





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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=0.999					,							_UpperCamelCase="cosine"					,							)						->		Union[str, Any]:






				"""simple docstring"""


				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				snake_case_		:       Tuple           =    []
				for i in range(snake_case__					):
								snake_case_		:       Dict           =    i / num_diffusion_timesteps
								snake_case_		:       Union[str, Any]           =    (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    __lowerCAmelCase			(     lowercase_,	lowercase_	):
			lowerCamelCase_	:	Dict			     = [e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase_	:	Tuple			     = 2






			@register_to_config
			def __init__(self   ,				__magic_name__ = 1000   ,				__magic_name__ = 0.00_085   ,				__magic_name__ = 0.012   ,				__magic_name__ = "linear"   ,				__magic_name__ = None   ,				__magic_name__ = "epsilon"   ,				__magic_name__ = "linspace"   ,				__magic_name__ = 0   ,				)	->  str:


							'''simple docstring'''



							if trained_betas is not None:
											snake_case_		:       Dict           =    torch.tensor(a__   ,				dtype=torch.floataa       )
							elif beta_schedule == "linear":
											snake_case_		:       Optional[int]           =    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_		:       Any           =    (
											    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_		:       List[str]           =    betas_for_alpha_bar(a__       )
							else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

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

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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__=None       )	->  Dict:


							'''simple docstring'''



							if schedule_timesteps is None:
											snake_case_		:       List[str]           =    self.timesteps

							snake_case_		:       List[str]           =    (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_		:       Any           =    1 if len(a__       ) > 1 else 0
							else:
											snake_case_		:       int           =    timestep.cpu().item() if torch.is_tensor(a__       ) else timestep
											snake_case_		:       Dict           =    self._index_counter[timestep_int]

							return indices[pos].item()






			@property
			def      lowerCamelCase    (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      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				)	->  torch.FloatTensor:


							'''simple docstring'''



							snake_case_		:       List[str]           =    self.index_for_timestep(a__       )

							if self.state_in_first_order:
											snake_case_		:       List[Any]           =    self.sigmas[step_index]
							else:
											snake_case_		:       Tuple           =    self.sigmas_interpol[step_index]

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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				)	->  Dict:


							'''simple docstring'''



							snake_case_		:       Tuple           =    num_inference_steps

							snake_case_		:       str           =    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_		:       Any           =    np.linspace(0   ,				num_train_timesteps - 1   ,				a__   ,				dtype=a__       )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
											snake_case_		:       Optional[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_		:       Optional[int]           =    (np.arange(0   ,				a__       ) * step_ratio).round()[::-1].copy().astype(a__       )
											timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
											snake_case_		:       Union[str, Any]           =    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_		:       Union[str, Any]           =    (np.arange(a__   ,				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_		:       List[str]           =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
							snake_case_		:       Optional[Any]           =    torch.from_numpy(np.log(a__       )       ).to(a__       )

							snake_case_		:       Optional[Any]           =    np.interp(a__   ,				np.arange(0   ,				len(a__       )       )   ,				a__       )
							snake_case_		:       Any           =    np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
							snake_case_		:       Optional[int]           =    torch.from_numpy(a__       ).to(device=a__       )

							# interpolate sigmas
							snake_case_		:       Any           =    sigmas.log().lerp(sigmas.roll(1       ).log()   ,				0.5       ).exp()

							snake_case_		:       int           =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
							snake_case_		:       Any           =    torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

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

							# interpolate timesteps
							snake_case_		:       Union[str, Any]           =    self.sigma_to_t(a__       ).to(a__   ,				dtype=timesteps.dtype       )
							snake_case_		:       Dict           =    torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])   ,				dim=-1       ).flatten()

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

							snake_case_		:       Optional[Any]           =    None

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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       List[str]           =    sigma.log()

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

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

							snake_case_		:       List[str]           =    self.log_sigmas[low_idx]
							snake_case_		:       int           =    self.log_sigmas[high_idx]

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

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






			@property
			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return self.sample is None






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = True   ,				)	->  Union[SchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_		:       List[str]           =    self.index_for_timestep(a__       )

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

							if self.state_in_first_order:
											snake_case_		:       int           =    self.sigmas[step_index]
											snake_case_		:       Tuple           =    self.sigmas_interpol[step_index + 1]
											snake_case_		:       str           =    self.sigmas[step_index + 1]
							else:
											# 2nd order / KDPM2's method
											snake_case_		:       str           =    self.sigmas[step_index - 1]
											snake_case_		:       int           =    self.sigmas_interpol[step_index]
											snake_case_		:       Dict           =    self.sigmas[step_index]

							# 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_		:       str           =    0
							snake_case_		:       List[Any]           =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
											snake_case_		:       Tuple           =    sigma_hat if self.state_in_first_order else sigma_interpol
											snake_case_		:       str           =    sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_		:       List[Any]           =    sigma_hat if self.state_in_first_order else sigma_interpol
											snake_case_		:       str           =    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_		:       Dict           =    (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											snake_case_		:       Dict           =    sigma_interpol - sigma_hat

											# store for 2nd order step
											snake_case_		:       Optional[Any]           =    sample
							else:
											# DPM-Solver-2
											# 2. Convert to an ODE derivative for 2nd order
											snake_case_		:       Union[str, Any]           =    (sample - pred_original_sample) / sigma_interpol

											# 3. delta timestep
											snake_case_		:       Optional[Any]           =    sigma_next - sigma_hat

											snake_case_		:       Tuple           =    self.sample
											snake_case_		:       List[str]           =    None

							snake_case_		:       Any           =    sample + derivative * dt

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=a__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				)	->  torch.FloatTensor:


							'''simple docstring'''



							snake_case_		:       Tuple           =    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_		:       Union[str, Any]           =    self.timesteps.to(original_samples.device   ,				dtype=torch.floataa       )
											snake_case_		:       Optional[int]           =    timesteps.to(original_samples.device   ,				dtype=torch.floataa       )
							else:
											snake_case_		:       Tuple           =    self.timesteps.to(original_samples.device       )
											snake_case_		:       int           =    timesteps.to(original_samples.device       )

							snake_case_		:       Dict           =    [self.index_for_timestep(a__   ,				a__       ) for t in timesteps]

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

							snake_case_		:       Union[str, Any]           =    original_samples + noise * sigma
							return noisy_samples






			def __len__(self       )	->  List[Any]:


							'''simple docstring'''



							return self.config.num_train_timesteps








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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                do_lower_case=a__      ,    remove_space=a__      ,    keep_accents=a__      ,    bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    pad_token=a__      ,    cls_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    {self.convert_ids_to_tokens(a__     ): i for i in range(self.vocab_size     )}
            vocab.update(self.added_tokens_encoder     )
            return vocab



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [self.cls_token_id]
            if token_ids_a is None:
                 return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            if already_has_special_tokens:
                 if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model."""     )
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                 return [1] + ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [self.cls_token_id]

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



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    os.path.join(
                a__      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

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

            return (out_vocab_file,)


