






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

import numpy as np
import torch
from scipy import integrate

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 					UpperCamelCase	(     snake_case__      ):
					__UpperCamelCase						=   42
					__UpperCamelCase						=   None




def 							_lowerCamelCase(				__snake_case						,						__snake_case=0.9_9_9						,						__snake_case="cosine"						,						)  ->   Tuple:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__snake_case	):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

		elif alpha_transform_type == "exp":

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

		else:
				raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

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





class 					UpperCamelCase	(     snake_case__			,      snake_case__      ):
					__UpperCamelCase						=   [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase						=   1




					@register_to_config
					def __init__( self :					str      ,_lowerCAmelCase :					int = 1_000      ,_lowerCAmelCase :					float = 0.0_0_0_1      ,_lowerCAmelCase :					float = 0.0_2      ,_lowerCAmelCase :					str = "linear"      ,_lowerCAmelCase :					Optional[Union[np.ndarray, List[float]]] = None      ,_lowerCAmelCase :					Optional[bool] = False      ,_lowerCAmelCase :					str = "epsilon"      ,_lowerCAmelCase :					str = "linspace"      ,_lowerCAmelCase :					int = 0      ,):


							"""simple docstring"""




							if trained_betas is not None:
									__snake_case		       =     torch.tensor(_lowerCAmelCase      ,dtype=torch.floataa						)
							elif beta_schedule == "linear":
									__snake_case		       =     torch.linspace(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,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      ,_lowerCAmelCase      ,dtype=torch.floataa						) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__snake_case		       =     betas_for_alpha_bar(_lowerCAmelCase						)
							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(_lowerCAmelCase						)

							# setable values
							__snake_case		       =     None
							__snake_case		       =     use_karras_sigmas
							self.set_timesteps(_lowerCAmelCase      ,_lowerCAmelCase						)
							__snake_case		       =     []
							__snake_case		       =     False




					@property
					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




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

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




					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					torch.FloatTensor      ,_lowerCAmelCase :					Union[float, torch.FloatTensor]						):


							"""simple docstring"""




							if isinstance(_lowerCAmelCase      ,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 :					int      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




							def lms_derivative(_lowerCAmelCase :					Union[str, Any]						):
									__snake_case		       =     1.0
									for k in range(_lowerCAmelCase						):
											if current_order == k:
													continue
											prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
									return prod

							__snake_case		       =     integrate.quad(_lowerCAmelCase      ,self.sigmas[t]      ,self.sigmas[t + 1]      ,epsrel=1E-4						)[0]

							return integrated_coeff




					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Union[str, torch.device] = None						):


							"""simple docstring"""




							__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      ,_lowerCAmelCase      ,dtype=_lowerCAmelCase						)[
									    ::-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      ,_lowerCAmelCase						) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase						)
									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(_lowerCAmelCase						)
									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(_lowerCAmelCase						)
							__snake_case		       =     np.interp(_lowerCAmelCase      ,np.arange(0      ,len(_lowerCAmelCase						)						)      ,_lowerCAmelCase						)

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

							__snake_case		       =     np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

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

							__snake_case		       =     []




					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							__snake_case		       =     np.log(_lowerCAmelCase						)

							# 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(_lowerCAmelCase      ,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 :					Optional[Any]      ,_lowerCAmelCase :					torch.FloatTensor						):


							"""simple docstring"""




							__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      ,self.num_inference_steps						)
							__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 :					List[str]      ,_lowerCAmelCase :					torch.FloatTensor      ,_lowerCAmelCase :					Union[float, torch.FloatTensor]      ,_lowerCAmelCase :					torch.FloatTensor      ,_lowerCAmelCase :					int = 4      ,_lowerCAmelCase :					bool = True      ,):


							"""simple docstring"""




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

							if isinstance(_lowerCAmelCase      ,torch.Tensor						):
									__snake_case		       =     timestep.to(self.timesteps.device						)
							__snake_case		       =     (self.timesteps == timestep).nonzero().item()
							__snake_case		       =     self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									__snake_case		       =     sample - sigma * 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))
							elif self.config.prediction_type == "sample":
									__snake_case		       =     model_output
							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
							self.derivatives.append(_lowerCAmelCase						)
							if len(self.derivatives						) > order:
									self.derivatives.pop(0						)

							# 3. Compute linear multistep coefficients
							__snake_case		       =     min(step_index + 1      ,_lowerCAmelCase						)
							__snake_case		       =     [self.get_lms_coefficient(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

							# 4. Compute previous sample based on the derivatives path
							__snake_case		       =     sample + sum(
							    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase      ,reversed(self.derivatives						)						)						)

							if not return_dict:
									return (prev_sample,)

							return LMSDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase      ,pred_original_sample=_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					torch.FloatTensor      ,_lowerCAmelCase :					torch.FloatTensor      ,_lowerCAmelCase :					torch.FloatTensor      ,):


							"""simple docstring"""




							__snake_case		       =     self.sigmas.to(device=original_samples.device      ,dtype=original_samples.dtype						)
							if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase						):
									# 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 :					Union[str, Any]						):


							"""simple docstring"""




							return self.config.num_train_timesteps






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

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


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		lowerCamelCase__           =				None


lowerCamelCase__           =				logging.get_logger(__name__)


lowerCamelCase__           =				'▁'

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

lowerCamelCase__           =				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

lowerCamelCase__           =				{
    'google/reformer-crime-and-punishment': 52_42_88,
}




class 					UpperCamelCase	(     snake_case__      ):
					__UpperCamelCase						=   VOCAB_FILES_NAMES
					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]
					__UpperCamelCase						=   ReformerTokenizer




					def __init__( self :					Tuple      ,_lowerCAmelCase :					Dict=None      ,_lowerCAmelCase :					Union[str, Any]=None      ,_lowerCAmelCase :					int="</s>"      ,_lowerCAmelCase :					Dict="<unk>"      ,_lowerCAmelCase :					Dict=[]      ,**_lowerCAmelCase :					Optional[Any]      ,):


							"""simple docstring"""




							super().__init__(
							    _lowerCAmelCase      ,tokenizer_file=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,additional_special_tokens=_lowerCAmelCase      ,**_lowerCAmelCase      ,)

							__snake_case		       =     vocab_file
							__snake_case		       =     False if not self.vocab_file else True







					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):


							"""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(_lowerCAmelCase						):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
									return
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

							return (out_vocab_file,)





