

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  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      torch.FloatTensor
      lowerCamelCase_	:      torch.FloatTensor



def    UpperCamelCase(					lowercase_			,					lowercase_=0.9_99			,					lowercase_="cosine"			,					)	->				Optional[Any]:





  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase_							):
      return math.cos((t + 0.0_08) / 1.0_08 * 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}'''							)

  snake_case_       						=      []
  for i in range(lowercase_							):
    snake_case_       						=      i / num_diffusion_timesteps
    snake_case_       						=      (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  __lowerCamelCase				(	__snake_case  ,							__snake_case	):
      lowerCamelCase_	:      List[str]          =					1







      @register_to_config
      def __init__(     self							,  lowerCamelCase = 1000							,  lowerCamelCase = 0.0001							,  lowerCamelCase = 0.02							,  lowerCamelCase = "linear"							,  lowerCamelCase = 0.0							,  lowerCamelCase = None							,  lowerCamelCase = True							,  )						->       Union[str, Any]:
        if trained_betas is not None:
          snake_case_       						=      torch.from_numpy(lowerCamelCase   )
        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   )
        elif beta_schedule == "sigmoid":
          # GeoDiff sigmoid schedule
          snake_case_       						=      torch.linspace(-6							,  6							,  lowerCamelCase   )
          snake_case_       						=      torch.sigmoid(lowerCamelCase   ) * (beta_end - beta_start) + beta_start
        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_       						=      torch.tensor(1.0   )

        snake_case_       						=      torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case_       						=      1.0

        # setable values
        snake_case_       						=      None
        snake_case_       						=      torch.from_numpy(np.arange(0							,  lowerCamelCase   )[::-1].copy()   )

        snake_case_       						=      eta







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = None   )						->       torch.FloatTensor:
        return sample







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = 10							,  lowerCamelCase = 10							,  lowerCamelCase = None							,  )						->       Optional[Any]:
        snake_case_       						=      min(self.config.num_train_timesteps							,  lowerCamelCase   )
        snake_case_       						=      num_inference_steps

        snake_case_       						=      []

        snake_case_       						=      {}
        for j in range(0							,  num_inference_steps - jump_length							,  lowerCamelCase   ):
          snake_case_       						=      jump_n_sample - 1

        snake_case_       						=      num_inference_steps
        while t >= 1:
          snake_case_       						=      t - 1
          timesteps.append(lowerCamelCase   )

          if jumps.get(lowerCamelCase							,  0   ) > 0:
            snake_case_       						=      jumps[t] - 1
            for _ in range(lowerCamelCase   ):
              snake_case_       						=      t + 1
              timesteps.append(lowerCamelCase   )

        snake_case_       						=      np.array(lowerCamelCase   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case_       						=      torch.from_numpy(lowerCamelCase   ).to(lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       Tuple:
        snake_case_       						=      t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case_       						=      self.alphas_cumprod[t]
        snake_case_       						=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case_       						=      1 - alpha_prod_t
        snake_case_       						=      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]
        snake_case_       						=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase = None							,  lowerCamelCase = True							,  )						->       Union[RePaintSchedulerOutput, Tuple]:
        snake_case_       						=      timestep
        snake_case_       						=      timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case_       						=      self.alphas_cumprod[t]
        snake_case_       						=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case_       						=      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
        snake_case_       						=      (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
          snake_case_       						=      torch.clamp(lowerCamelCase							,  -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
        snake_case_       						=      model_output.device
        snake_case_       						=      randn_tensor(model_output.shape							,  generator=lowerCamelCase							,  device=lowerCamelCase							,  dtype=model_output.dtype   )
        snake_case_       						=      self.eta * self._get_variance(lowerCamelCase   ) ** 0.5

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

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case_       						=      (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
        snake_case_       						=      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
        snake_case_       						=      (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
        snake_case_       						=      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=lowerCamelCase							,  pred_original_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase=None   )						->       Any:
        snake_case_       						=      self.config.num_train_timesteps // self.num_inference_steps

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

          # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
          snake_case_       						=      (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample







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







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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					'rembert'







      def __init__(     self							,  lowerCamelCase=250300							,  lowerCamelCase=1152							,  lowerCamelCase=32							,  lowerCamelCase=18							,  lowerCamelCase=256							,  lowerCamelCase=1664							,  lowerCamelCase=4608							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.1							,  lowerCamelCase=512							,  lowerCamelCase=2							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-12							,  lowerCamelCase=True							,  lowerCamelCase=0							,  lowerCamelCase=312							,  lowerCamelCase=313							,  **lowerCamelCase							,  )						->       Union[str, Any]:
        super().__init__(pad_token_id=lowerCamelCase							,  bos_token_id=lowerCamelCase							,  eos_token_id=lowerCamelCase							,  **lowerCamelCase   )

        snake_case_       						=      vocab_size
        snake_case_       						=      input_embedding_size
        snake_case_       						=      output_embedding_size
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      classifier_dropout_prob
        snake_case_       						=      initializer_range
        snake_case_       						=      type_vocab_size
        snake_case_       						=      layer_norm_eps
        snake_case_       						=      use_cache
        snake_case_       						=      False







class  __lowerCamelCase				(	__snake_case	):







      @property
      def 	lowerCAmelCase_    (     self   )						->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          snake_case_       						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
          snake_case_       						=      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]   )







      @property
      def 	lowerCAmelCase_    (     self   )						->       float:
        return 1e-4