten()
										while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
																	lowerCamelCase_	:							Optional[int]									=sqrt_one_minus_alpha_prod.unsqueeze(-1 )

										lowerCamelCase_	:							List[Any]									=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
										return noisy_samples











"""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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 			_snake_case    (					lowerCamelCase__     :   int=None  )    ->     Union[str, Any]:
							if subparsers is not None:
														lowerCamelCase_	:							List[Any]									=subparsers.add_parser("test"  )
							else:
														lowerCamelCase_	:							List[str]									=argparse.ArgumentParser("Accelerate test command"  )

							parser.add_argument(
							    "--config_file"    ,						default=lowerCamelCase__    ,						help=(
							        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )    ,						)

							if subparsers is not None:
														parser.set_defaults(func=lowerCamelCase__  )
							return parser

def 			_snake_case    (					lowerCamelCase__     :   List[Any]  )    ->     Any:
							lowerCamelCase_	:							Optional[Any]									=os.path.sep.join(__file__.split(os.path.sep  )[:-2] + ["test_utils", "scripts", "test_script.py"]  )

							if args.config_file is None:
														lowerCamelCase_	:							List[Any]									=script_name
							else:
														lowerCamelCase_	:							Union[str, Any]									=F"""--config_file={args.config_file} {script_name}"""

							lowerCamelCase_	:							List[str]									=["accelerate-launch"] + test_args.split()
							lowerCamelCase_	:							Tuple									=execute_subprocess_async(lowerCamelCase__    ,						env=os.environ.copy()  )
							if result.returncode == 0:
														print("Test is a success! You are ready for your distributed training!"  )

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Any									=test_command_parser()
							lowerCamelCase_	:							List[Any]									=parser.parse_args()
							test_command(lowerCamelCase__  )


if __name__ == "__main__":
						main()



