






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			42
				lowercase__											=			None






def     a     (		lowerCamelCase_						,  lowerCamelCase_=0.9_99						,  lowerCamelCase_="cosine"						,  ):






				'''simple docstring'''
				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}"""							)

				lowercase__  					=     []
				for i in range(lowerCamelCase_							):
								lowercase__  					=     i / num_diffusion_timesteps
								lowercase__  					=     (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   (			A__						,A__      ):

				"""simple docstring"""




				lowercase__											=			1




				@register_to_config
				def __init__(	self		:       Dict,    lowerCamelCase		:       int = 1_000,    lowerCamelCase		:       float = 0.0001,    lowerCamelCase		:       float = 0.02,    lowerCamelCase		:       str = "linear",    lowerCamelCase		:       Optional[Union[np.ndarray, List[float]]] = None,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       bool = True,    lowerCamelCase		:       int = 0,    lowerCamelCase		:       str = "epsilon",    lowerCamelCase		:       float = 1.0,    **lowerCamelCase		:       Dict,    ):

								'''simple docstring'''
								if kwargs.get('''set_alpha_to_one''',    lowerCamelCase    ) is not None:
												lowercase__  					=     (
												    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
												)
												deprecate('''set_alpha_to_one''',    '''1.0.0''',    lowerCamelCase,    standard_warn=lowerCamelCase    )
												lowercase__  					=     kwargs['''set_alpha_to_one''']
								if trained_betas is not None:
												lowercase__  					=     torch.tensor(lowerCamelCase,    dtype=torch.floataa    )
								elif beta_schedule == "linear":
												lowercase__  					=     torch.linspace(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												lowercase__  					=     (
												    torch.linspace(beta_start**0.5,    beta_end**0.5,    lowerCamelCase,    dtype=torch.floataa    ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												lowercase__  					=     betas_for_alpha_bar(lowerCamelCase    )
								else:
												raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

								# At every step in inverted ddim, we are looking into the next alphas_cumprod
								# For the final step, there is no next alphas_cumprod, and the index is out of bounds
								# `set_alpha_to_zero` decides whether we set this parameter simply to zero
								# in this case, self.step() just output the predicted noise
								# or whether we use the final alpha of the "non-previous" one.
								lowercase__  					=     torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								lowercase__  					=     1.0

								# setable values
								lowercase__  					=     None
								lowercase__  					=     torch.from_numpy(np.arange(0,    lowerCamelCase    ).copy().astype(np.intaa    )    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       Optional[int] = None    ):

								'''simple docstring'''
								return sample




				def 							lowercase__  (	self		:       List[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Union[str, torch.device] = None    ):

								'''simple docstring'''

								if num_inference_steps > self.config.num_train_timesteps:
												raise ValueError(
												    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
												    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
												    F""" maximal {self.config.num_train_timesteps} timesteps."""    )

								lowercase__  					=     num_inference_steps
								lowercase__  					=     self.config.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
								lowercase__  					=     (np.arange(0,    lowerCamelCase    ) * step_ratio).round().copy().astype(np.intaa    )
								lowercase__  					=     torch.from_numpy(lowerCamelCase    ).to(lowerCamelCase    )
								self.timesteps += self.config.steps_offset




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       int,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       bool = False,    lowerCamelCase		:       Optional[torch.FloatTensor] = None,    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								# 1. get previous step value (=t+1)
								lowercase__  					=     timestep + self.config.num_train_timesteps // self.num_inference_steps

								# 2. compute alphas, betas
								# change original implementation to exactly match noise levels for analogous forward process
								lowercase__  					=     self.alphas_cumprod[timestep]
								lowercase__  					=     (
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

								lowercase__  					=     1 - alpha_prod_t

								# 3. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								if self.config.prediction_type == "epsilon":
												lowercase__  					=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												lowercase__  					=     model_output
								elif self.config.prediction_type == "sample":
												lowercase__  					=     model_output
												lowercase__  					=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
												lowercase__  					=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
												lowercase__  					=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
								else:
												raise ValueError(
												    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
												    ''' `v_prediction`'''    )

								# 4. Clip or threshold "predicted x_0"
								if self.config.clip_sample:
												lowercase__  					=     pred_original_sample.clamp(
												    -self.config.clip_sample_range,    self.config.clip_sample_range    )

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								lowercase__  					=     (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

								# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								lowercase__  					=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

								if not return_dict:
												return (prev_sample, pred_original_sample)
								return DDIMSchedulerOutput(prev_sample=lowerCamelCase,    pred_original_sample=lowerCamelCase    )




				def __len__(	self		:       int    ):

								'''simple docstring'''
								return self.config.num_train_timesteps












import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A__      :     Tuple       =					{
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
								    F""" reinstalling {pkg}."""							)
				if not ops[op](version.parse(lowerCamelCase_							)						,  version.parse(lowerCamelCase_							)							):
								raise ImportError(
								    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_ = None							):






				'''simple docstring'''
				lowercase__  					=     F"""\n{hint}""" if hint is not None else ''''''

				# non-versioned check
				if re.match(r'''^[\w_\-\d]+$'''						,  lowerCamelCase_							):
								lowercase__					,		lowercase__					,		lowercase__  					=     requirement, None, None
				else:
								lowercase__  					=     re.findall(r'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)'''						,  lowerCamelCase_							)
								if not match:
												raise ValueError(
												    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
												    F""" got {requirement}"""							)
								lowercase__					,		lowercase__  					=     match[0]
								lowercase__  					=     want_full.split(''','''							)  # there could be multiple requirements
								lowercase__  					=     {}
								for w in want_range:
												lowercase__  					=     re.findall(r'''^([\s!=<>]{1,2})(.+)'''						,  lowerCamelCase_							)
												if not match:
																raise ValueError(
																    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																    F""" but got {requirement}"""							)
												lowercase__					,		lowercase__  					=     match[0]
												lowercase__  					=     want_ver
												if op not in ops:
																raise ValueError(F"""{requirement}: need one of {list(ops.keys()							)}, but got {op}"""							)

    # special case
				if pkg == "python":
								lowercase__  					=     '''.'''.join([str(lowerCamelCase_							) for x in sys.version_info[:3]]							)
								for op, want_ver in wanted.items():
												_compare_versions(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)
								return

				# check if any version is installed
				try:
								lowercase__  					=     importlib.metadata.version(lowerCamelCase_							)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""							)

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
				return require_version(lowerCamelCase_						,  lowerCamelCase_							)




