


"""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 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

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

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    1.0
        for k in range(__UpperCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

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

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     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'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    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
     a   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

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

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

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






  def __len__(						self						)				->					List[Any]:
     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


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= logging.getLogger(__name__)


class 	A_       :





  """simple docstring"""

  def __init__(						self						)				->					Dict:
     a   :	List[str]						=    False
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     if not self.initialized:
        a   :	Dict						=    RagRetriever(
            __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)
        a   :	Dict						=    True
  def 							lowercase_     (						self						)				->					List[Any]:
     self.retriever.index.init_index()
  def 							lowercase_     (						self						)				->					List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a   :	Dict						=    False






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:
     a	, a   :	List[str]						=    self.retriever._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
     a   :	List[Any]						=    self.retriever.index.get_doc_dicts(__UpperCAmelCase						)
     return doc_ids, retrieved_doc_embeds, doc_dicts



class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None						)				->					Tuple:
     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  ,					)

     a   :	Tuple						=    retrieval_workers
     a   :	List[str]						=    question_encoder_tokenizer
     a   :	Union[str, Any]						=    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 							lowercase_     (						self						)				->					Tuple:
     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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     if len(self.retrieval_workers						) > 0:
        # Select a random retrieval actor.
        a   :	Dict						=    self.retrieval_workers[random.randint(0  ,					len(self.retrieval_workers						) - 1						)]
        a	, a	, a   :	Any						=    ray.get(
            random_worker.retrieve.remote(__UpperCAmelCase  ,					__UpperCAmelCase						)						)
     else:
        a	, a   :	str						=    self._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
        a   :	str						=    self.index.get_doc_dicts(__UpperCAmelCase						)
     return retrieved_doc_embeds, doc_ids, doc_dicts
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					int:
     return super(__UpperCAmelCase  ,					cls						).get_tokenizers(__UpperCAmelCase  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					str:
     a   :	Union[str, Any]						=    kwargs.pop('config'  ,					__UpperCAmelCase						) or RagConfig.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
     a   :	Optional[Any]						=    RagTokenizer.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase						)
     a   :	List[str]						=    rag_tokenizer.question_encoder
     a   :	int						=    rag_tokenizer.generator

     if indexed_dataset is not None:
        a   :	Optional[int]						=    'custom'
        a   :	str						=    CustomHFIndex(config.retrieval_vector_size  ,					__UpperCAmelCase						)
     else:
        a   :	Dict						=    cls._build_index(__UpperCAmelCase						)

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






  def 							lowercase_     (						self						)				->					List[Any]:
     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
     a   :	Optional[Any]						=    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
         ]						)





