



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 (    A__    ,	A__=0.9_99    ,	A__="cosine"    ,	):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(A__						):
                  return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

      elif alpha_transform_type == "exp":

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

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

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



class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase						):

   '''simple docstring'''




   A     :	List[Any]												=     [e.name for e in KarrasDiffusionSchedulers]
   A     :	Dict												=     2


   @register_to_config
   def __init__(    self	:      Optional[Any]      ,					lowerCAmelCase	:      int = 10_00      ,					lowerCAmelCase	:      float = 0.0_00_85      ,					lowerCAmelCase	:      float = 0.0_12      ,					lowerCAmelCase	:      str = "linear"      ,					lowerCAmelCase	:      Optional[Union[np.ndarray, List[float]]] = None      ,					lowerCAmelCase	:      str = "epsilon"      ,					lowerCAmelCase	:      str = "linspace"      ,					lowerCAmelCase	:      int = 0      ,					)						->					Optional[Any]:




         """simple docstring"""

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

         lowercase__  =	1.0 - self.betas
         lowercase__  =	torch.cumprod(self.alphas      ,					dim=0)

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


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[str]=None)						->					List[Any]:




         """simple docstring"""

         if schedule_timesteps is None:
               lowercase__  =	self.timesteps

         lowercase__  =	(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:
               lowercase__  =	1 if len(lowerCAmelCase) > 1 else 0
         else:
               lowercase__  =	timestep.cpu().item() if torch.is_tensor(lowerCAmelCase) else timestep
               lowercase__  =	self._index_counter[timestep_int]

         return indices[pos].item()


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




         """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	:      int      ,					lowerCAmelCase	:      torch.FloatTensor      ,					lowerCAmelCase	:      Union[float, torch.FloatTensor]      ,					)						->					torch.FloatTensor:




         """simple docstring"""

         lowercase__  =	self.index_for_timestep(lowerCAmelCase)

         if self.state_in_first_order:
               lowercase__  =	self.sigmas[step_index]
         else:
               lowercase__  =	self.sigmas_interpol[step_index]

         lowercase__  =	sample / ((sigma**2 + 1) ** 0.5)
         return sample


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Union[str, torch.device] = None      ,					lowerCAmelCase	:      Optional[int] = None      ,					)						->					str:




         """simple docstring"""

         lowercase__  =	num_inference_steps

         lowercase__  =	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":
               lowercase__  =	np.linspace(0      ,					num_train_timesteps - 1      ,					lowerCAmelCase      ,					dtype=lowerCAmelCase)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase__  =	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
               lowercase__  =	(np.arange(0      ,					lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase__  =	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
               lowercase__  =	(np.arange(lowerCAmelCase      ,					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\'.''')

         lowercase__  =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         lowercase__  =	torch.from_numpy(np.log(lowerCAmelCase)).to(lowerCAmelCase)

         lowercase__  =	np.interp(lowerCAmelCase      ,					np.arange(0      ,					len(lowerCAmelCase))      ,					lowerCAmelCase)
         lowercase__  =	np.concatenate([sigmas, [0.0]]).astype(np.floataa)
         lowercase__  =	torch.from_numpy(lowerCAmelCase).to(device=lowerCAmelCase)

         # interpolate sigmas
         lowercase__  =	sigmas.log().lerp(sigmas.roll(1).log()      ,					0.5).exp()

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

         if str(lowerCAmelCase).startswith('mps'):
               # mps does not support float64
               lowercase__  =	torch.from_numpy(lowerCAmelCase).to(lowerCAmelCase      ,					dtype=torch.floataa)
         else:
               lowercase__  =	torch.from_numpy(lowerCAmelCase).to(lowerCAmelCase)

         # interpolate timesteps
         lowercase__  =	self.sigma_to_t(lowerCAmelCase).to(lowerCAmelCase      ,					dtype=timesteps.dtype)
         lowercase__  =	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])      ,					dim=-1).flatten()

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

         lowercase__  =	None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         lowercase__  =	defaultdict(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      int)						->					str:




         """simple docstring"""

         lowercase__  =	sigma.log()

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

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

         lowercase__  =	self.log_sigmas[low_idx]
         lowercase__  =	self.log_sigmas[high_idx]

         # interpolate sigmas
         lowercase__  =	(low - log_sigma) / (low - high)
         lowercase__  =	w.clamp(0      ,					1)

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


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




         """simple docstring"""

         return self.sample is None


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Union[torch.FloatTensor, np.ndarray]      ,					lowerCAmelCase	:      Union[float, torch.FloatTensor]      ,					lowerCAmelCase	:      Union[torch.FloatTensor, np.ndarray]      ,					lowerCAmelCase	:      bool = True      ,					)						->					Union[SchedulerOutput, Tuple]:




         """simple docstring"""

         lowercase__  =	self.index_for_timestep(lowerCAmelCase)

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

         if self.state_in_first_order:
               lowercase__  =	self.sigmas[step_index]
               lowercase__  =	self.sigmas_interpol[step_index + 1]
               lowercase__  =	self.sigmas[step_index + 1]
         else:
               # 2nd order / KDPM2's method
               lowercase__  =	self.sigmas[step_index - 1]
               lowercase__  =	self.sigmas_interpol[step_index]
               lowercase__  =	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
         lowercase__  =	0
         lowercase__  =	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":
               lowercase__  =	sigma_hat if self.state_in_first_order else sigma_interpol
               lowercase__  =	sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
               lowercase__  =	sigma_hat if self.state_in_first_order else sigma_interpol
               lowercase__  =	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
               lowercase__  =	(sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               lowercase__  =	sigma_interpol - sigma_hat

               # store for 2nd order step
               lowercase__  =	sample
         else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               lowercase__  =	(sample - pred_original_sample) / sigma_interpol

               # 3. delta timestep
               lowercase__  =	sigma_next - sigma_hat

               lowercase__  =	self.sample
               lowercase__  =	None

         lowercase__  =	sample + derivative * dt

         if not return_dict:
               return (prev_sample,)

         return SchedulerOutput(prev_sample=lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      torch.FloatTensor      ,					lowerCAmelCase	:      torch.FloatTensor      ,					lowerCAmelCase	:      torch.FloatTensor      ,					)						->					torch.FloatTensor:




         """simple docstring"""

         lowercase__  =	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
               lowercase__  =	self.timesteps.to(original_samples.device      ,					dtype=torch.floataa)
               lowercase__  =	timesteps.to(original_samples.device      ,					dtype=torch.floataa)
         else:
               lowercase__  =	self.timesteps.to(original_samples.device)
               lowercase__  =	timesteps.to(original_samples.device)

         lowercase__  =	[self.index_for_timestep(lowerCAmelCase      ,					lowerCAmelCase) for t in timesteps]

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

         lowercase__  =	original_samples + noise * sigma
         return noisy_samples



   def __len__(    self	:      Optional[Any])						->					int:




         """simple docstring"""

         return self.config.num_train_timesteps





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__   :					Optional[int] 						=      logging.get_logger(__name__)
a__   :					Dict 						=      {"vocab_file": "spiece.model"}

a__   :					str 						=      {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

a__   :					Union[str, Any] 						=      {
    "albert-base-v1": 5_12,
    "albert-large-v1": 5_12,
    "albert-xlarge-v1": 5_12,
    "albert-xxlarge-v1": 5_12,
    "albert-base-v2": 5_12,
    "albert-large-v2": 5_12,
    "albert-xlarge-v2": 5_12,
    "albert-xxlarge-v2": 5_12,
}

a__   :					Optional[int] 						=      "▁"



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Tuple												=     VOCAB_FILES_NAMES
   A     :	Dict												=     PRETRAINED_VOCAB_FILES_MAP
   A     :	str												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


   def __init__(    self	:      List[str]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[int]=True      ,					lowerCAmelCase	:      List[Any]=True      ,					lowerCAmelCase	:      str=False      ,					lowerCAmelCase	:      str="[CLS]"      ,					lowerCAmelCase	:      Optional[int]="[SEP]"      ,					lowerCAmelCase	:      int="<unk>"      ,					lowerCAmelCase	:      str="[SEP]"      ,					lowerCAmelCase	:      Dict="<pad>"      ,					lowerCAmelCase	:      int="[CLS]"      ,					lowerCAmelCase	:      Tuple="[MASK]"      ,					lowerCAmelCase	:      Optional[Dict[str, Any]] = None      ,					**lowerCAmelCase	:      List[str]      ,					)						->					None:




         """simple docstring"""

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

         lowercase__  =	{} if sp_model_kwargs is None else sp_model_kwargs

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

         lowercase__  =	do_lower_case
         lowercase__  =	remove_space
         lowercase__  =	keep_accents
         lowercase__  =	vocab_file

         lowercase__  =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(lowerCAmelCase)


   @property
   def       UpperCAmelCase		(    self	:      Any)						->					Optional[int]:




         """simple docstring"""

         return len(self.sp_model)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Any:




         """simple docstring"""

         lowercase__  =	{self.convert_ids_to_tokens(lowerCAmelCase): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab


   def __getstate__(    self	:      int)						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.__dict__.copy()
         lowercase__  =	None
         return state


   def __setstate__(    self	:      int      ,					lowerCAmelCase	:      str)						->					Any:




         """simple docstring"""

         lowercase__  =	d

         # for backward compatibility
         if not hasattr(self      ,					'sp_model_kwargs'):
               lowercase__  =	{}

         lowercase__  =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      int)						->					List[str]:




         """simple docstring"""

         if self.remove_space:
               lowercase__  =	' '.join(inputs.strip().split())
         else:
               lowercase__  =	inputs
         lowercase__  =	outputs.replace('``'      ,					'"').replace('\'\''      ,					'"')

         if not self.keep_accents:
               lowercase__  =	unicodedata.normalize('NFKD'      ,					lowerCAmelCase)
               lowercase__  =	''.join([c for c in outputs if not unicodedata.combining(lowerCAmelCase)])
         if self.do_lower_case:
               lowercase__  =	outputs.lower()

         return outputs


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      str)						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.preprocess_text(lowerCAmelCase)
         lowercase__  =	self.sp_model.encode(lowerCAmelCase      ,					out_type=lowerCAmelCase)
         lowercase__  =	[]
         for piece in pieces:
               if len(lowerCAmelCase) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
                     lowercase__  =	self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase      ,					''))
                     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                           if len(cur_pieces[0]) == 1:
                                 lowercase__  =	cur_pieces[1:]
                           else:
                                 lowercase__  =	cur_pieces[0][1:]
                     cur_pieces.append(piece[-1])
                     new_pieces.extend(lowerCAmelCase)
               else:
                     new_pieces.append(lowerCAmelCase)

         return new_pieces


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      int)						->					int:




         """simple docstring"""

         return self.sp_model.PieceToId(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Any)						->					Tuple:




         """simple docstring"""

         return self.sp_model.IdToPiece(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	[]
         lowercase__  =	''
         lowercase__  =	False
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     if not prev_is_special:
                           out_string += " "
                     out_string += self.sp_model.decode(lowerCAmelCase) + token
                     lowercase__  =	True
                     lowercase__  =	[]
               else:
                     current_sub_tokens.append(lowerCAmelCase)
                     lowercase__  =	False
         out_string += self.sp_model.decode(lowerCAmelCase)
         return out_string.strip()


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None      ,					lowerCAmelCase	:      bool = False)						->					List[int]:




         """simple docstring"""

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase      ,					token_ids_a=lowerCAmelCase      ,					already_has_special_tokens=lowerCAmelCase)

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


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[str] = None)						->					Tuple[str]:




         """simple docstring"""

         if not os.path.isdir(lowerCAmelCase):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
               return
         lowercase__  =	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) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file      ,					lowerCAmelCase)
         elif not os.path.isfile(self.vocab_file):
               with open(lowerCAmelCase      ,					'wb') as fi:
                     lowercase__  =	self.sp_model.serialized_model_proto()
                     fi.write(lowerCAmelCase)

         return (out_vocab_file,)
