



"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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 			__snake_case			:
  snake_case__							:  CommonSchedulerState

  # setable values
  snake_case__							:  jnp.ndarray
  snake_case__							:  jnp.ndarray
  snake_case__							:  Optional[int]         =		None




  @classmethod
  def 		SCREAMING_SNAKE_CASE							(					cls	:		List[str]		,   __lowerCAmelCase	:		CommonSchedulerState		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		jnp.ndarray						):






         """simple docstring"""






         return cls(common=__lowerCAmelCase		,   init_noise_sigma=__lowerCAmelCase		,   timesteps=__lowerCAmelCase						)






@dataclass
class 			__snake_case			(      _lowercase):
  snake_case__							:  DDPMSchedulerState






class 			__snake_case			(      _lowercase    ,	_lowercase):
  snake_case__							:  Union[str, Any]         =		[e.name for e in FlaxKarrasDiffusionSchedulers]

  snake_case__							:  jnp.dtype




  @property
  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         return True




  @register_to_config
  def __init__(					self	:		List[str]		,   __lowerCAmelCase	:		int = 1_0_0_0		,   __lowerCAmelCase	:		float = 0.00_01		,   __lowerCAmelCase	:		float = 0.02		,   __lowerCAmelCase	:		str = "linear"		,   __lowerCAmelCase	:		Optional[jnp.ndarray] = None		,   __lowerCAmelCase	:		str = "fixed_small"		,   __lowerCAmelCase	:		bool = True		,   __lowerCAmelCase	:		str = "epsilon"		,   __lowerCAmelCase	:		jnp.dtype = jnp.floataa		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					dtype




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		Optional[CommonSchedulerState] = None						):






         """simple docstring"""






         if common is None:
                _lowerCamelCase		:							List[Any]            =					CommonSchedulerState.create(self						)

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

         _lowerCamelCase		:							Union[str, Any]            =					jnp.arange(0		,   self.config.num_train_timesteps						).round()[::-1]

         return DDPMSchedulerState.create(
             common=__lowerCAmelCase		,   init_noise_sigma=__lowerCAmelCase		,   timesteps=__lowerCAmelCase		,   )




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		DDPMSchedulerState		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		Optional[int] = None						):






         """simple docstring"""






         return sample




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any		,   __lowerCAmelCase	:		DDPMSchedulerState		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		Tuple = ()						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					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		:							Any            =					(jnp.arange(0		,   __lowerCAmelCase						) * step_ratio).round()[::-1]

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




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   __lowerCAmelCase	:		DDPMSchedulerState		,   __lowerCAmelCase	:		List[Any]		,   __lowerCAmelCase	:		Tuple=None		,   __lowerCAmelCase	:		int=None						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					state.common.alphas_cumprod[t]
         _lowerCamelCase		:							List[str]            =					jnp.where(t > 0		,   state.common.alphas_cumprod[t - 1]		,   jnp.array(1.0		,   dtype=self.dtype						)						)

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         _lowerCamelCase		:							List[str]            =					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

         if variance_type is None:
                _lowerCamelCase		:							str            =					self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
                _lowerCamelCase		:							Optional[Any]            =					jnp.clip(__lowerCAmelCase		,   a_min=1E-20						)
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
                _lowerCamelCase		:							Optional[Any]            =					jnp.log(jnp.clip(__lowerCAmelCase		,   a_min=1E-20						)						)
         elif variance_type == "fixed_large":
                _lowerCamelCase		:							List[str]            =					state.common.betas[t]
         elif variance_type == "fixed_large_log":
                # Glide max_log
                _lowerCamelCase		:							List[Any]            =					jnp.log(state.common.betas[t]						)
         elif variance_type == "learned":
                return predicted_variance
         elif variance_type == "learned_range":
                _lowerCamelCase		:							Dict            =					variance
                _lowerCamelCase		:							Union[str, Any]            =					state.common.betas[t]
                _lowerCamelCase		:							Union[str, Any]            =					(predicted_variance + 1) / 2
                _lowerCamelCase		:							Optional[int]            =					frac * max_log + (1 - frac) * min_log

         return variance




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		DDPMSchedulerState		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		Optional[jax.random.KeyArray] = None		,   __lowerCAmelCase	:		bool = True		,   ):






         """simple docstring"""






         _lowerCamelCase		:							str            =					timestep

         if key is None:
                _lowerCamelCase		:							List[Any]            =					jax.random.PRNGKey(0						)

         if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					jnp.split(__lowerCAmelCase		,   sample.shape[1]		,   axis=1						)
         else:
                _lowerCamelCase		:							Union[str, Any]            =					None

         # 1. compute alphas, betas
         _lowerCamelCase		:							str            =					state.common.alphas_cumprod[t]
         _lowerCamelCase		:							Tuple            =					jnp.where(t > 0		,   state.common.alphas_cumprod[t - 1]		,   jnp.array(1.0		,   dtype=self.dtype						)						)
         _lowerCamelCase		:							Optional[Any]            =					1 - alpha_prod_t
         _lowerCamelCase		:							Optional[int]            =					1 - alpha_prod_t_prev

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
                _lowerCamelCase		:							Dict            =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
                _lowerCamelCase		:							Dict            =					model_output
         elif self.config.prediction_type == "v_prediction":
                _lowerCamelCase		:							int            =					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         else:
                raise ValueError(
                    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
                    ''' for the FlaxDDPMScheduler.'''						)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
                _lowerCamelCase		:							Tuple            =					jnp.clip(__lowerCAmelCase		,   -1		,   1						)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         _lowerCamelCase		:							Any            =					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         _lowerCamelCase		:							List[Any]            =					state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         _lowerCamelCase		:							List[str]            =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         def random_variance():
                _lowerCamelCase		:							Dict            =					jax.random.split(__lowerCAmelCase		,   num=1						)
                _lowerCamelCase		:							List[str]            =					jax.random.normal(__lowerCAmelCase		,   shape=model_output.shape		,   dtype=self.dtype						)
                return (self._get_variance(__lowerCAmelCase		,   __lowerCAmelCase		,   predicted_variance=__lowerCAmelCase						) ** 0.5) * noise

         _lowerCamelCase		:							List[str]            =					jnp.where(t > 0		,   random_variance()		,   jnp.zeros(model_output.shape		,   dtype=self.dtype						)						)

         _lowerCamelCase		:							Union[str, Any]            =					pred_prev_sample + variance

         if not return_dict:
                return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase		,   state=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		DDPMSchedulerState		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		jnp.ndarray		,   ):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		int		,   __lowerCAmelCase	:		DDPMSchedulerState		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		jnp.ndarray		,   __lowerCAmelCase	:		jnp.ndarray		,   ):






         """simple docstring"""






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




  def __len__(					self	:		List[Any]						):






         """simple docstring"""






         return self.config.num_train_timesteps






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Optional[int]										=	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       Union[str, Any]										=	{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:       Optional[int]										=	{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			['input_ids', 'attention_mask']
 SCREAMING_SNAKE_CASE__ =			RobertaTokenizer




 def __init__(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase="replace"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="<unk>"   ,    _lowerCamelCase="<pad>"   ,    _lowerCamelCase="<mask>"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(
           _lowerCamelCase   ,    _lowerCamelCase   ,    tokenizer_file=_lowerCamelCase   ,    errors=_lowerCamelCase   ,    bos_token=_lowerCamelCase   ,    eos_token=_lowerCamelCase   ,    sep_token=_lowerCamelCase   ,    cls_token=_lowerCamelCase   ,    unk_token=_lowerCamelCase   ,    pad_token=_lowerCamelCase   ,    mask_token=_lowerCamelCase   ,    add_prefix_space=_lowerCamelCase   ,    trim_offsets=_lowerCamelCase   ,    **_lowerCamelCase   ,    )

       a		:List[Any]									=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
       if pre_tok_state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
             a		:Optional[int]									=			getattr(_lowerCamelCase   ,    pre_tok_state.pop('''type'''       )       )
             a		:Union[str, Any]									=			add_prefix_space
             a		:Dict									=			pre_tok_class(**_lowerCamelCase       )

       a		:Union[str, Any]									=			add_prefix_space

       a		:List[Any]									=			'''post_processor'''
       a		:List[str]									=			getattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )
       if tokenizer_component_instance:
             a		:Optional[Any]									=			json.loads(tokenizer_component_instance.__getstate__()       )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                   a		:Optional[int]									=			tuple(state['''sep''']       )
             if "cls" in state:
                   a		:Union[str, Any]									=			tuple(state['''cls''']       )

             a		:Optional[int]									=			False

             if state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
                   a		:int									=			add_prefix_space
                   a		:Union[str, Any]									=			True

             if state.get('''trim_offsets'''   ,    _lowerCamelCase       ) != trim_offsets:
                   a		:int									=			trim_offsets
                   a		:List[str]									=			True

             if changes_to_apply:
                   a		:Union[str, Any]									=			getattr(_lowerCamelCase   ,    state.pop('''type'''       )       )
                   a		:str									=			component_class(**_lowerCamelCase       )
                   setattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Union[str, Any]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase   ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else value
       a		:int									=			value




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Union[str, Any]									=			kwargs.get('''is_split_into_words'''   ,    _lowerCamelCase       )
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Any									=			kwargs.get('''is_split_into_words'''   ,    _lowerCamelCase       )

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

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




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Any									=			self._tokenizer.model.save(_lowerCamelCase   ,    name=_lowerCamelCase       )
       return tuple(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a		:Union[str, Any]									=			[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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Dict									=			[self.sep_token_id]
       a		:int									=			[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]





