






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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

def    lowerCAmelCase__		(			_a				:	Optional[Any]     ,					_a				:	List[Any]=0.999     ,					_a				:	int="cosine"     ,					):
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		snake_case_ :							Dict         =				[]
		for i in range(SCREAMING_SNAKE_CASE_			):
				snake_case_ :							Optional[Any]         =				i / num_diffusion_timesteps
				snake_case_ :							Dict         =				(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_			) / alpha_bar_fn(SCREAMING_SNAKE_CASE_			)     ,					SCREAMING_SNAKE_CASE_			)			)
		return torch.tensor(SCREAMING_SNAKE_CASE_     ,					dtype=torch.floataa			)




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ,     SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Tuple						 =    [e.name for e in KarrasDiffusionSchedulers]
						A   :      List[str]						 =    2






						@register_to_config
						def __init__(		self						, _SCREAMING_SNAKE_CASE = 1000						, _SCREAMING_SNAKE_CASE = 0.0_0085						, _SCREAMING_SNAKE_CASE = 0.012						, _SCREAMING_SNAKE_CASE = "linear"						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = "epsilon"						, _SCREAMING_SNAKE_CASE = "linspace"						, _SCREAMING_SNAKE_CASE = 0						, )       -> str:







								if trained_betas is not None:
										snake_case_ :							List[str]         =				torch.tensor(lowercase__						, dtype=torch.floataa )
								elif beta_schedule == "linear":
										snake_case_ :							str         =				torch.linspace(lowercase__						, lowercase__						, lowercase__						, dtype=torch.floataa )
								elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										snake_case_ :							Dict         =				(
										    torch.linspace(beta_start**0.5						, beta_end**0.5						, lowercase__						, dtype=torch.floataa ) ** 2
										)
								elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										snake_case_ :							Optional[Any]         =				betas_for_alpha_bar(lowercase__ )
								else:
										raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

								snake_case_ :							Optional[Any]         =				1.0 - self.betas
								snake_case_ :							Union[str, Any]         =				torch.cumprod(self.alphas						, dim=0 )

								#  set all values
								self.set_timesteps(lowercase__						, lowercase__						, lowercase__ )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE=None )       -> Union[str, Any]:







								if schedule_timesteps is None:
										snake_case_ :							Dict         =				self.timesteps

								snake_case_ :							int         =				(schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter ) == 0:
										snake_case_ :							Union[str, Any]         =				1 if len(lowercase__ ) > 1 else 0
								else:
										snake_case_ :							Dict         =				timestep.cpu().item() if torch.is_tensor(lowercase__ ) else timestep
										snake_case_ :							Dict         =				self._index_counter[timestep_int]

								return indices[pos].item()






						@property
						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:







								if self.config.timestep_spacing in ["linspace", "trailing"]:
										return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> torch.FloatTensor:
								snake_case_ :							Optional[Any]         =				self.index_for_timestep(lowercase__ )

								if self.state_in_first_order:
										snake_case_ :							Any         =				self.sigmas[step_index]
								else:
										snake_case_ :							Optional[int]         =				self.sigmas_interpol[step_index - 1]

								snake_case_ :							Tuple         =				sample / ((sigma**2 + 1) ** 0.5)
								return sample






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, )       -> List[str]:
								snake_case_ :							Tuple         =				num_inference_steps

								snake_case_ :							Dict         =				num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
										snake_case_ :							int         =				np.linspace(0						, num_train_timesteps - 1						, lowercase__						, dtype=lowercase__ )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
										snake_case_ :							str         =				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_ :							Optional[Any]         =				(np.arange(0						, lowercase__ ) * step_ratio).round()[::-1].copy().astype(lowercase__ )
										timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
										snake_case_ :							int         =				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_ :							int         =				(np.arange(lowercase__						, 0						, -step_ratio )).round().copy().astype(lowercase__ )
										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_ :							List[Any]         =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
								snake_case_ :							str         =				torch.from_numpy(np.log(lowercase__ ) ).to(lowercase__ )

								snake_case_ :							Tuple         =				np.interp(lowercase__						, np.arange(0						, len(lowercase__ ) )						, lowercase__ )
								snake_case_ :							List[str]         =				np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
								snake_case_ :							Dict         =				torch.from_numpy(lowercase__ ).to(device=lowercase__ )

								# compute up and down sigmas
								snake_case_ :							Optional[int]         =				sigmas.roll(-1 )
								snake_case_ :							Optional[int]         =				0.0
								snake_case_ :							Optional[int]         =				(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
								snake_case_ :							List[Any]         =				(sigmas_next**2 - sigmas_up**2) ** 0.5
								snake_case_ :							Optional[Any]         =				0.0

								# compute interpolated sigmas
								snake_case_ :							int         =				sigmas.log().lerp(sigmas_down.log()						, 0.5 ).exp()
								snake_case_ :							str         =				0.0

								# set sigmas
								snake_case_ :							Dict         =				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
								snake_case_ :							Optional[Any]         =				torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
								snake_case_ :							List[Any]         =				torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
								snake_case_ :							List[str]         =				torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

								if str(lowercase__ ).startswith("mps" ):
										# mps does not support float64
										snake_case_ :							Optional[int]         =				torch.from_numpy(lowercase__ ).to(lowercase__						, dtype=torch.floataa )
								else:
										snake_case_ :							int         =				torch.from_numpy(lowercase__ ).to(lowercase__ )

								snake_case_ :							List[Any]         =				self.sigma_to_t(lowercase__ ).to(lowercase__						, dtype=timesteps.dtype )
								snake_case_ :							Union[str, Any]         =				torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						, dim=-1 ).flatten()

								snake_case_ :							List[Any]         =				torch.cat([timesteps[:1], interleaved_timesteps] )

								snake_case_ :							str         =				None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								snake_case_ :							Union[str, Any]         =				defaultdict(lowercase__ )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Optional[Any]:
								snake_case_ :							str         =				sigma.log()

								# get distribution
								snake_case_ :							List[str]         =				log_sigma - self.log_sigmas[:, None]

								# get sigmas range
								snake_case_ :							int         =				dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
								snake_case_ :							List[Any]         =				low_idx + 1

								snake_case_ :							int         =				self.log_sigmas[low_idx]
								snake_case_ :							Union[str, Any]         =				self.log_sigmas[high_idx]

								# interpolate sigmas
								snake_case_ :							Tuple         =				(low - log_sigma) / (low - high)
								snake_case_ :							Any         =				w.clamp(0						, 1 )

								# transform interpolation to time range
								snake_case_ :							int         =				(1 - w) * low_idx + w * high_idx
								snake_case_ :							int         =				t.view(sigma.shape )
								return t






						@property
						def 					_lowerCAmelCase				(		self )       -> Tuple:
								return self.sample is None






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = True						, )       -> Union[SchedulerOutput, Tuple]:
								snake_case_ :							int         =				self.index_for_timestep(lowercase__ )

								# advance index counter by 1
								snake_case_ :							int         =				timestep.cpu().item() if torch.is_tensor(lowercase__ ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
										snake_case_ :							Any         =				self.sigmas[step_index]
										snake_case_ :							Optional[Any]         =				self.sigmas_interpol[step_index]
										snake_case_ :							Optional[Any]         =				self.sigmas_up[step_index]
										snake_case_ :							List[str]         =				self.sigmas_down[step_index - 1]
								else:
										# 2nd order / KPDM2's method
										snake_case_ :							Dict         =				self.sigmas[step_index - 1]
										snake_case_ :							int         =				self.sigmas_interpol[step_index - 1]
										snake_case_ :							Tuple         =				self.sigmas_up[step_index - 1]
										snake_case_ :							Optional[Any]         =				self.sigmas_down[step_index - 1]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								snake_case_ :							List[str]         =				0
								snake_case_ :							Union[str, Any]         =				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								snake_case_ :							int         =				model_output.device
								snake_case_ :							int         =				randn_tensor(model_output.shape						, dtype=model_output.dtype						, device=lowercase__						, generator=lowercase__ )

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
										snake_case_ :							List[Any]         =				sigma_hat if self.state_in_first_order else sigma_interpol
										snake_case_ :							Dict         =				sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
										snake_case_ :							List[str]         =				sigma_hat if self.state_in_first_order else sigma_interpol
										snake_case_ :							Dict         =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
										    sample / (sigma_input**2 + 1)
										)
								elif self.config.prediction_type == "sample":
										raise NotImplementedError("prediction_type not implemented yet: sample" )
								else:
										raise ValueError(
										    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

								if self.state_in_first_order:
										# 2. Convert to an ODE derivative for 1st order
										snake_case_ :							Tuple         =				(sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										snake_case_ :							Optional[int]         =				sigma_interpol - sigma_hat

										# store for 2nd order step
										snake_case_ :							List[str]         =				sample
										snake_case_ :							Tuple         =				dt
										snake_case_ :							List[Any]         =				sample + derivative * dt
								else:
										# DPM-Solver-2
										# 2. Convert to an ODE derivative for 2nd order
										snake_case_ :							str         =				(sample - pred_original_sample) / sigma_interpol
										# 3. delta timestep
										snake_case_ :							Any         =				sigma_down - sigma_hat

										snake_case_ :							str         =				self.sample
										snake_case_ :							Dict         =				None

										snake_case_ :							Union[str, Any]         =				sample + derivative * dt
										snake_case_ :							Dict         =				prev_sample + noise * sigma_up

								if not return_dict:
										return (prev_sample,)

								return SchedulerOutput(prev_sample=lowercase__ )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, )       -> torch.FloatTensor:
								snake_case_ :							str         =				self.sigmas.to(device=original_samples.device						, dtype=original_samples.dtype )
								if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__ ):
										# mps does not support float64
										snake_case_ :							Dict         =				self.timesteps.to(original_samples.device						, dtype=torch.floataa )
										snake_case_ :							Tuple         =				timesteps.to(original_samples.device						, dtype=torch.floataa )
								else:
										snake_case_ :							List[str]         =				self.timesteps.to(original_samples.device )
										snake_case_ :							Any         =				timesteps.to(original_samples.device )

								snake_case_ :							Optional[int]         =				[self.index_for_timestep(lowercase__						, lowercase__ ) for t in timesteps]

								snake_case_ :							Optional[int]         =				sigmas[step_indices].flatten()
								while len(sigma.shape ) < len(original_samples.shape ):
										snake_case_ :							Optional[Any]         =				sigma.unsqueeze(-1 )

								snake_case_ :							int         =				original_samples + noise * sigma
								return noisy_samples






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


"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case    =		logging.get_logger(__name__)

snake_case    =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=False		,	lowercase__=True		,	lowercase__=False		,	lowercase__="<s>"		,	lowercase__="</s>"		,	lowercase__="<unk>"		,	lowercase__="<sep>"		,	lowercase__="<pad>"		,	lowercase__="<cls>"		,	lowercase__="<mask>"		,	lowercase__=["<eop>", "<eod>"]		,	**lowercase__		,	)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									super().__init__(
									    vocab_file=lowercase__		,	tokenizer_file=lowercase__		,	do_lower_case=lowercase__		,	remove_space=lowercase__		,	keep_accents=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	pad_token=lowercase__		,	cls_token=lowercase__		,	mask_token=lowercase__		,	additional_special_tokens=lowercase__		,	**lowercase__		,	)

									SCREAMING_SNAKE_CASE				=	3
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	remove_space
									SCREAMING_SNAKE_CASE				=	keep_accents
									SCREAMING_SNAKE_CASE				=	vocab_file
									SCREAMING_SNAKE_CASE				=	False if not self.vocab_file else True

									try:
														import jieba
									except ModuleNotFoundError as error:
														raise error.__class__(
														    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
														    'See https://pypi.org/project/jieba/ for installation.'			)
									SCREAMING_SNAKE_CASE				=	jieba
									SCREAMING_SNAKE_CASE				=	str.maketrans(' \n'		,	'\u2582\u2583'			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[self.cls_token_id]
									if token_ids_a is None:
														return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[2]

									if token_ids_a is None:
														return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""simple docstring"""







									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
														    'tokenizer.'			)

									if not os.path.isdir(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	os.path.join(
									    lowercase__		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase__			):
														copyfile(self.vocab_file		,	lowercase__			)

									return (out_vocab_file,)


				def 							A					(				self		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[
									    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase__		,	cut_all=lowercase__			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase__		,	*lowercase__		,	**lowercase__			)




				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super()._decode(*lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	text.replace(' '		,	''			).replace('\u2582'		,	' '			).replace('\u2583'		,	'\n'			)
									return text
