'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
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
from ..utils.torch_utils import randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class RePaintSchedulerOutput(BaseOutput):
    """Args:"""
    prev_sample: torch.Tensor
    pred_original_sample: torch.Tensor
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999, alpha_transform_type='cosine'):
    """Returns:"""
    if alpha_transform_type == 'cosine':
        def alpha_bar_fn(t): return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
    elif alpha_transform_type == 'exp':
        def alpha_bar_fn(t): return math.exp(t * -12.0)
    else: raise ValueError(f'Unsupported alpha_transform_type: {alpha_transform_type}')
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
    return torch.tensor(betas, dtype=torch.float32)
class RePaintScheduler(SchedulerMixin, ConfigMixin):
    """Args:"""
    order = 1
    @register_to_config
    def __init__(self, num_train_timesteps: int=1000, beta_start: float=0.0001, beta_end: float=0.02, beta_schedule: str='linear', eta: float=0.0,
    trained_betas: Optional[np.ndarray]=None, clip_sample: bool=True):
        if trained_betas is not None: self.betas = torch.from_numpy(trained_betas)
        elif beta_schedule == 'linear': self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
        elif beta_schedule == 'scaled_linear': self.betas = torch.linspace(beta_start ** 0.5, beta_end ** 0.5, num_train_timesteps, dtype=torch.float32) ** 2
        elif beta_schedule == 'squaredcos_cap_v2': self.betas = betas_for_alpha_bar(num_train_timesteps)
        elif beta_schedule == 'sigmoid':
            betas = torch.linspace(-6, 6, num_train_timesteps)
            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
        else: raise NotImplementedError(f'{beta_schedule} is not implemented for {self.__class__}')
        self.alphas = 1.0 - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
        self.one = torch.tensor(1.0)
        self.final_alpha_cumprod = torch.tensor(1.0)
        self.init_noise_sigma = 1.0
        self.num_inference_steps = None
        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
        self.eta = eta
    def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int]=None) -> torch.Tensor:
        """Returns:"""
        return sample
    def set_timesteps(self, num_inference_steps: int, jump_length: int=10, jump_n_sample: int=10, device: Union[str, torch.device]=None):
        """Args:"""
        num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
        self.num_inference_steps = num_inference_steps
        timesteps = []
        jumps = {}
        for j in range(0, num_inference_steps - jump_length, jump_length): jumps[j] = jump_n_sample - 1
        t = num_inference_steps
        while t >= 1:
            t = t - 1
            timesteps.append(t)
            if jumps.get(t, 0) > 0:
                jumps[t] = jumps[t] - 1
                for _ in range(jump_length):
                    t = t + 1
                    timesteps.append(t)
        timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)
        self.timesteps = torch.from_numpy(timesteps).to(device)
    def _get_variance(self, t):
        prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps
        alpha_prod_t = self.alphas_cumprod[t]
        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        beta_prod_t = 1 - alpha_prod_t
        beta_prod_t_prev = 1 - alpha_prod_t_prev
        variance = beta_prod_t_prev / beta_prod_t * (1 - alpha_prod_t / alpha_prod_t_prev)
        return variance
    def step(self, model_output: torch.Tensor, timestep: int, sample: torch.Tensor, original_image: torch.Tensor, mask: torch.Tensor, generator: Optional[torch.Generator]=None,
    return_dict: bool=True) -> Union[RePaintSchedulerOutput, Tuple]:
        """Returns:"""
        t = timestep
        prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
        alpha_prod_t = self.alphas_cumprod[t]
        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        beta_prod_t = 1 - alpha_prod_t
        pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        if self.config.clip_sample: pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
        device = model_output.device
        noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)
        std_dev_t = self.eta * self._get_variance(timestep) ** 0.5
        variance = 0
        if t > 0 and self.eta > 0: variance = std_dev_t * noise
        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t ** 2) ** 0.5 * model_output
        prev_unknown_part = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction + variance
        prev_known_part = alpha_prod_t_prev ** 0.5 * original_image + (1 - alpha_prod_t_prev) * noise
        pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part
        if not return_dict: return (pred_prev_sample, pred_original_sample)
        return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
    def undo_step(self, sample, timestep, generator=None):
        n = self.config.num_train_timesteps // self.num_inference_steps
        for i in range(n):
            beta = self.betas[timestep + i]
            if sample.device.type == 'mps':
                noise = randn_tensor(sample.shape, dtype=sample.dtype, generator=generator)
                noise = noise.to(sample.device)
            else: noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
            sample = (1 - beta) ** 0.5 * sample + beta ** 0.5 * noise
        return sample
    def add_noise(self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor: raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.')
    def __len__(self): return self.config.num_train_timesteps
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
