





"""simple docstring"""


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

import numpy as np
import torch

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



@dataclass
class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:torch.FloatTensor
  SCREAMING_SNAKE_CASE__							:torch.FloatTensor





def     lowercase__			(    lowercase_						,lowercase_=0.999						,lowercase_="cosine"						,)    ->						Optional[int]:




      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

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



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    1

  @register_to_config
  def __init__(						self      :				Optional[Any]    ,      __a      :				int = 1000    ,      __a      :				float = 0.00_01    ,      __a      :				float = 0.02    ,      __a      :				str = "linear"    ,      __a      :				float = 0.0    ,      __a      :				Optional[np.ndarray] = None    ,      __a      :				bool = True    ,      )						-> Optional[Any]:
        if trained_betas is not None:
              _UpperCamelCase					:		Tuple  =				torch.from_numpy(__a		)
        elif beta_schedule == "linear":
              _UpperCamelCase					:		Any  =				torch.linspace(__a    ,      __a    ,      __a    ,      dtype=torch.floataa		)
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _UpperCamelCase					:		str  =				(
                  torch.linspace(beta_start**0.5    ,      beta_end**0.5    ,      __a    ,      dtype=torch.floataa		) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _UpperCamelCase					:		Dict  =				betas_for_alpha_bar(__a		)
        elif beta_schedule == "sigmoid":
              # GeoDiff sigmoid schedule
              _UpperCamelCase					:		Any  =				torch.linspace(-6    ,      6    ,      __a		)
              _UpperCamelCase					:		List[Any]  =				torch.sigmoid(__a		) * (beta_end - beta_start) + beta_start
        else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

        _UpperCamelCase					:		List[Any]  =				1.0 - self.betas
        _UpperCamelCase					:		Dict  =				torch.cumprod(self.alphas    ,      dim=0		)
        _UpperCamelCase					:		List[Any]  =				torch.tensor(1.0		)

        _UpperCamelCase					:		List[Any]  =				torch.tensor(1.0		)

        # standard deviation of the initial noise distribution
        _UpperCamelCase					:		Optional[int]  =				1.0

        # setable values
        _UpperCamelCase					:		Dict  =				None
        _UpperCamelCase					:		List[str]  =				torch.from_numpy(np.arange(0    ,      __a		)[::-1].copy()		)

        _UpperCamelCase					:		str  =				eta

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				torch.FloatTensor    ,      __a      :				Optional[int] = None		)						-> torch.FloatTensor:
        return sample

  def       __SCREAMING_SNAKE_CASE (						self      :				int    ,      __a      :				int    ,      __a      :				int = 10    ,      __a      :				int = 10    ,      __a      :				Union[str, torch.device] = None    ,      )						-> Union[str, Any]:
        _UpperCamelCase					:		Dict  =				min(self.config.num_train_timesteps    ,      __a		)
        _UpperCamelCase					:		List[Any]  =				num_inference_steps

        _UpperCamelCase					:		Optional[Any]  =				[]

        _UpperCamelCase					:		List[str]  =				{}
        for j in range(0    ,      num_inference_steps - jump_length    ,      __a		):
              _UpperCamelCase					:		Optional[Any]  =				jump_n_sample - 1

        _UpperCamelCase					:		Union[str, Any]  =				num_inference_steps
        while t >= 1:
              _UpperCamelCase					:		List[str]  =				t - 1
              timesteps.append(__a		)

              if jumps.get(__a    ,      0		) > 0:
                    _UpperCamelCase					:		Tuple  =				jumps[t] - 1
                    for _ in range(__a		):
                          _UpperCamelCase					:		str  =				t + 1
                          timesteps.append(__a		)

        _UpperCamelCase					:		Optional[Any]  =				np.array(__a		) * (self.config.num_train_timesteps // self.num_inference_steps)
        _UpperCamelCase					:		Tuple  =				torch.from_numpy(__a		).to(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				Dict		)						-> str:
        _UpperCamelCase					:		List[str]  =				t - self.config.num_train_timesteps // self.num_inference_steps

        _UpperCamelCase					:		Dict  =				self.alphas_cumprod[t]
        _UpperCamelCase					:		Optional[int]  =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        _UpperCamelCase					:		Any  =				1 - alpha_prod_t
        _UpperCamelCase					:		Tuple  =				1 - alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from
        # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
        # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
        # variance to pred_sample
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        _UpperCamelCase					:		List[Any]  =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				torch.FloatTensor    ,      __a      :				int    ,      __a      :				torch.FloatTensor    ,      __a      :				torch.FloatTensor    ,      __a      :				torch.FloatTensor    ,      __a      :				Optional[torch.Generator] = None    ,      __a      :				bool = True    ,      )						-> Union[RePaintSchedulerOutput, Tuple]:
        _UpperCamelCase					:		Union[str, Any]  =				timestep
        _UpperCamelCase					:		str  =				timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        _UpperCamelCase					:		Any  =				self.alphas_cumprod[t]
        _UpperCamelCase					:		List[Any]  =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        _UpperCamelCase					:		Optional[int]  =				1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        _UpperCamelCase					:		int  =				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
              _UpperCamelCase					:		List[str]  =				torch.clamp(__a    ,      -1    ,      1		)

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        _UpperCamelCase					:		str  =				model_output.device
        _UpperCamelCase					:		Dict  =				randn_tensor(model_output.shape    ,      generator=__a    ,      device=__a    ,      dtype=model_output.dtype		)
        _UpperCamelCase					:		Dict  =				self.eta * self._get_variance(__a		) ** 0.5

        _UpperCamelCase					:		Any  =				0
        if t > 0 and self.eta > 0:
              _UpperCamelCase					:		Any  =				std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        _UpperCamelCase					:		int  =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        _UpperCamelCase					:		List[Any]  =				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        _UpperCamelCase					:		int  =				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        _UpperCamelCase					:		Optional[Any]  =				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
              return (
                  pred_prev_sample,
                  pred_original_sample,
              )

        return RePaintSchedulerOutput(prev_sample=__a    ,      pred_original_sample=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				Tuple    ,      __a      :				List[str]    ,      __a      :				Dict=None		)						-> Tuple:
        _UpperCamelCase					:		int  =				self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__a		):
              _UpperCamelCase					:		Any  =				self.betas[timestep + i]
              if sample.device.type == "mps":
                    # randn does not work reproducibly on mps
                    _UpperCamelCase					:		List[Any]  =				randn_tensor(sample.shape    ,      dtype=sample.dtype    ,      generator=__a		)
                    _UpperCamelCase					:		Union[str, Any]  =				noise.to(sample.device		)
              else:
                    _UpperCamelCase					:		List[Any]  =				randn_tensor(sample.shape    ,      generator=__a    ,      device=sample.device    ,      dtype=sample.dtype		)

              # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
              _UpperCamelCase					:		List[Any]  =				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				torch.FloatTensor    ,      __a      :				torch.FloatTensor    ,      __a      :				torch.IntTensor    ,      )						-> torch.FloatTensor:
        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)


  def __len__(						self      :				int		)						-> Optional[Any]:
        return self.config.num_train_timesteps







"""simple docstring"""


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_4288,
}



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:Any		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:str		   =    ReformerTokenizer

  def __init__(						self      :				List[str]    ,      __a      :				str=None    ,      __a      :				List[Any]=None    ,      __a      :				Optional[int]="</s>"    ,      __a      :				str="<unk>"    ,      __a      :				Dict=[]    ,      **__a      :				Tuple    ,      )						-> Optional[int]:
        super().__init__(
            __a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      unk_token=__a    ,      additional_special_tokens=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Dict  =				vocab_file
        _UpperCamelCase					:		Optional[Any]  =				False if not self.vocab_file else True


  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        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(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		Optional[Any]  =				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,)
