



"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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







@dataclass
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42







class       UpperCamelCase__   (	lowercase_,					lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     1



      @register_to_config
      def __init__(      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      int = 2_0_0_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.15    ,			SCREAMING_SNAKE_CASE_				:      float = 0.01    ,			SCREAMING_SNAKE_CASE_				:      float = 13_48.0    ,			SCREAMING_SNAKE_CASE_				:      float = 1E-5    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			):
        # standard deviation of the initial noise distribution
        lowerCAmelCase_   :				Optional[Any]								= sigma_max

        # setable values
        lowerCAmelCase_   :				List[Any]								= None

        self.set_sigmas(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None       ):
        return sample



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      float = None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, torch.device] = None       ):
        lowerCAmelCase_   :				List[str]								= sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowerCAmelCase_   :				int								= torch.linspace(1    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			device=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      float = None    ,			SCREAMING_SNAKE_CASE_				:      float = None    ,			SCREAMING_SNAKE_CASE_				:      float = None       ):
        lowerCAmelCase_   :				Any								= sigma_min if sigma_min is not None else self.config.sigma_min
        lowerCAmelCase_   :				Optional[Any]								= sigma_max if sigma_max is not None else self.config.sigma_max
        lowerCAmelCase_   :				Union[str, Any]								= sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
          self.set_timesteps(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[Any]								= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        lowerCAmelCase_   :				int								= torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE_       )    ,			math.log(SCREAMING_SNAKE_CASE_       )    ,			SCREAMING_SNAKE_CASE_       )       )
        lowerCAmelCase_   :				Tuple								= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        return torch.where(
            timesteps == 0    ,			torch.zeros_like(t.to(timesteps.device       )       )    ,			self.discrete_sigmas[timesteps - 1].to(timesteps.device       )    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      Optional[torch.Generator] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			):
        if self.timesteps is None:
          raise ValueError(
              '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'       )

        lowerCAmelCase_   :				Any								= timestep * torch.ones(
            sample.shape[0]    ,			device=sample.device       )  # torch.repeat_interleave(timestep, sample.shape[0])
        lowerCAmelCase_   :				Dict								= (timestep * (len(self.timesteps       ) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        lowerCAmelCase_   :				List[Any]								= timesteps.to(self.discrete_sigmas.device       )

        lowerCAmelCase_   :				Optional[int]								= self.discrete_sigmas[timesteps].to(sample.device       )
        lowerCAmelCase_   :				Optional[int]								= self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ).to(sample.device       )
        lowerCAmelCase_   :				Tuple								= torch.zeros_like(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        lowerCAmelCase_   :				Optional[Any]								= diffusion.flatten()
        while len(diffusion.shape       ) < len(sample.shape       ):
          lowerCAmelCase_   :				Any								= diffusion.unsqueeze(-1       )
        lowerCAmelCase_   :				Tuple								= drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        lowerCAmelCase_   :				str								= randn_tensor(
            sample.shape    ,			layout=sample.layout    ,			generator=SCREAMING_SNAKE_CASE_    ,			device=sample.device    ,			dtype=sample.dtype       )
        lowerCAmelCase_   :				Union[str, Any]								= sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        lowerCAmelCase_   :				List[Any]								= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
          return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_    ,			prev_sample_mean=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      Optional[torch.Generator] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			):
        if self.timesteps is None:
          raise ValueError(
              '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'       )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        lowerCAmelCase_   :				Any								= randn_tensor(sample.shape    ,			layout=sample.layout    ,			generator=SCREAMING_SNAKE_CASE_       ).to(sample.device       )

        # compute step size from the model_output, the noise, and the snr
        lowerCAmelCase_   :				Dict								= torch.norm(model_output.reshape(model_output.shape[0]    ,			-1       )    ,			dim=-1       ).mean()
        lowerCAmelCase_   :				Tuple								= torch.norm(noise.reshape(noise.shape[0]    ,			-1       )    ,			dim=-1       ).mean()
        lowerCAmelCase_   :				Optional[Any]								= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        lowerCAmelCase_   :				Dict								= step_size * torch.ones(sample.shape[0]       ).to(sample.device       )
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        lowerCAmelCase_   :				str								= step_size.flatten()
        while len(step_size.shape       ) < len(sample.shape       ):
          lowerCAmelCase_   :				str								= step_size.unsqueeze(-1       )
        lowerCAmelCase_   :				str								= sample + step_size * model_output
        lowerCAmelCase_   :				Optional[Any]								= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
          return (prev_sample,)

        return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCAmelCase_   :				Union[str, Any]								= timesteps.to(original_samples.device       )
        lowerCAmelCase_   :				Union[str, Any]								= self.discrete_sigmas.to(original_samples.device       )[timesteps]
        lowerCAmelCase_   :				int								= (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(SCREAMING_SNAKE_CASE_       ) * sigmas[:, None, None, None]
        )
        lowerCAmelCase_   :				Optional[Any]								= noise + original_samples
        return noisy_samples





      def __len__(      self				:      int       ):
        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 BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


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

lowercase__		:				List[str]          =	{"""tokenizer_file""": """tokenizer.json"""}

lowercase__		:				List[str]          =	{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     ["""input_ids""", """attention_mask"""]
      _SCREAMING_SNAKE_CASE  				=     None



      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]="<unk>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="</s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<pad>"    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      Any=False    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			unk_token=SCREAMING_SNAKE_CASE_    ,			bos_token=SCREAMING_SNAKE_CASE_    ,			eos_token=SCREAMING_SNAKE_CASE_    ,			pad_token=SCREAMING_SNAKE_CASE_    ,			add_prefix_space=SCREAMING_SNAKE_CASE_    ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_    ,			)
        lowerCAmelCase_   :				Optional[Any]								= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get('add_prefix_space'    ,			SCREAMING_SNAKE_CASE_       ) != add_prefix_space:
          lowerCAmelCase_   :				str								= getattr(SCREAMING_SNAKE_CASE_    ,			pre_tok_state.pop('type'       )       )
          lowerCAmelCase_   :				str								= add_prefix_space
          lowerCAmelCase_   :				str								= pre_tok_class(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				str								= add_prefix_space



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )
        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )

        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = None       ):
        lowerCAmelCase_   :				Optional[int]								= self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,			name=SCREAMING_SNAKE_CASE_       )
        return tuple(SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      "Conversation"       ):
        lowerCAmelCase_   :				int								= []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       ) + [self.eos_token_id]       )

        if len(SCREAMING_SNAKE_CASE_       ) > self.model_max_length:
          lowerCAmelCase_   :				Any								= input_ids[-self.model_max_length :]
        return input_ids





