





'''simple docstring'''




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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class  SCREAMING_SNAKE_CASE    :
     # setable values
     lowerCAmelCase					=       None
     lowerCAmelCase					=       None
     lowerCAmelCase					=       None  # sigma(t_i)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls):

          '''simple docstring'''






          return cls()




@dataclass
class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       42
     lowerCAmelCase					=       42
     lowerCAmelCase					=       42




class  SCREAMING_SNAKE_CASE    (a__					,		a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return True


     @register_to_config
     def __init__(						self						,	_UpperCAmelCase = 0.02						,	_UpperCAmelCase = 100						,	_UpperCAmelCase = 1.007						,	_UpperCAmelCase = 80						,	_UpperCAmelCase = 0.05						,	_UpperCAmelCase = 50						,	):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return KarrasVeSchedulerState.create()


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = ()):

          '''simple docstring'''






          __A						: Dict      =       jnp.arange(0						,	_UpperCAmelCase)[::-1].copy()
          __A						: Dict      =       [
              (
                  self.config.sigma_max**2
                  * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
              )
              for i in timesteps
          ]

          return state.replace(
              num_inference_steps=_UpperCAmelCase						,	schedule=jnp.array(_UpperCAmelCase						,	dtype=jnp.floataa)						,	timesteps=_UpperCAmelCase						,	)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	):

          '''simple docstring'''






          if self.config.s_min <= sigma <= self.config.s_max:
               __A						: List[Any]      =       min(self.config.s_churn / state.num_inference_steps						,	2**0.5 - 1)
          else:
               __A						: Dict      =       0

          # sample eps ~ N(0, S_noise^2 * I)
          __A						: Optional[Any]      =       random.split(_UpperCAmelCase						,	num=1)
          __A						: Union[str, Any]      =       self.config.s_noise * random.normal(key=_UpperCAmelCase						,	shape=sample.shape)
          __A						: Tuple      =       sigma + gamma * sigma
          __A						: Optional[int]      =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

          return sample_hat, sigma_hat


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: str      =       sample_hat + sigma_hat * model_output
          __A						: Any      =       (sample_hat - pred_original_sample) / sigma_hat
          __A						: List[str]      =       sample_hat + (sigma_prev - sigma_hat) * derivative

          if not return_dict:
               return (sample_prev, derivative, state)

          return FlaxKarrasVeOutput(prev_sample=_UpperCAmelCase						,	derivative=_UpperCAmelCase						,	state=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: Tuple      =       sample_prev + sigma_prev * model_output
          __A						: Dict      =       (sample_prev - pred_original_sample) / sigma_prev
          __A						: Dict      =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

          if not return_dict:
               return (sample_prev, derivative, state)

          return FlaxKarrasVeOutput(prev_sample=_UpperCAmelCase						,	derivative=_UpperCAmelCase						,	state=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          raise NotImplementedError()






'''simple docstring'''




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase = "▁"						,	_UpperCAmelCase = True						,	_UpperCAmelCase = "<unk>"						,	_UpperCAmelCase = "</s>"						,	_UpperCAmelCase = "<pad>"						,	):

          '''simple docstring'''






          __A						: Dict      =       {
              'pad': {'id': 0, 'token': pad_token},
              'eos': {'id': 1, 'token': eos_token},
              'unk': {'id': 2, 'token': unk_token},
          }

          __A						: List[Any]      =       [None] * len(self.special_tokens)
          for token_dict in self.special_tokens.values():
               __A						: List[str]      =       token_dict['token']

          __A						: str      =       Tokenizer(Unigram())

          __A						: Dict      =       normalizers.Sequence(
              [
                  normalizers.Nmt(),
                  normalizers.NFKC(),
                  normalizers.Replace(Regex(' {2,}')						,	' '),
                  normalizers.Lowercase(),
              ])
          __A						: Union[str, Any]      =       pre_tokenizers.Sequence(
              [
                  pre_tokenizers.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase),
                  pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
                  pre_tokenizers.Punctuation(),
              ])
          __A						: Any      =       decoders.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase)

          __A						: Dict      =       TemplateProcessing(
              single=F'$A {self.special_tokens["eos"]["token"]}'						,	special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]						,	)

          __A						: Any      =       {
              'model': 'SentencePieceUnigram',
              'replacement': replacement,
              'add_prefix_space': add_prefix_space,
          }

          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: str      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
               __A						: Union[str, Any]      =       [files]
          self._tokenizer.train(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: Dict      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          self._tokenizer.train_from_iterator(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       json.loads(self._tokenizer.to_str())

          __A						: Union[str, Any]      =       self.special_tokens['unk']['id']

          __A						: str      =       Tokenizer.from_str(json.dumps(_UpperCAmelCase))