# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 				a_ :






 '''simple docstring'''






 UpperCAmelCase_	 =					42
 UpperCAmelCase_	 =					42

 # setable values
 UpperCAmelCase_	 =					42
 UpperCAmelCase_	 =					42
 UpperCAmelCase_	 =					None




 @classmethod
 def 		__snake_case						(			cls					:  Dict			,    lowercase__					:  CommonSchedulerState			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  jnp.ndarray			,    ):







        '''simple docstring'''



        return cls(
            common=lowercase__			,    final_alpha_cumprod=lowercase__			,    init_noise_sigma=lowercase__			,    timesteps=lowercase__			,    )






@dataclass
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					42






class 				a_ (						SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					[e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase_	 =					42




 @property
 def 		__snake_case						(			self					:  Dict):







        '''simple docstring'''



        return True




 @register_to_config
 def __init__(			self					:  Optional[Any]			,    lowercase__					:  int = 1_000			,    lowercase__					:  float = 0.0_001			,    lowercase__					:  float = 0.02			,    lowercase__					:  str = "linear"			,    lowercase__					:  Optional[jnp.ndarray] = None			,    lowercase__					:  bool = True			,    lowercase__					:  int = 0			,    lowercase__					:  str = "epsilon"			,    lowercase__					:  jnp.dtype = jnp.floataa			,    ):







        '''simple docstring'''



        lowerCAmelCase__     	=					dtype




 def 		__snake_case						(			self					:  List[str]			,    lowercase__					:  Optional[CommonSchedulerState] = None):







        '''simple docstring'''



        if common is None:
               lowerCAmelCase__     	=					CommonSchedulerState.create(self)

        # At every step in ddim, we are looking into the previous alphas_cumprod
        # For the final step, there is no previous alphas_cumprod because we are already at 0
        # `set_alpha_to_one` decides whether we set this parameter simply to one or
        # whether we use the final alpha of the "non-previous" one.
        lowerCAmelCase__     	=					(
            jnp.array(1.0			,    dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
        )

        # standard deviation of the initial noise distribution
        lowerCAmelCase__     	=					jnp.array(1.0			,    dtype=self.dtype)

        lowerCAmelCase__     	=					jnp.arange(0			,    self.config.num_train_timesteps).round()[::-1]

        return DDIMSchedulerState.create(
            common=lowercase__			,    final_alpha_cumprod=lowercase__			,    init_noise_sigma=lowercase__			,    timesteps=lowercase__			,    )




 def 		__snake_case						(			self					:  Any			,    lowercase__					:  DDIMSchedulerState			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  Optional[int] = None):







        '''simple docstring'''



        return sample




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  DDIMSchedulerState			,    lowercase__					:  int			,    lowercase__					:  Tuple = ()):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        lowerCAmelCase__     	=					(jnp.arange(0			,    lowercase__) * step_ratio).round()[::-1] + self.config.steps_offset

        return state.replace(
            num_inference_steps=lowercase__			,    timesteps=lowercase__			,    )




 def 		__snake_case						(			self					:  int			,    lowercase__					:  DDIMSchedulerState			,    lowercase__					:  Optional[int]			,    lowercase__					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					state.common.alphas_cumprod[timestep]
        lowerCAmelCase__     	=					jnp.where(
            prev_timestep >= 0			,    state.common.alphas_cumprod[prev_timestep]			,    state.final_alpha_cumprod)
        lowerCAmelCase__     	=					1 - alpha_prod_t
        lowerCAmelCase__     	=					1 - alpha_prod_t_prev

        lowerCAmelCase__     	=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  DDIMSchedulerState			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  int			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  float = 0.0			,    lowercase__					:  bool = True			,    ):







        '''simple docstring'''



        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')

        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
        # Ideally, read DDIM paper in-detail understanding

        # Notation (<variable name> -> <name in paper>
        # - pred_noise_t -> e_theta(x_t, t)
        # - pred_original_sample -> f_theta(x_t, t) or x_0
        # - std_dev_t -> sigma_t
        # - eta -> η
        # - pred_sample_direction -> "direction pointing to x_t"
        # - pred_prev_sample -> "x_t-1"

        # 1. get previous step value (=t-1)
        lowerCAmelCase__     	=					timestep - self.config.num_train_timesteps // state.num_inference_steps

        lowerCAmelCase__     	=					state.common.alphas_cumprod
        lowerCAmelCase__     	=					state.final_alpha_cumprod

        # 2. compute alphas, betas
        lowerCAmelCase__     	=					alphas_cumprod[timestep]
        lowerCAmelCase__     	=					jnp.where(prev_timestep >= 0			,    alphas_cumprod[prev_timestep]			,    lowercase__)

        lowerCAmelCase__     	=					1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
               lowerCAmelCase__     	=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
               lowerCAmelCase__     	=					model_output
        elif self.config.prediction_type == "sample":
               lowerCAmelCase__     	=					model_output
               lowerCAmelCase__     	=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
               lowerCAmelCase__     	=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
               lowerCAmelCase__     	=					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
               raise ValueError(
                   F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                   ' `v_prediction`')

        # 4. compute variance: "sigma_t(η)" -> see formula (16)
        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
        lowerCAmelCase__     	=					self._get_variance(lowercase__			,    lowercase__			,    lowercase__)
        lowerCAmelCase__     	=					eta * variance ** 0.5

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCAmelCase__     	=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        lowerCAmelCase__     	=					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
               return (prev_sample, state)

        return FlaxDDIMSchedulerOutput(prev_sample=lowercase__			,    state=lowercase__)




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  DDIMSchedulerState			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  jnp.ndarray			,    ):







        '''simple docstring'''



        return add_noise_common(state.common			,    lowercase__			,    lowercase__			,    lowercase__)




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  DDIMSchedulerState			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  jnp.ndarray			,    lowercase__					:  jnp.ndarray			,    ):







        '''simple docstring'''



        return get_velocity_common(state.common			,    lowercase__			,    lowercase__			,    lowercase__)




 def __len__(			self					:  Dict):







        '''simple docstring'''



        return self.config.num_train_timesteps

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							= {
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__							= {
    'microsoft/mpnet-base': 512,
}

lowerCAmelCase__							= {
    'microsoft/mpnet-base': {'do_lower_case': True},
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					MPNetTokenizer
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']




 def __init__(			self					:  Dict			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str=True			,    lowercase__					:  int="<s>"			,    lowercase__					:  List[str]="</s>"			,    lowercase__					:  Tuple="</s>"			,    lowercase__					:  Tuple="<s>"			,    lowercase__					:  Dict="[UNK]"			,    lowercase__					:  int="<pad>"			,    lowercase__					:  Union[str, Any]="<mask>"			,    lowercase__					:  Tuple=True			,    lowercase__					:  int=None			,    **lowercase__					:  Union[str, Any]			,    ):







        '''simple docstring'''



        super().__init__(
            lowercase__			,    tokenizer_file=lowercase__			,    do_lower_case=lowercase__			,    bos_token=lowercase__			,    eos_token=lowercase__			,    sep_token=lowercase__			,    cls_token=lowercase__			,    unk_token=lowercase__			,    pad_token=lowercase__			,    mask_token=lowercase__			,    tokenize_chinese_chars=lowercase__			,    strip_accents=lowercase__			,    **lowercase__			,    )

        lowerCAmelCase__     	=					json.loads(self.backend_tokenizer.normalizer.__getstate__())
        if (
            pre_tok_state.get('lowercase'			,    lowercase__) != do_lower_case
            or pre_tok_state.get('strip_accents'			,    lowercase__) != strip_accents
        ):
               lowerCAmelCase__     	=					getattr(lowercase__			,    pre_tok_state.pop('type'))
               lowerCAmelCase__     	=					do_lower_case
               lowerCAmelCase__     	=					strip_accents
               lowerCAmelCase__     	=					pre_tok_class(**lowercase__)

        lowerCAmelCase__     	=					do_lower_case




 @property
 def 		__snake_case						(			self					:  Tuple):







        '''simple docstring'''



        if self._mask_token is None:
               if self.verbose:
                      logger.error('Using mask_token, but it is not set yet.')
               return None
        return str(self._mask_token)




 @mask_token.setter
 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					AddedToken(lowercase__			,    lstrip=lowercase__			,    rstrip=lowercase__) if isinstance(lowercase__			,    lowercase__) else value
        lowerCAmelCase__     	=					value




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  Union[str, Any]			,    lowercase__					:  Any=None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
               return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.sep_token_id]
        lowerCAmelCase__     	=					[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 + sep + token_ids_a + sep) * [0]




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
