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_ :			Dict    ,						A_ :			Any=0.9_99    ,						A_ :			Optional[Any]="cosine"    ,						)     ->	str:





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_ :			Optional[Any]       ):
      return math.exp(t * -12.0       )

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

  __UpperCAmelCase         =							[]
  for i in range(a__       ):
    __UpperCAmelCase         =							i / num_diffusion_timesteps
    __UpperCAmelCase         =							(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__     (		_a		,						_a       ):



  """simple docstring"""






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






  @register_to_config
  def __init__(					self:      Union[str, Any]					,					__lowerCAmelCase:      List[Any] = 1_000					,					__lowerCAmelCase:      Tuple = 0.00085					,					__lowerCAmelCase:      str = 0.012					,					__lowerCAmelCase:      Optional[int] = "linear"					,					__lowerCAmelCase:      Dict = None					,					__lowerCAmelCase:      List[str] = "epsilon"					,					__lowerCAmelCase:      Optional[Any] = False					,					__lowerCAmelCase:      Union[str, Any] = False					,					__lowerCAmelCase:      Any = 1.0					,					__lowerCAmelCase:      List[Any] = "linspace"					,					__lowerCAmelCase:      Any = 0					,					)			->			Tuple:




    '''simple docstring'''




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

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

    #  set all values
    self.set_timesteps(__lowerCAmelCase					,					__lowerCAmelCase					,					__lowerCAmelCase )
    __UpperCAmelCase         =							use_karras_sigmas






  def 						_UpperCAmelCase					(					self:      Union[str, Any]					,					__lowerCAmelCase:      List[str]					,					__lowerCAmelCase:      Union[str, Any]=None )			->			List[Any]:




    '''simple docstring'''




    if schedule_timesteps is None:
      __UpperCAmelCase         =							self.timesteps

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

    return indices[pos].item()






  @property
  def 						_UpperCAmelCase					(					self:      List[Any] )			->			str:




    '''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:      List[str]					,					__lowerCAmelCase:      Optional[int]					,					__lowerCAmelCase:      Tuple					,					)			->			Tuple:




    '''simple docstring'''




    __UpperCAmelCase         =							self.index_for_timestep(__lowerCAmelCase )

    __UpperCAmelCase         =							self.sigmas[step_index]
    __UpperCAmelCase         =							sample / ((sigma**2 + 1) ** 0.5)
    return sample






  def 						_UpperCAmelCase					(					self:      Optional[Any]					,					__lowerCAmelCase:      List[Any]					,					__lowerCAmelCase:      List[Any] = None					,					__lowerCAmelCase:      int = None					,					)			->			str:




    '''simple docstring'''




    __UpperCAmelCase         =							num_inference_steps

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

    __UpperCAmelCase         =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
    __UpperCAmelCase         =							np.log(__lowerCAmelCase )
    __UpperCAmelCase         =							np.interp(__lowerCAmelCase					,					np.arange(0					,					len(__lowerCAmelCase ) )					,					__lowerCAmelCase )

    if self.config.use_karras_sigmas:
      __UpperCAmelCase         =							self._convert_to_karras(in_sigmas=__lowerCAmelCase					,					num_inference_steps=self.num_inference_steps )
      __UpperCAmelCase         =							np.array([self._sigma_to_t(__lowerCAmelCase					,					__lowerCAmelCase ) for sigma in sigmas] )

    __UpperCAmelCase         =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
    __UpperCAmelCase         =							torch.from_numpy(__lowerCAmelCase ).to(device=__lowerCAmelCase )
    __UpperCAmelCase         =							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

    __UpperCAmelCase         =							torch.from_numpy(__lowerCAmelCase )
    __UpperCAmelCase         =							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

    if str(__lowerCAmelCase ).startswith("mps" ):
      # mps does not support float64
      __UpperCAmelCase         =							timesteps.to(__lowerCAmelCase					,					dtype=torch.floataa )
    else:
      __UpperCAmelCase         =							timesteps.to(device=__lowerCAmelCase )

    # empty dt and derivative
    __UpperCAmelCase         =							None
    __UpperCAmelCase         =							None

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






  def 						_UpperCAmelCase					(					self:      Optional[int]					,					__lowerCAmelCase:      Union[str, Any]					,					__lowerCAmelCase:      Union[str, Any] )			->			List[Any]:




    '''simple docstring'''




    __UpperCAmelCase         =							np.log(__lowerCAmelCase )

    # get distribution
    __UpperCAmelCase         =							log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    __UpperCAmelCase         =							np.cumsum((dists >= 0)					,					axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
    __UpperCAmelCase         =							low_idx + 1

    __UpperCAmelCase         =							log_sigmas[low_idx]
    __UpperCAmelCase         =							log_sigmas[high_idx]

    # interpolate sigmas
    __UpperCAmelCase         =							(low - log_sigma) / (low - high)
    __UpperCAmelCase         =							np.clip(__lowerCAmelCase					,					0					,					1 )

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






  def 						_UpperCAmelCase					(					self:      List[str]					,					__lowerCAmelCase:      Optional[int]					,					__lowerCAmelCase:      List[Any] )			->			int:




    '''simple docstring'''




    __UpperCAmelCase         =							in_sigmas[-1].item()
    __UpperCAmelCase         =							in_sigmas[0].item()

    __UpperCAmelCase         =							7.0  # 7.0 is the value used in the paper
    __UpperCAmelCase         =							np.linspace(0					,					1					,					__lowerCAmelCase )
    __UpperCAmelCase         =							sigma_min ** (1 / rho)
    __UpperCAmelCase         =							sigma_max ** (1 / rho)
    __UpperCAmelCase         =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas






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




    '''simple docstring'''




    return self.dt is None






  def 						_UpperCAmelCase					(					self:      List[Any]					,					__lowerCAmelCase:      int					,					__lowerCAmelCase:      List[Any]					,					__lowerCAmelCase:      Optional[int]					,					__lowerCAmelCase:      Tuple = True					,					)			->			Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase         =							self.index_for_timestep(__lowerCAmelCase )

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

    if self.state_in_first_order:
      __UpperCAmelCase         =							self.sigmas[step_index]
      __UpperCAmelCase         =							self.sigmas[step_index + 1]
    else:
      # 2nd order / Heun's method
      __UpperCAmelCase         =							self.sigmas[step_index - 1]
      __UpperCAmelCase         =							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
    __UpperCAmelCase         =							0
    __UpperCAmelCase         =							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":
      __UpperCAmelCase         =							sigma_hat if self.state_in_first_order else sigma_next
      __UpperCAmelCase         =							sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
      __UpperCAmelCase         =							sigma_hat if self.state_in_first_order else sigma_next
      __UpperCAmelCase         =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
          sample / (sigma_input**2 + 1)
      )
    elif self.config.prediction_type == "sample":
      __UpperCAmelCase         =							model_output
    else:
      raise ValueError(
          F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

    if self.config.clip_sample:
      __UpperCAmelCase         =							pred_original_sample.clamp(
          -self.config.clip_sample_range					,					self.config.clip_sample_range )

    if self.state_in_first_order:
      # 2. Convert to an ODE derivative for 1st order
      __UpperCAmelCase         =							(sample - pred_original_sample) / sigma_hat
      # 3. delta timestep
      __UpperCAmelCase         =							sigma_next - sigma_hat

      # store for 2nd order step
      __UpperCAmelCase         =							derivative
      __UpperCAmelCase         =							dt
      __UpperCAmelCase         =							sample
    else:
      # 2. 2nd order / Heun's method
      __UpperCAmelCase         =							(sample - pred_original_sample) / sigma_next
      __UpperCAmelCase         =							(self.prev_derivative + derivative) / 2

      # 3. take prev timestep & sample
      __UpperCAmelCase         =							self.dt
      __UpperCAmelCase         =							self.sample

      # free dt and derivative
      # Note, this puts the scheduler in "first order mode"
      __UpperCAmelCase         =							None
      __UpperCAmelCase         =							None
      __UpperCAmelCase         =							None

    __UpperCAmelCase         =							sample + derivative * dt

    if not return_dict:
      return (prev_sample,)

    return SchedulerOutput(prev_sample=__lowerCAmelCase )






  def 						_UpperCAmelCase					(					self:      List[str]					,					__lowerCAmelCase:      List[Any]					,					__lowerCAmelCase:      Union[str, Any]					,					__lowerCAmelCase:      Optional[int]					,					)			->			Optional[Any]:




    '''simple docstring'''




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

    __UpperCAmelCase         =							[self.index_for_timestep(__lowerCAmelCase					,					__lowerCAmelCase ) for t in timesteps]

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

    __UpperCAmelCase         =							original_samples + noise * sigma
    return noisy_samples






  def __len__(					self:      Any )			->			Dict:




    '''simple docstring'''




    return self.config.num_train_timesteps










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

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


if is_sentencepiece_available():
					from .tokenization_big_bird import BigBirdTokenizer
else:
					UpperCamelCase__           =			None

UpperCamelCase__           =			logging.get_logger(__name__)
UpperCamelCase__           =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__           =			{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__           =			{
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


UpperCamelCase__           =			"▁"

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							VOCAB_FILES_NAMES
							snake_case  :   List[str]      =							PRETRAINED_VOCAB_FILES_MAP
							snake_case  :   List[str]      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case  :   int      =							BigBirdTokenizer
							snake_case  :   List[Any]      =							["""input_ids""", """attention_mask"""]
							snake_case  :   List[int]      =							[]






							def __init__(    self	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase="<unk>"	,			__lowerCAmelCase="<s>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="<pad>"	,			__lowerCAmelCase="[SEP]"	,			__lowerCAmelCase="[MASK]"	,			__lowerCAmelCase="[CLS]"	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else bos_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else eos_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else unk_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else pad_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else cls_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else sep_token

														# Mask token behave like a normal word, i.e. include the space before it
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else mask_token

														super().__init__(
														    __lowerCAmelCase	,			tokenizer_file=__lowerCAmelCase	,			bos_token=__lowerCAmelCase	,			eos_token=__lowerCAmelCase	,			unk_token=__lowerCAmelCase	,			sep_token=__lowerCAmelCase	,			pad_token=__lowerCAmelCase	,			cls_token=__lowerCAmelCase	,			mask_token=__lowerCAmelCase	,			**__lowerCAmelCase	,			)

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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [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 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = False	):

														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 None:
																					return [1] + ([0] * len(__lowerCAmelCase	)) + [1]
														return [1] + ([0] * len(__lowerCAmelCase	)) + [1] + ([0] * len(__lowerCAmelCase	)) + [1]






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [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 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														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
														UpperCamelCase__					  =      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,)




