
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)






@flax.struct.dataclass
class 				UpperCAmelCase_			:
 UpperCAmelCase_						      =  4_2

 # setable values
 UpperCAmelCase_						      =  4_2
 UpperCAmelCase_						      =  4_2
 UpperCAmelCase_						      =  4_2
 UpperCAmelCase_						      =  None

 # running values
 UpperCAmelCase_						      =  None





 @classmethod
 def     snake_case__						(      cls	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_):
     return cls(common=lowercase_	,    init_noise_sigma=lowercase_	,    timesteps=lowercase_	,    sigmas=lowercase_)






@dataclass
class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  4_2






class 				UpperCAmelCase_			(		snake_case__      ,  snake_case__   ):
 UpperCAmelCase_						      =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase_						      =  4_2





 @property
 def     snake_case__						(      self):
     return True





 @register_to_config
 def __init__(      self	,    lowercase_ = 10_00	,    lowercase_ = 0.0_001	,    lowercase_ = 0.02	,    lowercase_ = "linear"	,    lowercase_ = None	,    lowercase_ = "epsilon"	,    lowercase_ = jnp.floataa	,    ):
     snake_case_	:      Tuple            =  dtype





 def     snake_case__						(      self	,    lowercase_ = None):
     if common is None:
         snake_case_	:      Tuple            =  CommonSchedulerState.create(self)

     snake_case_	:      Union[str, Any]            =  jnp.arange(0	,    self.config.num_train_timesteps).round()[::-1]
     snake_case_	:      Optional[int]            =  ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     snake_case_	:      int            =  sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=lowercase_	,    init_noise_sigma=lowercase_	,    timesteps=lowercase_	,    sigmas=lowercase_	,    )





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     (snake_case_	)	:      List[Any]            =  jnp.where(state.timesteps == timestep	,    size=1)
     snake_case_	:      Optional[Any]            =  step_index[0]

     snake_case_	:      List[str]            =  state.sigmas[step_index]
     snake_case_	:      List[Any]            =  sample / ((sigma**2 + 1) ** 0.5)
     return sample





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_):

     def lms_derivative(lowercase_):
         snake_case_	:      Tuple            =  1.0
         for k in range(lowercase_):
             if current_order == k:
                 continue
             prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

     snake_case_	:      Optional[Any]            =  integrate.quad(lowercase_	,    state.sigmas[t]	,    state.sigmas[t + 1]	,    epsrel=1E-4)[0]

     return integrated_coeff





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_ = ()):
     snake_case_	:      Tuple            =  jnp.linspace(self.config.num_train_timesteps - 1	,    0	,    lowercase_	,    dtype=self.dtype)

     snake_case_	:      Optional[Any]            =  jnp.floor(lowercase_).astype(jnp.intaa)
     snake_case_	:      Dict            =  jnp.ceil(lowercase_).astype(jnp.intaa)

     snake_case_	:      Optional[Any]            =  jnp.mod(lowercase_	,    1.0)

     snake_case_	:      int            =  ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     snake_case_	:      Union[str, Any]            =  (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     snake_case_	:      Optional[int]            =  jnp.concatenate([sigmas, jnp.array([0.0]	,    dtype=self.dtype)])

     snake_case_	:      Union[str, Any]            =  timesteps.astype(jnp.intaa)

     # initial running values
     snake_case_	:      Dict            =  jnp.zeros((0,) + shape	,    dtype=self.dtype)

     return state.replace(
         timesteps=lowercase_	,    sigmas=lowercase_	,    num_inference_steps=lowercase_	,    derivatives=lowercase_	,    )





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_ = 4	,    lowercase_ = True	,    ):
     if state.num_inference_steps is None:
         raise ValueError(
             "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler")

     snake_case_	:      int            =  state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
         snake_case_	:      Optional[Any]            =  sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         snake_case_	:      int            =  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
         raise ValueError(
             F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

     # 2. Convert to an ODE derivative
     snake_case_	:      Tuple            =  (sample - pred_original_sample) / sigma
     snake_case_	:      Optional[Any]            =  state.replace(derivatives=jnp.append(state.derivatives	,    lowercase_))
     if len(state.derivatives) > order:
         snake_case_	:      Dict            =  state.replace(derivatives=jnp.delete(state.derivatives	,    0))

     # 3. Compute linear multistep coefficients
     snake_case_	:      Any            =  min(timestep + 1	,    lowercase_)
     snake_case_	:      Tuple            =  [self.get_lms_coefficient(lowercase_	,    lowercase_	,    lowercase_	,    lowercase_) for curr_order in range(lowercase_)]

     # 4. Compute previous sample based on the derivatives path
     snake_case_	:      str            =  sample + sum(
         coeff * derivative for coeff, derivative in zip(lowercase_	,    reversed(state.derivatives)))

     if not return_dict:
         return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=lowercase_	,    state=lowercase_)





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    ):
     snake_case_	:      Optional[Any]            =  state.sigmas[timesteps].flatten()
     snake_case_	:      Optional[Any]            =  broadcast_to_shape_from_left(lowercase_	,    noise.shape)

     snake_case_	:      Dict            =  original_samples + noise * sigma

     return noisy_samples





 def __len__(      self):
     return self.config.num_train_timesteps






'''simple docstring'''
from typing import Dict, List, Optional, Tuple

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


a_     =  logging.get_logger(__name__)






class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  ["""input_ids""", """attention_mask"""]





 def __init__(      self	,    lowercase_="[PAD]"	,    lowercase_="[BOS]"	,    lowercase_="[EOS]"	,    lowercase_="[MASK]"	,    lowercase_="[CLS]"	,    lowercase_="[SEP]"	,    lowercase_=20_48	,    **lowercase_	,    ):
     snake_case_	:      int            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else pad_token
     snake_case_	:      List[str]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else bos_token
     snake_case_	:      Tuple            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else eos_token
     snake_case_	:      Optional[Any]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else mask_token
     snake_case_	:      List[str]            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else cls_token
     snake_case_	:      int            =  AddedToken(lowercase_	,    lstrip=lowercase_	,    rstrip=lowercase_) if isinstance(lowercase_	,    lowercase_) else sep_token

     super().__init__(
         pad_token=lowercase_	,    bos_token=lowercase_	,    eos_token=lowercase_	,    mask_token=lowercase_	,    cls_token=lowercase_	,    sep_token=lowercase_	,    model_max_length=lowercase_	,    **lowercase_	,    )

     snake_case_	:      Dict            =  2**8  # utf is 8 bits

     # define special tokens dict
     snake_case_	:      Dict[str, int]            =  {
         self.pad_token: 0,
         self.bos_token: 1,
         self.eos_token: 2,
         self.mask_token: 3,
         self.cls_token: 4,
         self.sep_token: 5,
     }
     snake_case_	:      Dict            =  len(self.special_tokens_encoder)
     snake_case_	:      Dict[int, str]            =  {v: k for k, v in self.special_tokens_encoder.items()}





 def     snake_case__						(      self):
     snake_case_	:      str            =  self.special_tokens_encoder.copy()
     vocab.update(self.added_tokens_encoder)
     for i in range(self._utf_vocab_size):
         snake_case_	:      Tuple            =  chr(lowercase_)
         snake_case_	:      Optional[Any]            =  i + len(self.special_tokens_encoder)
     return vocab





 @property
 def     snake_case__						(      self):
     return self._utf_vocab_size + self._num_special_tokens





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None	,    lowercase_ = False):
     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_	,    token_ids_a=lowercase_	,    already_has_special_tokens=lowercase_)

     # normal case: some special tokens
     if token_ids_a is None:
         return [1] + [0] * len(lowercase_) + [1]
     return [1] + ([0] * len(lowercase_)) + [1] + ([0] * len(lowercase_)) + [1]





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     else:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      Tuple            =  [chr(lowercase_) for i in text.encode("utf-8")]
     return tokens





 def     snake_case__						(      self	,    lowercase_):
     if token in self.special_tokens_encoder:
         snake_case_	:      Optional[int]            =  self.special_tokens_encoder[token]
     elif token in self.added_tokens_encoder:
         snake_case_	:      str            =  self.added_tokens_encoder[token]
     elif len(lowercase_) != 1:
         snake_case_	:      Union[str, Any]            =  self.unk_token_id
     else:
         snake_case_	:      Optional[Any]            =  ord(lowercase_) + self._num_special_tokens
     return token_id





 def     snake_case__						(      self	,    lowercase_):
     if index in self.special_tokens_decoder:
         snake_case_	:      Optional[Any]            =  self.special_tokens_decoder[index]
     elif index in self.added_tokens_decoder:
         snake_case_	:      List[str]            =  self.added_tokens_decoder[index]
     else:
         snake_case_	:      Union[str, Any]            =  chr(index - self._num_special_tokens)
     return token





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      str            =  b""
     for token in tokens:
         if token in self.special_tokens_decoder:
             snake_case_	:      List[Any]            =  self.special_tokens_decoder[token].encode("utf-8")
         elif token in self.added_tokens_decoder:
             snake_case_	:      Any            =  self.special_tokens_decoder[token].encode("utf-8")
         elif token in self.special_tokens_encoder:
             snake_case_	:      Union[str, Any]            =  token.encode("utf-8")
         elif token in self.added_tokens_encoder:
             snake_case_	:      Any            =  token.encode("utf-8")
         else:
             snake_case_	:      Union[str, Any]            =  bytes([ord(lowercase_)])
         bstring += tok_string
     snake_case_	:      Union[str, Any]            =  bstring.decode("utf-8"	,    errors="replace")
     return string





 def     snake_case__						(      self	,    lowercase_	,    lowercase_ = None):
     return ()




