




'''simple docstring'''




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 						lowerCAmelCase_   :







  '''simple docstring'''







  lowerCAmelCase_				:					CommonSchedulerState
  lowerCAmelCase_				:					jnp.ndarray

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


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	str						,		_UpperCAmelCase    :	CommonSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		):






   """simple docstring"""






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

@dataclass
class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					DDIMSchedulerState

class 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					List[str]				     =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowerCAmelCase_				:					jnp.dtype


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   return True


  @register_to_config
  def __init__(						self    :	List[str]						,		_UpperCAmelCase    :	int = 10_00						,		_UpperCAmelCase    :	float = 0.0001						,		_UpperCAmelCase    :	float = 0.02						,		_UpperCAmelCase    :	str = "linear"						,		_UpperCAmelCase    :	Optional[jnp.ndarray] = None						,		_UpperCAmelCase    :	bool = True						,		_UpperCAmelCase    :	int = 0						,		_UpperCAmelCase    :	str = "epsilon"						,		_UpperCAmelCase    :	jnp.dtype = jnp.floataa						,		):






   """simple docstring"""






   UpperCAmelCase__										=    dtype


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[CommonSchedulerState] = None ):






   """simple docstring"""






   if common is None:
    UpperCAmelCase__										=    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.
   UpperCAmelCase__										=    (
       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
   UpperCAmelCase__										=    jnp.array(1.0						,		dtype=self.dtype )

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

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	Optional[int] = None ):






   """simple docstring"""






   return sample


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Tuple = () ):






   """simple docstring"""






   UpperCAmelCase__										=    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
   UpperCAmelCase__										=    (jnp.arange(0						,		_UpperCAmelCase ) * step_ratio).round()[::-1] + self.config.steps_offset

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






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

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

   return variance


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	float = 0.0						,		_UpperCAmelCase    :	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)
   UpperCAmelCase__										=    timestep - self.config.num_train_timesteps // state.num_inference_steps

   UpperCAmelCase__										=    state.common.alphas_cumprod
   UpperCAmelCase__										=    state.final_alpha_cumprod

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

   UpperCAmelCase__										=    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":
    UpperCAmelCase__										=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    UpperCAmelCase__										=    model_output
   elif self.config.prediction_type == "sample":
    UpperCAmelCase__										=    model_output
    UpperCAmelCase__										=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
    UpperCAmelCase__										=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    UpperCAmelCase__										=    (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)
   UpperCAmelCase__										=    self._get_variance(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    eta * variance ** 0.5

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   UpperCAmelCase__										=    (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
   UpperCAmelCase__										=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if not return_dict:
    return (prev_sample, state)

   return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase						,		state=_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	DDIMSchedulerState						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		_UpperCAmelCase    :	jnp.ndarray						,		):






   """simple docstring"""






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



  def __len__(						self    :	List[str] ):






   """simple docstring"""






   return self.config.num_train_timesteps






'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCAmelCase_       				=      logging.getLogger(__name__)

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	Optional[Any] ):






   """simple docstring"""






   if not self.initialized:
    UpperCAmelCase__										=    RagRetriever(
        _UpperCAmelCase						,		question_encoder_tokenizer=_UpperCAmelCase						,		generator_tokenizer=_UpperCAmelCase						,		index=_UpperCAmelCase						,		init_retrieval=_UpperCAmelCase						,		)
    UpperCAmelCase__										=    True


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   self.retriever.index.init_index()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   del self.retriever
   UpperCAmelCase__										=    False



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.retriever._main_retrieve(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    self.retriever.index.get_doc_dicts(_UpperCAmelCase )
   return doc_ids, retrieved_doc_embeds, doc_dicts

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def __init__(						self    :	Optional[Any]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	Dict						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	List[Any]=None ):






   """simple docstring"""






   if index is not None and index.is_initialized() and len(_UpperCAmelCase ) > 0:
    raise ValueError(
        """When using Ray for distributed fine-tuning, """
        """you'll need to provide the paths instead, """
        """as the dataset and the index are loaded """
        """separately. More info in examples/rag/use_own_knowledge_dataset.py """ )

   super().__init__(
       _UpperCAmelCase						,		question_encoder_tokenizer=_UpperCAmelCase						,		generator_tokenizer=_UpperCAmelCase						,		index=_UpperCAmelCase						,		init_retrieval=_UpperCAmelCase						,		)

   UpperCAmelCase__										=    retrieval_workers
   UpperCAmelCase__										=    question_encoder_tokenizer
   UpperCAmelCase__										=    generator_tokenizer
   if len(self.retrieval_workers ) > 0:
    ray.get(
        [
            worker.create_rag_retriever.remote(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )
            for worker in self.retrieval_workers
        ] )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   logger.info("""initializing retrieval""" )

   if len(self.retrieval_workers ) > 0:
    ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
   else:
    # Non-distributed training. Load index into this same process.
    self.index.init_index()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Dict ):






   """simple docstring"""






   if len(self.retrieval_workers ) > 0:
    # Select a random retrieval actor.
    UpperCAmelCase__										=    self.retrieval_workers[random.randint(0						,		len(self.retrieval_workers ) - 1 )]
    UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    ray.get(
        random_worker.retrieve.remote(_UpperCAmelCase						,		_UpperCAmelCase ) )
   else:
    UpperCAmelCase__							,			UpperCAmelCase__										=    self._main_retrieve(_UpperCAmelCase						,		_UpperCAmelCase )
    UpperCAmelCase__										=    self.index.get_doc_dicts(_UpperCAmelCase )
   return retrieved_doc_embeds, doc_ids, doc_dicts


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	str						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str=None						,		**_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   return super(_UpperCAmelCase						,		cls ).get_tokenizers(_UpperCAmelCase						,		_UpperCAmelCase						,		**_UpperCAmelCase )


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	int						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	Union[str, Any]=None						,		**_UpperCAmelCase    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    kwargs.pop("""config"""						,		_UpperCAmelCase ) or RagConfig.from_pretrained(_UpperCAmelCase						,		**_UpperCAmelCase )
   UpperCAmelCase__										=    RagTokenizer.from_pretrained(_UpperCAmelCase						,		config=_UpperCAmelCase )
   UpperCAmelCase__										=    rag_tokenizer.question_encoder
   UpperCAmelCase__										=    rag_tokenizer.generator

   if indexed_dataset is not None:
    UpperCAmelCase__										=    """custom"""
    UpperCAmelCase__										=    CustomHFIndex(config.retrieval_vector_size						,		_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    cls._build_index(_UpperCAmelCase )

   return cls(
       _UpperCAmelCase						,		question_encoder_tokenizer=_UpperCAmelCase						,		generator_tokenizer=_UpperCAmelCase						,		retrieval_workers=_UpperCAmelCase						,		index=_UpperCAmelCase						,		)



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   logger.info("""re-loading the new dataset with embeddings""" )
   # access from the training loop

   ray.get([worker.clear_object.remote() for worker in self.retrieval_workers] )

   # build the index object again
   UpperCAmelCase__										=    self._build_index(self.config )

   ray.get(
       [
           worker.create_rag_retriever.remote(
               self.config						,		self.question_encoder_tokenizer						,		self.generator_tokenizer						,		_UpperCAmelCase )
           for worker in self.retrieval_workers
       ] )
