






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


snake_case_   :	List[str]							      =logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class        a__      (	A_		):
		UpperCAmelCase_							:       torch.FloatTensor





class        a__      (	A_	,       A_		):
		UpperCAmelCase_							:       str 					= 1






		@register_to_config
		def __init__(      self							, lowercase__ = 40							, lowercase__ = 0.002							, lowercase__ = 80.0							, lowercase__ = 0.5							, lowercase__ = 1.0							, lowercase__ = 7.0							, lowercase__ = True							, )				->				Optional[int]:
				# standard deviation of the initial noise distribution
				__A    =							sigma_max

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=None						)				->				Any:
				if schedule_timesteps is None:
						__A    =							self.timesteps

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				torch.FloatTensor:
				# Get sigma corresponding to timestep
				if isinstance(_A							, torch.Tensor						):
						__A    =							timestep.to(self.timesteps.device						)
				__A    =							self.index_for_timestep(_A						)
				__A    =							self.sigmas[step_idx]

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

				__A    =							True
				return sample






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Dict:
				if not isinstance(_A							, np.ndarray						):
						__A    =							np.array(_A							, dtype=np.floataa						)

				__A    =							1000 * 0.25 * np.log(sigmas + 1e-44						)

				return timesteps






		def 					_lowerCamelCase					(      self							, lowercase__ = None							, lowercase__ = None							, lowercase__ = 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}."""						)

						__A    =							np.array(_A							, dtype=np.intaa						)
						__A    =							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."""						)

						__A    =							num_inference_steps

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

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

				__A    =							np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
				__A    =							torch.from_numpy(_A						).to(device=_A						)

				if str(_A						).startswith("mps"						):
						# mps does not support float64
						__A    =							torch.from_numpy(_A						).to(_A							, dtype=torch.floataa						)
				else:
						__A    =							torch.from_numpy(_A						).to(device=_A						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Tuple:
				__A    =							self.config.sigma_min
				__A    =							self.config.sigma_max

				__A    =							self.config.rho
				__A    =							sigma_min ** (1 / rho)
				__A    =							sigma_max ** (1 / rho)
				__A    =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				List[Any]:
				__A    =							self.config.sigma_data

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






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				List[str]:
				__A    =							self.config.sigma_min
				__A    =							self.config.sigma_data

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__ = None							, lowercase__ = 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						):
						__A    =							timestep.to(self.timesteps.device						)

				__A    =							self.config.sigma_min
				__A    =							self.config.sigma_max

				__A    =							self.index_for_timestep(_A						)

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

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

				# 1. Denoise model output using boundary conditions
				__A    =							c_out * model_output + c_skip * sample
				if self.config.clip_denoised:
						__A    =							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:
						__A    =							randn_tensor(
						    model_output.shape							, dtype=model_output.dtype							, device=model_output.device							, generator=_A						)
				else:
						__A    =							torch.zeros_like(_A						)
				__A    =							noise * self.config.s_noise

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

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

				if not return_dict:
						return (prev_sample,)

				return CMStochasticIterativeSchedulerOutput(prev_sample=_A						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, )				->				torch.FloatTensor:
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				__A    =							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
						__A    =							self.timesteps.to(original_samples.device							, dtype=torch.floataa						)
						__A    =							timesteps.to(original_samples.device							, dtype=torch.floataa						)
				else:
						__A    =							self.timesteps.to(original_samples.device						)
						__A    =							timesteps.to(original_samples.device						)

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

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

				__A    =							original_samples + noise * sigma
				return noisy_samples






		def __len__(      self						)				->				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  )

