
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 SchedulerMixin


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







@dataclass
class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				torch.FloatTensor







class 	lowerCamelCase						(    A_				,					A_    ):
     UpperCAmelCase__							:				str 	=						1





     @register_to_config
     def __init__(self :       Union[str, Any]							,       _A :       int = 4_0							,       _A :       float = 0.0_02							,       _A :       float = 80.0							,       _A :       float = 0.5							,       _A :       float = 1.0							,       _A :       float = 7.0							,       _A :       bool = True							,       )		->		Optional[int]:
         # standard deviation of the initial noise distribution
         snake_case						=   sigma_max

         snake_case						=   np.linspace(0							,       1							,       _A  )
         snake_case						=   self._convert_to_karras(_A  )
         snake_case						=   self.sigma_to_t(_A  )

         # setable values
         snake_case						=   None
         snake_case						=   torch.from_numpy(_A  )
         snake_case						=   torch.from_numpy(_A  )
         snake_case						=   False
         snake_case						=   False





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       str							,       _A :       Tuple=None  )		->		Any:
         if schedule_timesteps is None:
             snake_case						=   self.timesteps

         snake_case						=   (schedule_timesteps == timestep).nonzero()
         return indices.item()





     def 						UpperCAmelCase(self :       Dict							,       _A :       torch.FloatTensor							,       _A :       Union[float, torch.FloatTensor]  )		->		torch.FloatTensor:
         # Get sigma corresponding to timestep
         if isinstance(_A							,       torch.Tensor  ):
             snake_case						=   timestep.to(self.timesteps.device  )
         snake_case						=   self.index_for_timestep(_A  )
         snake_case						=   self.sigmas[step_idx]

         snake_case						=   sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         snake_case						=   True
         return sample





     def 						UpperCAmelCase(self :       Any							,       _A :       Union[float, np.ndarray]  )		->		Dict:
         if not isinstance(_A							,       np.ndarray  ):
             snake_case						=   np.array(_A							,       dtype=np.floataa  )

         snake_case						=   1_0_0_0 * 0.25 * np.log(sigmas + 1E-44  )

         return timesteps





     def 						UpperCAmelCase(self :       Any							,       _A :       Optional[int] = None							,       _A :       Union[str, torch.device] = None							,       _A :       Optional[List[int]] = None							,       )		->		str:
         if num_inference_steps is None and timesteps is None:
             raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."  )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."  )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1							,       len(_A  )  ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError("`timesteps` must be in descending order."  )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f'`timesteps` must start before `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps}.'  )

             snake_case						=   np.array(_A							,       dtype=np.intaa  )
             snake_case						=   True
         else:
             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.'  )

             snake_case						=   num_inference_steps

             snake_case						=   self.config.num_train_timesteps // self.num_inference_steps
             snake_case						=   (np.arange(0							,       _A  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
             snake_case						=   False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         snake_case						=   self.config.num_train_timesteps
         snake_case						=   timesteps[::-1].copy()
         snake_case						=   ramp / (num_train_timesteps - 1)
         snake_case						=   self._convert_to_karras(_A  )
         snake_case						=   self.sigma_to_t(_A  )

         snake_case						=   np.concatenate([sigmas, [self.sigma_min]]  ).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 :       List[Any]							,       _A :       Tuple  )		->		Tuple:
         snake_case						=   self.config.sigma_min
         snake_case						=   self.config.sigma_max

         snake_case						=   self.config.rho
         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 :       Tuple							,       _A :       List[str]  )		->		List[Any]:
         snake_case						=   self.config.sigma_data

         snake_case						=   sigma_data**2 / (sigma**2 + sigma_data**2)
         snake_case						=   sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       List[str]  )		->		List[str]:
         snake_case						=   self.config.sigma_min
         snake_case						=   self.config.sigma_data

         snake_case						=   sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         snake_case						=   (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       torch.FloatTensor							,       _A :       Union[float, torch.FloatTensor]							,       _A :       torch.FloatTensor							,       _A :       Optional[torch.Generator] = None							,       _A :       bool = True							,       )		->		Union[CMStochasticIterativeSchedulerOutput, 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"
                     f' `{self.__class__}.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.config.sigma_min
         snake_case						=   self.config.sigma_max

         snake_case						=   self.index_for_timestep(_A  )

         # sigma_next corresponds to next_t in original implementation
         snake_case						=   self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             snake_case						=   self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             snake_case						=   self.sigmas[-1]

         # Get scalings for boundary conditions
         snake_case	,				snake_case						=   self.get_scalings_for_boundary_condition(_A  )

         # 1. Denoise model output using boundary conditions
         snake_case						=   c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             snake_case						=   denoised.clamp(-1							,       1  )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps  ) > 1:
             snake_case						=   randn_tensor(
                 model_output.shape							,       dtype=model_output.dtype							,       device=model_output.device							,       generator=_A  )
         else:
             snake_case						=   torch.zeros_like(_A  )
         snake_case						=   noise * self.config.s_noise

         snake_case						=   sigma_next.clamp(min=_A							,       max=_A  )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         snake_case						=   denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=_A  )





     def 						UpperCAmelCase(self :       List[Any]							,       _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 :       Optional[Any]  )		->		Dict:
         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  )

