



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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

A_		:  Union[str, Any]										=  logging.get_logger(__name__)

A_		:  Dict										=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

A_		:  Optional[Any]										=  {
    '''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'''
        ),
    },
}

A_		:  int										=  {
    '''microsoft/deberta-base''': 5_12,
    '''microsoft/deberta-large''': 5_12,
    '''microsoft/deberta-xlarge''': 5_12,
    '''microsoft/deberta-base-mnli''': 5_12,
    '''microsoft/deberta-large-mnli''': 5_12,
    '''microsoft/deberta-xlarge-mnli''': 5_12,
}

A_		:  List[str]										=  {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		(
     list(range(ord("!"       )			,       ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,       ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,       ord("ÿ"       ) + 1       )       )
 )
 a							=		bs[:]
 a							=		0
 for b in range(2**8       ):
  if b not in bs:
   bs.append(UpperCAmelCase__       )
   cs.append(2**8 + n       )
   n += 1
 a							=		[chr(UpperCAmelCase__       ) for n in cs]
 return dict(zip(UpperCAmelCase__			,       UpperCAmelCase__       )       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]       ):






 '''simple docstring'''

 a							=		set()
 a							=		word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)       )
  a							=		char
 return pairs




class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      VOCAB_FILES_NAMES
   _UpperCAmelCase  						=      PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase  						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase  						=      ['''input_ids''', '''attention_mask''', '''token_type_ids''']

   def __init__(   self      :							str				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							List[Any]="replace"				, __lowerCAmelCase      :							int="[CLS]"				, __lowerCAmelCase      :							List[str]="[SEP]"				, __lowerCAmelCase      :							Any="[SEP]"				, __lowerCAmelCase      :							Optional[Any]="[CLS]"				, __lowerCAmelCase      :							Optional[Any]="[UNK]"				, __lowerCAmelCase      :							Tuple="[PAD]"				, __lowerCAmelCase      :							List[str]="[MASK]"				, __lowerCAmelCase      :							Optional[Any]=False				, __lowerCAmelCase      :							Optional[int]=False				, **__lowerCAmelCase      :							Any				, )							->					Dict:


    """simple docstring"""






    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else bos_token
    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else eos_token
    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else sep_token
    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else cls_token
    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else unk_token
    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else pad_token

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

    super().__init__(
        errors=__lowerCAmelCase				, bos_token=__lowerCAmelCase				, eos_token=__lowerCAmelCase				, unk_token=__lowerCAmelCase				, sep_token=__lowerCAmelCase				, cls_token=__lowerCAmelCase				, pad_token=__lowerCAmelCase				, mask_token=__lowerCAmelCase				, add_prefix_space=__lowerCAmelCase				, add_bos_token=__lowerCAmelCase				, **__lowerCAmelCase				, )
    a							=		add_bos_token

    with open(__lowerCAmelCase				, encoding="utf-8"				) as vocab_handle:
     a							=		json.load(__lowerCAmelCase				)
    a							=		{v: k for k, v in self.encoder.items()}
    a							=		errors  # how to handle errors in decoding
    a							=		bytes_to_unicode()
    a							=		{v: k for k, v in self.byte_encoder.items()}
    with open(__lowerCAmelCase				, encoding="utf-8"				) as merges_handle:
     a							=		merges_handle.read().split("\n"				)[1:-1]
    a							=		[tuple(merge.split()				) for merge in bpe_merges]
    a							=		dict(zip(__lowerCAmelCase				, range(len(__lowerCAmelCase				)				)				)				)
    a							=		{}
    a							=		add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    a							=		re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"				)

   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def 		A				(   self      :							List[Any]				)							->					int:


    """simple docstring"""






    return len(self.encoder				)

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return dict(self.encoder				, **self.added_tokens_encoder				)

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


    """simple docstring"""






    if token in self.cache:
     return self.cache[token]
    a							=		tuple(__lowerCAmelCase				)
    a							=		get_pairs(__lowerCAmelCase				)

    if not pairs:
     return token

    while True:
     a							=		min(__lowerCAmelCase				, key=lambda __lowerCAmelCase				: self.bpe_ranks.get(__lowerCAmelCase				, float("inf"				)				)				)
     if bigram not in self.bpe_ranks:
      break
     a		,	a							=		bigram
     a							=		[]
     a							=		0
     while i < len(__lowerCAmelCase				):
      try:
       a							=		word.index(__lowerCAmelCase				, __lowerCAmelCase				)
      except ValueError:
       new_word.extend(word[i:]				)
       break
      else:
       new_word.extend(word[i:j]				)
       a							=		j

      if word[i] == first and i < len(__lowerCAmelCase				) - 1 and word[i + 1] == second:
       new_word.append(first + second				)
       i += 2
      else:
       new_word.append(word[i]				)
       i += 1
     a							=		tuple(__lowerCAmelCase				)
     a							=		new_word
     if len(__lowerCAmelCase				) == 1:
      break
     else:
      a							=		get_pairs(__lowerCAmelCase				)
    a							=		" ".join(__lowerCAmelCase				)
    a							=		word
    return word

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


    """simple docstring"""






    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    a							=		[self.cls_token_id]
    a							=		[self.sep_token_id]
    return cls + token_ids_a + sep + token_ids_a + sep

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[int]				, __lowerCAmelCase      :							Optional[List[int]] = None				, __lowerCAmelCase      :							bool = False				)							->					List[int]:


    """simple docstring"""






    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=__lowerCAmelCase				, token_ids_a=__lowerCAmelCase				, already_has_special_tokens=__lowerCAmelCase				)

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

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


    """simple docstring"""






    a							=		[self.sep_token_id]
    a							=		[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 		A				(   self      :							List[str]				, __lowerCAmelCase      :							Tuple				)							->					List[Any]:


    """simple docstring"""






    a							=		[]
    for token in re.findall(self.pat				, __lowerCAmelCase				):
     a							=		"".join(
         self.byte_encoder[b] for b in token.encode("utf-8"				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCAmelCase				).split(" "				)				)
    return bpe_tokens

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Optional[int]				)							->					Optional[int]:


    """simple docstring"""






    return self.encoder.get(__lowerCAmelCase				, self.encoder.get(self.unk_token				)				)

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Union[str, Any]				)							->					Union[str, Any]:


    """simple docstring"""






    return self.decoder.get(__lowerCAmelCase				)

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							List[Any]				)							->					str:


    """simple docstring"""






    a							=		"".join(__lowerCAmelCase				)
    a							=		bytearray([self.byte_decoder[c] for c in text]				).decode("utf-8"				, errors=self.errors				)
    return text

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[str] = None				)							->					Tuple[str]:


    """simple docstring"""






    if not os.path.isdir(__lowerCAmelCase				):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
     return
    a							=		os.path.join(
        __lowerCAmelCase				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
    a							=		os.path.join(
        __lowerCAmelCase				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]				)

    with open(__lowerCAmelCase				, "w"				, encoding="utf-8"				) as f:
     f.write(json.dumps(self.encoder				, indent=2				, sort_keys=__lowerCAmelCase				, ensure_ascii=__lowerCAmelCase				) + "\n"				)

    a							=		0
    with open(__lowerCAmelCase				, "w"				, encoding="utf-8"				) as writer:
     writer.write("#version: 0.2\n"				)
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda __lowerCAmelCase				: kv[1]				):
      if index != token_index:
       logger.warning(
           f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
           " Please check that the tokenizer is not corrupted!"				)
       a							=		token_index
      writer.write(" ".join(__lowerCAmelCase				) + "\n"				)
      index += 1

    return vocab_file, merge_file

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							List[Any]=False				, **__lowerCAmelCase      :							Union[str, Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		kwargs.pop("add_prefix_space"				, self.add_prefix_space				)
    if (is_split_into_words or add_prefix_space) and (len(__lowerCAmelCase				) > 0 and not text[0].isspace()):
     a							=		" " + text
    return (text, kwargs)




   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							"Conversation"				)							->					List[int]:


    """simple docstring"""






    a							=		[]
    for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				) + [self.eos_token_id]				)
    if len(__lowerCAmelCase				) > self.model_max_length:
     a							=		input_ids[-self.model_max_length :]
    return input_ids








"""simple docstring"""


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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase=0.999						,  _UpperCAmelCase="cosine"						,  )     ->	str:

 '''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}"						)

 snake_case__     				=					[]
 for i in range(_UpperCAmelCase						):
  snake_case__     				=					i / num_diffusion_timesteps
  snake_case__     				=					(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__					:				Tuple									=  [e.name for e in KarrasDiffusionSchedulers]
       snake_case__					:				Any									=  2

       @register_to_config
       def __init__(      self ,       __SCREAMING_SNAKE_CASE = 1000 ,       __SCREAMING_SNAKE_CASE = 0.00085 ,       __SCREAMING_SNAKE_CASE = 0.012 ,       __SCREAMING_SNAKE_CASE = "linear" ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "epsilon" ,       __SCREAMING_SNAKE_CASE = "linspace" ,       __SCREAMING_SNAKE_CASE = 0 ,       ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__     				=					torch.tensor(__SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        elif beta_schedule == "linear":
         snake_case__     				=					torch.linspace(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       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 ,       __SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__     				=					betas_for_alpha_bar(__SCREAMING_SNAKE_CASE  )
        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  )

        #  set all values
        self.set_timesteps(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        if schedule_timesteps is None:
         snake_case__     				=					self.timesteps

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

        return indices[pos].item()

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

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

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					self.index_for_timestep(__SCREAMING_SNAKE_CASE  )

        if self.state_in_first_order:
         snake_case__     				=					self.sigmas[step_index]
        else:
         snake_case__     				=					self.sigmas_interpol[step_index - 1]

        snake_case__     				=					sample / ((sigma**2 + 1) ** 0.5)
        return sample

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       ):






        '''simple docstring'''

        snake_case__     				=					num_inference_steps

        snake_case__     				=					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":
         snake_case__     				=					np.linspace(0 ,       num_train_timesteps - 1 ,       __SCREAMING_SNAKE_CASE ,       dtype=__SCREAMING_SNAKE_CASE  )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
         snake_case__     				=					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
         snake_case__     				=					(np.arange(0 ,       __SCREAMING_SNAKE_CASE  ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE  )
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         snake_case__     				=					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
         snake_case__     				=					(np.arange(__SCREAMING_SNAKE_CASE ,       0 ,       -step_ratio  )).round().copy().astype(__SCREAMING_SNAKE_CASE  )
         timesteps -= 1
        else:
         raise ValueError(
             F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."  )

        snake_case__     				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case__     				=					torch.from_numpy(np.log(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					np.interp(__SCREAMING_SNAKE_CASE ,       np.arange(0 ,       len(__SCREAMING_SNAKE_CASE  )  ) ,       __SCREAMING_SNAKE_CASE  )
        snake_case__     				=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(device=__SCREAMING_SNAKE_CASE  )

        # compute up and down sigmas
        snake_case__     				=					sigmas.roll(-1  )
        snake_case__     				=					0.0
        snake_case__     				=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
        snake_case__     				=					(sigmas_next**2 - sigmas_up**2) ** 0.5
        snake_case__     				=					0.0

        # compute interpolated sigmas
        snake_case__     				=					sigmas.log().lerp(sigmas_down.log() ,       0.5  ).exp()
        snake_case__     				=					0.0

        # set sigmas
        snake_case__     				=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
        snake_case__     				=					torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )
        snake_case__     				=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2  ), sigmas_up[-1:]]  )
        snake_case__     				=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2  ), sigmas_down[-1:]]  )

        if str(__SCREAMING_SNAKE_CASE  ).startswith("mps"  ):
         # mps does not support float64
         snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE ,       dtype=torch.floataa  )
        else:
         snake_case__     				=					torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					self.sigma_to_t(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE ,       dtype=timesteps.dtype  )
        snake_case__     				=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,       dim=-1  ).flatten()

        snake_case__     				=					torch.cat([timesteps[:1], interleaved_timesteps]  )

        snake_case__     				=					None

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

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					sigma.log()

        # get distribution
        snake_case__     				=					log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        snake_case__     				=					dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
        snake_case__     				=					low_idx + 1

        snake_case__     				=					self.log_sigmas[low_idx]
        snake_case__     				=					self.log_sigmas[high_idx]

        # interpolate sigmas
        snake_case__     				=					(low - log_sigma) / (low - high)
        snake_case__     				=					w.clamp(0 ,       1  )

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

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return self.sample is None

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = True ,       ):






        '''simple docstring'''

        snake_case__     				=					self.index_for_timestep(__SCREAMING_SNAKE_CASE  )

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

        if self.state_in_first_order:
         snake_case__     				=					self.sigmas[step_index]
         snake_case__     				=					self.sigmas_interpol[step_index]
         snake_case__     				=					self.sigmas_up[step_index]
         snake_case__     				=					self.sigmas_down[step_index - 1]
        else:
         # 2nd order / KPDM2's method
         snake_case__     				=					self.sigmas[step_index - 1]
         snake_case__     				=					self.sigmas_interpol[step_index - 1]
         snake_case__     				=					self.sigmas_up[step_index - 1]
         snake_case__     				=					self.sigmas_down[step_index - 1]

        # 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
        snake_case__     				=					0
        snake_case__     				=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        snake_case__     				=					model_output.device
        snake_case__     				=					randn_tensor(model_output.shape ,       dtype=model_output.dtype ,       device=__SCREAMING_SNAKE_CASE ,       generator=__SCREAMING_SNAKE_CASE  )

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         snake_case__     				=					sigma_hat if self.state_in_first_order else sigma_interpol
         snake_case__     				=					sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
         snake_case__     				=					sigma_hat if self.state_in_first_order else sigma_interpol
         snake_case__     				=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**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`"  )

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

         # store for 2nd order step
         snake_case__     				=					sample
         snake_case__     				=					dt
         snake_case__     				=					sample + derivative * dt
        else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         snake_case__     				=					(sample - pred_original_sample) / sigma_interpol
         # 3. delta timestep
         snake_case__     				=					sigma_down - sigma_hat

         snake_case__     				=					self.sample
         snake_case__     				=					None

         snake_case__     				=					sample + derivative * dt
         snake_case__     				=					prev_sample + noise * sigma_up

        if not return_dict:
         return (prev_sample,)

        return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					self.sigmas.to(device=original_samples.device ,       dtype=original_samples.dtype  )
        if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE  ):
         # mps does not support float64
         snake_case__     				=					self.timesteps.to(original_samples.device ,       dtype=torch.floataa  )
         snake_case__     				=					timesteps.to(original_samples.device ,       dtype=torch.floataa  )
        else:
         snake_case__     				=					self.timesteps.to(original_samples.device  )
         snake_case__     				=					timesteps.to(original_samples.device  )

        snake_case__     				=					[self.index_for_timestep(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) for t in timesteps]

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

        snake_case__     				=					original_samples + noise * sigma
        return noisy_samples

       def __len__(      self  ):






        '''simple docstring'''

        return self.config.num_train_timesteps

