





"""simple docstring"""


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

a__       :  Optional[Any]       		=					logging.get_logger(__name__)

a__       :  Any       		=					{"""vocab_file""": """tokenizer.model"""}

a__       :  Tuple       		=					{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
a__       :  List[Any]       		=					{
    """hf-internal-testing/llama-tokenizer""": 2_0_4_8,
}
a__       :  Optional[Any]       		=					"""▁"""

a__		,			a__       :  List[Any]       		=					"""[INST]""", """[/INST]"""
a__		,			a__       :  str       		=					"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
a__       :  Optional[int]       		=					"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				List[Any]									=  VOCAB_FILES_NAMES
       snake_case__					:				List[Any]									=  PRETRAINED_VOCAB_FILES_MAP
       snake_case__					:				List[Any]									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__					:				Optional[int]									=  ["""input_ids""", """attention_mask"""]

       def __init__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE="<unk>" ,       __SCREAMING_SNAKE_CASE="<s>" ,       __SCREAMING_SNAKE_CASE="</s>" ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=False ,       __SCREAMING_SNAKE_CASE=False ,       __SCREAMING_SNAKE_CASE=True ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					{} if sp_model_kwargs is None else sp_model_kwargs
        snake_case__     				=					AddedToken(__SCREAMING_SNAKE_CASE ,       lstrip=__SCREAMING_SNAKE_CASE ,       rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) else bos_token
        snake_case__     				=					AddedToken(__SCREAMING_SNAKE_CASE ,       lstrip=__SCREAMING_SNAKE_CASE ,       rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) else eos_token
        snake_case__     				=					AddedToken(__SCREAMING_SNAKE_CASE ,       lstrip=__SCREAMING_SNAKE_CASE ,       rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) else unk_token
        snake_case__     				=					AddedToken(__SCREAMING_SNAKE_CASE ,       lstrip=__SCREAMING_SNAKE_CASE ,       rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ) else pad_token
        super().__init__(
            bos_token=__SCREAMING_SNAKE_CASE ,       eos_token=__SCREAMING_SNAKE_CASE ,       unk_token=__SCREAMING_SNAKE_CASE ,       pad_token=__SCREAMING_SNAKE_CASE ,       add_bos_token=__SCREAMING_SNAKE_CASE ,       add_eos_token=__SCREAMING_SNAKE_CASE ,       sp_model_kwargs=self.sp_model_kwargs ,       clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE ,       legacy=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE ,       )
        if legacy:
         logger.warning_once(
             F"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
             " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"  )
        snake_case__     				=					legacy
        snake_case__     				=					vocab_file
        snake_case__     				=					add_bos_token
        snake_case__     				=					add_eos_token
        snake_case__     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.Load(__SCREAMING_SNAKE_CASE  )

       def __getstate__(      self  ):






        '''simple docstring'''

        snake_case__     				=					self.__dict__.copy()
        snake_case__     				=					None
        snake_case__     				=					self.sp_model.serialized_model_proto()
        return state

       def __setstate__(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					d
        snake_case__     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )

       @property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return self.sp_model.get_piece_size()

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE  ): i for i in range(self.vocab_size  )}
        vocab.update(self.added_tokens_encoder  )
        return vocab

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






        '''simple docstring'''

        if not self.legacy:
         snake_case__     				=					SPIECE_UNDERLINE + text.replace(__SCREAMING_SNAKE_CASE ,       " "  )
        return super().tokenize(__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        if not self.legacy:
         snake_case__     				=					text.startswith(__SCREAMING_SNAKE_CASE  )
         if is_first:
          snake_case__     				=					text[1:]

        snake_case__     				=					self.sp_model.encode(__SCREAMING_SNAKE_CASE ,       out_type=__SCREAMING_SNAKE_CASE  )

        if not self.legacy and not is_first and not text.startswith(" "  ) and tokens[0].startswith(__SCREAMING_SNAKE_CASE  ):
         snake_case__     				=					([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
        return tokens

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE  )
        return token

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					[]
        snake_case__     				=					""
        snake_case__     				=					False
        for i, token in enumerate(__SCREAMING_SNAKE_CASE  ):
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
          if not prev_is_special and i != 0:
           out_string += " "
          out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE  ) + token
          snake_case__     				=					True
          snake_case__     				=					[]
         else:
          current_sub_tokens.append(__SCREAMING_SNAKE_CASE  )
          snake_case__     				=					False
        out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE  )
        return out_string

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






        '''simple docstring'''

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

        if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file ,       __SCREAMING_SNAKE_CASE  )
        elif not os.path.isfile(self.vocab_file  ):
         with open(__SCREAMING_SNAKE_CASE ,       "wb"  ) as fi:
          snake_case__     				=					self.sp_model.serialized_model_proto()
          fi.write(__SCREAMING_SNAKE_CASE  )

        return (out_vocab_file,)

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






        '''simple docstring'''

        snake_case__     				=					[self.bos_token_id] if self.add_bos_token else []
        snake_case__     				=					[self.eos_token_id] if self.add_eos_token else []

        snake_case__     				=					bos_token_id + token_ids_a + eos_token_id

        if token_ids_a is not None:
         snake_case__     				=					output + bos_token_id + token_ids_a + eos_token_id

        return output

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






        '''simple docstring'''

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

        snake_case__     				=					[1] if self.add_bos_token else []
        snake_case__     				=					[1] if self.add_eos_token else []

        if token_ids_a is None:
         return bos_token_id + ([0] * len(__SCREAMING_SNAKE_CASE  )) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(__SCREAMING_SNAKE_CASE  ))
            + eos_token_id
            + bos_token_id
            + ([0] * len(__SCREAMING_SNAKE_CASE  ))
            + eos_token_id
        )

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






        '''simple docstring'''

        snake_case__     				=					[self.bos_token_id] if self.add_bos_token else []
        snake_case__     				=					[self.eos_token_id] if self.add_eos_token else []

        snake_case__     				=					[0] * len(bos_token_id + token_ids_a + eos_token_id  )

        if token_ids_a is not None:
         output += [1] * len(bos_token_id + token_ids_a + eos_token_id  )

        return output

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					list(conversation.iter_texts()  )
        if not all([is_user for is_user, msg in dialogue[::2]]  ) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]  ):
         raise ValueError(
             "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"  )

        snake_case__     				=					[]
        if len(conversation.past_user_inputs  ) > 0:
         if not conversation.past_user_inputs[0].startswith(__SCREAMING_SNAKE_CASE  ) or E_SYS not in conversation.past_user_inputs[0]:
          snake_case__     				=					(
              B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
          )
        elif not dialogue[0][1].startswith(__SCREAMING_SNAKE_CASE  ) or E_SYS not in dialogue[0][1]:
         snake_case__     				=					(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    F"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} " ,       add_special_tokens=__SCREAMING_SNAKE_CASE  )
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2] ,       dialogue[1::2]  )
            ] ,       [] ,       )
        if not (dialogue[-1][0]):
         raise ValueError(F"Last message must be from user, got {dialogue[-1]['role']}"  )
        dialog_tokens += [self.bos_token_id] + self.encode(
            F"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}" ,       add_special_tokens=__SCREAMING_SNAKE_CASE  )
        return dialog_tokens








"""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

