

"""simple docstring"""




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

import numpy as np
import torch

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


__UpperCAmelCase	=			logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    4_2
  snake_case_			    =    None







def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase=0.999							,      __UpperCamelCase="cosine"							,      ):
 '''simple docstring'''





 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

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







class        __lowercase     (      __lowerCamelCase		,     __lowerCamelCase   ):
  snake_case_			    =    [e.name for e in KarrasDiffusionSchedulers]
  snake_case_			    =    1

  @register_to_config
  def __init__(					self						:     Tuple   ,A						:     int = 1_000   ,A						:     float = 0.0_0_0_1   ,A						:     float = 0.0_2   ,A						:     str = "linear"   ,A						:     Optional[Union[np.ndarray, List[float]]] = None   ,A						:     str = "epsilon"   ,A						:     str = "linspace"   ,A						:     int = 0   ,):






   '''simple docstring'''
   if trained_betas is not None:
    UpperCAmelCase__   :					int   				=   torch.tensor(A   ,dtype=torch.floataa      )
   elif beta_schedule == "linear":
    UpperCAmelCase__   :					Optional[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__   :					Union[str, Any]   				=   (
        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      )
   else:
    raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

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

   UpperCAmelCase__   :					Optional[Any]   				=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
   UpperCAmelCase__   :					Union[str, Any]   				=   np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
   UpperCAmelCase__   :					List[str]   				=   torch.from_numpy(A      )

   # setable values
   UpperCAmelCase__   :					List[Any]   				=   None
   UpperCAmelCase__   :					Union[str, Any]   				=   np.linspace(0   ,num_train_timesteps - 1   ,A   ,dtype=A      )[::-1].copy()
   UpperCAmelCase__   :					List[Any]   				=   torch.from_numpy(A      )
   UpperCAmelCase__   :					List[str]   				=   False

  @property
  def 				__lowercase    (					self						:     Optional[Any]      ):






   '''simple docstring'''
   # standard deviation of the initial noise distribution
   if self.config.timestep_spacing in ["linspace", "trailing"]:
    return self.sigmas.max()

   return (self.sigmas.max() ** 2 + 1) ** 0.5

  def 				__lowercase    (					self						:     Optional[Any]   ,A						:     torch.FloatTensor   ,A						:     Union[float, torch.FloatTensor]      ):






   '''simple docstring'''
   if isinstance(A   ,torch.Tensor      ):
    UpperCAmelCase__   :					Optional[int]   				=   timestep.to(self.timesteps.device      )
   UpperCAmelCase__   :					Optional[int]   				=   (self.timesteps == timestep).nonzero().item()
   UpperCAmelCase__   :					List[str]   				=   self.sigmas[step_index]
   UpperCAmelCase__   :					List[str]   				=   sample / ((sigma**2 + 1) ** 0.5)
   UpperCAmelCase__   :					Optional[int]   				=   True
   return sample

  def 				__lowercase    (					self						:     Optional[Any]   ,A						:     int   ,A						:     Union[str, torch.device] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   num_inference_steps

   # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
   if self.config.timestep_spacing == "linspace":
    UpperCAmelCase__   :					Optional[Any]   				=   np.linspace(0   ,self.config.num_train_timesteps - 1   ,A   ,dtype=A      )[
        ::-1
    ].copy()
   elif self.config.timestep_spacing == "leading":
    UpperCAmelCase__   :					Union[str, Any]   				=   self.config.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__   :					str   				=   (np.arange(0   ,A      ) * step_ratio).round()[::-1].copy().astype(A      )
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    UpperCAmelCase__   :					str   				=   self.config.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__   :					int   				=   (np.arange(self.config.num_train_timesteps   ,0   ,-step_ratio      )).round().copy().astype(A      )
    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__   :					Tuple   				=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
   UpperCAmelCase__   :					Dict   				=   np.interp(A   ,np.arange(0   ,len(A      )      )   ,A      )
   UpperCAmelCase__   :					Optional[Any]   				=   np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
   UpperCAmelCase__   :					Dict   				=   torch.from_numpy(A      ).to(device=A      )
   if str(A      ).startswith("""mps"""      ):
    # mps does not support float64
    UpperCAmelCase__   :					List[Any]   				=   torch.from_numpy(A      ).to(A   ,dtype=torch.floataa      )
   else:
    UpperCAmelCase__   :					Dict   				=   torch.from_numpy(A      ).to(device=A      )

  def 				__lowercase    (					self						:     str   ,A						:     torch.FloatTensor   ,A						:     Union[float, torch.FloatTensor]   ,A						:     torch.FloatTensor   ,A						:     Optional[torch.Generator] = None   ,A						:     bool = True   ,):






   '''simple docstring'''

   if (
       isinstance(A   ,A      )
       or isinstance(A   ,torch.IntTensor      )
       or isinstance(A   ,torch.LongTensor      )
   ):
    raise ValueError(
        (
            """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
            """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
            """ one of the `scheduler.timesteps` as a timestep."""
        )   ,)

   if not self.is_scale_input_called:
    logger.warning(
        """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
        """See `StableDiffusionPipeline` for a usage example."""      )

   if isinstance(A   ,torch.Tensor      ):
    UpperCAmelCase__   :					List[Any]   				=   timestep.to(self.timesteps.device      )

   UpperCAmelCase__   :					Tuple   				=   (self.timesteps == timestep).nonzero().item()
   UpperCAmelCase__   :					Any   				=   self.sigmas[step_index]

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
    UpperCAmelCase__   :					List[Any]   				=   sample - sigma * model_output
   elif self.config.prediction_type == "v_prediction":
    # * c_out + input * c_skip
    UpperCAmelCase__   :					Optional[Any]   				=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"      )

   UpperCAmelCase__   :					Any   				=   self.sigmas[step_index]
   UpperCAmelCase__   :					Any   				=   self.sigmas[step_index + 1]
   UpperCAmelCase__   :					Optional[int]   				=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
   UpperCAmelCase__   :					Any   				=   (sigma_to**2 - sigma_up**2) ** 0.5

   # 2. Convert to an ODE derivative
   UpperCAmelCase__   :					str   				=   (sample - pred_original_sample) / sigma

   UpperCAmelCase__   :					Dict   				=   sigma_down - sigma

   UpperCAmelCase__   :					Tuple   				=   sample + derivative * dt

   UpperCAmelCase__   :					Any   				=   model_output.device
   UpperCAmelCase__   :					List[Any]   				=   randn_tensor(model_output.shape   ,dtype=model_output.dtype   ,device=A   ,generator=A      )

   UpperCAmelCase__   :					Tuple   				=   prev_sample + noise * sigma_up

   if not return_dict:
    return (prev_sample,)

   return EulerAncestralDiscreteSchedulerOutput(
       prev_sample=A   ,pred_original_sample=A      )

  def 				__lowercase    (					self						:     str   ,A						:     torch.FloatTensor   ,A						:     torch.FloatTensor   ,A						:     torch.FloatTensor   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   self.sigmas.to(device=original_samples.device   ,dtype=original_samples.dtype      )
   if original_samples.device.type == "mps" and torch.is_floating_point(A      ):
    # mps does not support float64
    UpperCAmelCase__   :					Tuple   				=   self.timesteps.to(original_samples.device   ,dtype=torch.floataa      )
    UpperCAmelCase__   :					str   				=   timesteps.to(original_samples.device   ,dtype=torch.floataa      )
   else:
    UpperCAmelCase__   :					str   				=   self.timesteps.to(original_samples.device      )
    UpperCAmelCase__   :					Any   				=   timesteps.to(original_samples.device      )

   UpperCAmelCase__   :					List[Any]   				=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

   UpperCAmelCase__   :					List[Any]   				=   sigmas[step_indices].flatten()
   while len(sigma.shape      ) < len(original_samples.shape      ):
    UpperCAmelCase__   :					Union[str, Any]   				=   sigma.unsqueeze(-1      )

   UpperCAmelCase__   :					int   				=   original_samples + noise * sigma
   return noisy_samples




  def __len__(					self						:     Optional[int]      ):






   '''simple docstring'''
   return self.config.num_train_timesteps








"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	=			logging.get_logger(__name__)

__UpperCAmelCase	=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
  snake_case_			    =    DebertaTokenizer

  def __init__(					self						:     Optional[Any]   ,A						:     Any=None   ,A						:     Any=None   ,A						:     Dict=None   ,A						:     Optional[int]="replace"   ,A						:     Optional[int]="[CLS]"   ,A						:     str="[SEP]"   ,A						:     Optional[int]="[SEP]"   ,A						:     List[Any]="[CLS]"   ,A						:     Dict="[UNK]"   ,A						:     List[str]="[PAD]"   ,A						:     Optional[Any]="[MASK]"   ,A						:     str=False   ,**A						:     Dict   ,):






   '''simple docstring'''
   super().__init__(
       A   ,A   ,tokenizer_file=A   ,errors=A   ,bos_token=A   ,eos_token=A   ,unk_token=A   ,sep_token=A   ,cls_token=A   ,pad_token=A   ,mask_token=A   ,add_prefix_space=A   ,**A   ,)
   UpperCAmelCase__   :					Tuple   				=   kwargs.pop("""add_bos_token"""   ,A      )

   UpperCAmelCase__   :					Tuple   				=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
   if pre_tok_state.get("""add_prefix_space"""   ,A      ) != add_prefix_space:
    UpperCAmelCase__   :					List[Any]   				=   getattr(A   ,pre_tok_state.pop("""type"""      )      )
    UpperCAmelCase__   :					Any   				=   add_prefix_space
    UpperCAmelCase__   :					List[Any]   				=   pre_tok_class(**A      )

   UpperCAmelCase__   :					str   				=   add_prefix_space

  @property
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   if self._mask_token is None:
    if self.verbose:
     logger.error("""Using mask_token, but it is not set yet."""      )
    return None
   return str(self._mask_token      )

  @mask_token.setter
  def 				__lowercase    (					self						:     Dict   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   AddedToken(A   ,lstrip=A   ,rstrip=A      ) if isinstance(A   ,A      ) else value
   UpperCAmelCase__   :					List[str]   				=   value

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__   :					Dict   				=   [self.cls_token_id]
   UpperCAmelCase__   :					str   				=   [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   [self.sep_token_id]
   UpperCAmelCase__   :					str   				=   [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 				__lowercase    (					self						:     int   ,*A						:     Tuple   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   kwargs.get("""is_split_into_words"""   ,A      )
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     str   ,*A						:     List[str]   ,**A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   kwargs.get("""is_split_into_words"""   ,A      )

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     Tuple   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self._tokenizer.model.save(A   ,name=A      )
   return tuple(A      )




  def 				__lowercase    (					self						:     List[str]   ,A						:     "Conversation"      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   []
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(A   ,add_special_tokens=A      ) + [self.eos_token_id]      )

   if len(A      ) > self.model_max_length:
    UpperCAmelCase__   :					Optional[int]   				=   input_ids[-self.model_max_length :]
   return input_ids





