





"""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 lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					42
   __UpperCAmelCase        =					42




def 			__snake_case						(      __A ,__A=0.9_99 ,__A="cosine" ,)		->				Any:
   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    :			Union[str, Any]  =       []
   for i in range(__A    ):
      lowercase    :			Union[str, Any]  =       i / num_diffusion_timesteps
      lowercase    :			Optional[int]  =       (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 lowerCamelCase__  (    __UpperCamelCase  ,				__UpperCamelCase				):
   __UpperCAmelCase        =					1



   @register_to_config
   def __init__(       self      ,   snake_case = 1_0_0_0      ,   snake_case = 0.00_01      ,   snake_case = 0.02      ,   snake_case = "linear"      ,   snake_case = 0.0      ,   snake_case = None      ,   snake_case = True      ,   ) ->    int:







      """simple docstring"""
      if trained_betas is not None:
         lowercase    :			Dict  =       torch.from_numpy(snake_case	)
      elif beta_schedule == "linear":
         lowercase    :			List[Any]  =       torch.linspace(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.floataa	)
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         lowercase    :			Optional[int]  =       (
             torch.linspace(beta_start**0.5      ,   beta_end**0.5      ,   snake_case      ,   dtype=torch.floataa	) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         lowercase    :			int  =       betas_for_alpha_bar(snake_case	)
      elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         lowercase    :			Union[str, Any]  =       torch.linspace(-6      ,   6      ,   snake_case	)
         lowercase    :			int  =       torch.sigmoid(snake_case	) * (beta_end - beta_start) + beta_start
      else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''	)

      lowercase    :			str  =       1.0 - self.betas
      lowercase    :			Optional[int]  =       torch.cumprod(self.alphas      ,   dim=0	)
      lowercase    :			Optional[Any]  =       torch.tensor(1.0	)

      lowercase    :			List[str]  =       torch.tensor(1.0	)

      # standard deviation of the initial noise distribution
      lowercase    :			Any  =       1.0

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

      lowercase    :			Dict  =       eta



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = None	) ->    torch.FloatTensor:







      """simple docstring"""
      return sample



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = 1_0      ,   snake_case = 1_0      ,   snake_case = None      ,   ) ->    str:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       min(self.config.num_train_timesteps      ,   snake_case	)
      lowercase    :			List[str]  =       num_inference_steps

      lowercase    :			List[str]  =       []

      lowercase    :			Any  =       {}
      for j in range(0      ,   num_inference_steps - jump_length      ,   snake_case	):
         lowercase    :			Union[str, Any]  =       jump_n_sample - 1

      lowercase    :			Union[str, Any]  =       num_inference_steps
      while t >= 1:
         lowercase    :			Union[str, Any]  =       t - 1
         timesteps.append(snake_case	)

         if jumps.get(snake_case      ,   0	) > 0:
            lowercase    :			Optional[int]  =       jumps[t] - 1
            for _ in range(snake_case	):
               lowercase    :			Any  =       t + 1
               timesteps.append(snake_case	)

      lowercase    :			str  =       np.array(snake_case	) * (self.config.num_train_timesteps // self.num_inference_steps)
      lowercase    :			Dict  =       torch.from_numpy(snake_case	).to(snake_case	)



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Union[str, Any]:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       t - self.config.num_train_timesteps // self.num_inference_steps

      lowercase    :			Union[str, Any]  =       self.alphas_cumprod[t]
      lowercase    :			str  =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
      lowercase    :			Any  =       1 - alpha_prod_t
      lowercase    :			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]
      lowercase    :			Tuple  =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case = None      ,   snake_case = True      ,   ) ->    Union[RePaintSchedulerOutput, Tuple]:







      """simple docstring"""
      lowercase    :			Any  =       timestep
      lowercase    :			List[Any]  =       timestep - self.config.num_train_timesteps // self.num_inference_steps

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

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         lowercase    :			int  =       torch.clamp(snake_case      ,   -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
      lowercase    :			Any  =       model_output.device
      lowercase    :			List[str]  =       randn_tensor(model_output.shape      ,   generator=snake_case      ,   device=snake_case      ,   dtype=model_output.dtype	)
      lowercase    :			List[str]  =       self.eta * self._get_variance(snake_case	) ** 0.5

      lowercase    :			Optional[Any]  =       0
      if t > 0 and self.eta > 0:
         lowercase    :			Any  =       std_dev_t * noise

      # 6. compute "direction pointing to x_t" of formula (12)
      # from https://arxiv.org/pdf/2010.02502.pdf
      lowercase    :			Union[str, Any]  =       (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
      lowercase    :			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
      lowercase    :			Tuple  =       (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
      lowercase    :			List[str]  =       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=snake_case      ,   pred_original_sample=snake_case	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case=None	) ->    List[str]:







      """simple docstring"""
      lowercase    :			int  =       self.config.num_train_timesteps // self.num_inference_steps

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

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

      return sample



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   ) ->    torch.FloatTensor:







      """simple docstring"""
      raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""	)





   def __len__(       self	) ->    str:







      """simple docstring"""
      return self.config.num_train_timesteps









"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

         lowercase    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[Any]  =       features["""xpaths"""]
      else:
         if html_strings is not None:
            raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
         if nodes is None or xpaths is None:
            raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      lowercase    :			Dict  =       self.tokenizer(
          text=questions if questions is not None else nodes      ,   text_pair=nodes if questions is not None else None      ,   xpaths=snake_case      ,   node_labels=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_token_type_ids=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names


