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

import numpy as np
import torch

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


_A       =					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				torch.FloatTensor
     UpperCAmelCase__							:				Optional[torch.FloatTensor] 	=						None



def 		lowercase_     (  A__					,						A__=0.999					,						A__="cosine"					,						)				->				Union[str, Any]:
    """simple docstring"""

    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(A__						):
            return math.exp(t * -12.0						)

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

    snake_case						=   []
    for i in range(A__						):
        snake_case						=   i / num_diffusion_timesteps
        snake_case						=   (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(A__						) / alpha_bar_fn(A__						)					,						A__						)						)
    return torch.tensor(A__					,						dtype=torch.floataa						)







class 	lowerCamelCase						(    A_				,					A_    ):
     UpperCAmelCase__							:				List[Any] 	=						[e.name for e in KarrasDiffusionSchedulers]
     UpperCAmelCase__							:				Tuple 	=						1





     @register_to_config
     def __init__(self :       Optional[Any]							,       _A :       int = 1_0_0_0							,       _A :       float = 0.00_01							,       _A :       float = 0.02							,       _A :       str = "linear"							,       _A :       Optional[Union[np.ndarray, List[float]]] = None							,       _A :       str = "epsilon"							,       _A :       str = "linear"							,       _A :       Optional[bool] = False							,       _A :       str = "linspace"							,       _A :       int = 0							,       )		->		str:
         if trained_betas is not None:
             snake_case						=   torch.tensor(_A							,       dtype=torch.floataa  )
         elif beta_schedule == "linear":
             snake_case						=   torch.linspace(_A							,       _A							,       _A							,       dtype=torch.floataa  )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             snake_case						=   (
                 torch.linspace(beta_start**0.5							,       beta_end**0.5							,       _A							,       dtype=torch.floataa  ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             snake_case						=   betas_for_alpha_bar(_A  )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )

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

         snake_case						=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
         snake_case						=   np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
         snake_case						=   torch.from_numpy(_A  )

         # setable values
         snake_case						=   None
         snake_case						=   np.linspace(0							,       num_train_timesteps - 1							,       _A							,       dtype=_A  )[::-1].copy()
         snake_case						=   torch.from_numpy(_A  )
         snake_case						=   False
         snake_case						=   use_karras_sigmas





     @property
     def 						UpperCAmelCase(self :       str  )		->		Dict:
         # 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 						UpperCAmelCase(self :       int							,       _A :       torch.FloatTensor							,       _A :       Union[float, torch.FloatTensor]  )		->		torch.FloatTensor:
         if isinstance(_A							,       torch.Tensor  ):
             snake_case						=   timestep.to(self.timesteps.device  )
         snake_case						=   (self.timesteps == timestep).nonzero().item()
         snake_case						=   self.sigmas[step_index]

         snake_case						=   sample / ((sigma**2 + 1) ** 0.5)

         snake_case						=   True
         return sample





     def 						UpperCAmelCase(self :       List[str]							,       _A :       int							,       _A :       Union[str, torch.device] = None  )		->		List[str]:
         snake_case						=   num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             snake_case						=   np.linspace(0							,       self.config.num_train_timesteps - 1							,       _A							,       dtype=_A  )[
                 ::-1
             ].copy()
         elif self.config.timestep_spacing == "leading":
             snake_case						=   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
             snake_case						=   (np.arange(0							,       _A  ) * step_ratio).round()[::-1].copy().astype(_A  )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             snake_case						=   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
             snake_case						=   (np.arange(self.config.num_train_timesteps							,       0							,       -step_ratio  )).round().copy().astype(_A  )
             timesteps -= 1
         else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'  )

         snake_case						=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
         snake_case						=   np.log(_A  )

         if self.config.interpolation_type == "linear":
             snake_case						=   np.interp(_A							,       np.arange(0							,       len(_A  )  )							,       _A  )
         elif self.config.interpolation_type == "log_linear":
             snake_case						=   torch.linspace(np.log(sigmas[-1]  )							,       np.log(sigmas[0]  )							,       num_inference_steps + 1  ).exp()
         else:
             raise ValueError(
                 f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                 " 'linear' or 'log_linear'"  )

         if self.use_karras_sigmas:
             snake_case						=   self._convert_to_karras(in_sigmas=_A							,       num_inference_steps=self.num_inference_steps  )
             snake_case						=   np.array([self._sigma_to_t(_A							,       _A  ) for sigma in sigmas]  )

         snake_case						=   np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
         snake_case						=   torch.from_numpy(_A  ).to(device=_A  )
         if str(_A  ).startswith("mps"  ):
             # mps does not support float64
             snake_case						=   torch.from_numpy(_A  ).to(_A							,       dtype=torch.floataa  )
         else:
             snake_case						=   torch.from_numpy(_A  ).to(device=_A  )





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       Dict							,       _A :       Dict  )		->		Optional[Any]:
         # get log sigma
         snake_case						=   np.log(_A  )

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

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

         snake_case						=   log_sigmas[low_idx]
         snake_case						=   log_sigmas[high_idx]

         # interpolate sigmas
         snake_case						=   (low - log_sigma) / (low - high)
         snake_case						=   np.clip(_A							,       0							,       1  )

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





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       torch.FloatTensor							,       _A :       int  )		->		torch.FloatTensor:
         snake_case						=   in_sigmas[-1].item()
         snake_case						=   in_sigmas[0].item()

         snake_case						=   7.0  # 7.0 is the value used in the paper
         snake_case						=   np.linspace(0							,       1							,       _A  )
         snake_case						=   sigma_min ** (1 / rho)
         snake_case						=   sigma_max ** (1 / rho)
         snake_case						=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       torch.FloatTensor							,       _A :       Union[float, torch.FloatTensor]							,       _A :       torch.FloatTensor							,       _A :       float = 0.0							,       _A :       float = 0.0							,       _A :       float = float("inf"  )							,       _A :       float = 1.0							,       _A :       Optional[torch.Generator] = None							,       _A :       bool = True							,       )		->		Union[EulerDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(_A							,       _A  )
             or isinstance(_A							,       torch.IntTensor  )
             or isinstance(_A							,       torch.LongTensor  )
         ):
             raise ValueError(
                 (
                     "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                     " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                     " one of the `scheduler.timesteps` as a timestep."
                 )							,       )

         if not self.is_scale_input_called:
             logger.warning(
                 "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                 "See `StableDiffusionPipeline` for a usage example."  )

         if isinstance(_A							,       torch.Tensor  ):
             snake_case						=   timestep.to(self.timesteps.device  )

         snake_case						=   (self.timesteps == timestep).nonzero().item()
         snake_case						=   self.sigmas[step_index]

         snake_case						=   min(s_churn / (len(self.sigmas  ) - 1)							,       2**0.5 - 1  ) if s_tmin <= sigma <= s_tmax else 0.0

         snake_case						=   randn_tensor(
             model_output.shape							,       dtype=model_output.dtype							,       device=model_output.device							,       generator=_A  )

         snake_case						=   noise * s_noise
         snake_case						=   sigma * (gamma + 1)

         if gamma > 0:
             snake_case						=   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
             snake_case						=   model_output
         elif self.config.prediction_type == "epsilon":
             snake_case						=   sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             snake_case						=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

         # 2. Convert to an ODE derivative
         snake_case						=   (sample - pred_original_sample) / sigma_hat

         snake_case						=   self.sigmas[step_index + 1] - sigma_hat

         snake_case						=   sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=_A							,       pred_original_sample=_A  )





     def 						UpperCAmelCase(self :       Optional[int]							,       _A :       torch.FloatTensor							,       _A :       torch.FloatTensor							,       _A :       torch.FloatTensor							,       )		->		torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         snake_case						=   self.sigmas.to(device=original_samples.device							,       dtype=original_samples.dtype  )
         if original_samples.device.type == "mps" and torch.is_floating_point(_A  ):
             # mps does not support float64
             snake_case						=   self.timesteps.to(original_samples.device							,       dtype=torch.floataa  )
             snake_case						=   timesteps.to(original_samples.device							,       dtype=torch.floataa  )
         else:
             snake_case						=   self.timesteps.to(original_samples.device  )
             snake_case						=   timesteps.to(original_samples.device  )

         snake_case						=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         snake_case						=   sigmas[step_indices].flatten()
         while len(sigma.shape  ) < len(original_samples.shape  ):
             snake_case						=   sigma.unsqueeze(-1  )

         snake_case						=   original_samples + noise * sigma
         return noisy_samples





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



from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

