




"""simple docstring"""

import math

import flax.linen as nn
import jax.numpy as jnp






def 		_snake_case	(   _snake_case				:   jnp.ndarray							,    _snake_case				:   int							,    _snake_case				:   float = 1							,    _snake_case				:   float = 1							,    _snake_case				:   float = 1.0E4							,    _snake_case				:   bool = False							,    _snake_case				:   float = 1.0							,    )       ->   jnp.ndarray:






      '''simple docstring'''


      assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
      assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
      _A     =  float(embedding_dim // 2				)
      _A     =  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
      _A     =  min_timescale * jnp.exp(jnp.arange(_snake_case							,    dtype=jnp.floataa				) * -log_timescale_increment				)
      _A     =  jnp.expand_dims(_snake_case							,    1				) * jnp.expand_dims(_snake_case							,    0				)

      # scale embeddings
      _A     =  scale * emb

      if flip_sin_to_cos:
            _A     =  jnp.concatenate([jnp.cos(_snake_case				), jnp.sin(_snake_case				)]							,    axis=1				)
      else:
            _A     =  jnp.concatenate([jnp.sin(_snake_case				), jnp.cos(_snake_case				)]							,    axis=1				)
      _A     =  jnp.reshape(_snake_case							,    [jnp.shape(_snake_case				)[0], embedding_dim]				)
      return signal





class 							lowercase_			(	nn.Module				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	jnp.dtype     =      jnp.floataa

 @nn.compact
 def __call__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  nn.Dense(self.time_embed_dim       ,				dtype=self.dtype       ,				name='linear_1'						)(_UpperCAmelCase						)
       _A     =  nn.silu(_UpperCAmelCase						)
       _A     =  nn.Dense(self.time_embed_dim       ,				dtype=self.dtype       ,				name='linear_2'						)(_UpperCAmelCase						)
       return temb





class 							lowercase_			(	nn.Module				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	float     =      1

 @nn.compact
 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			str						):
       return get_sinusoidal_embeddings(
           _UpperCAmelCase       ,				embedding_dim=self.dim       ,				flip_sin_to_cos=self.flip_sin_to_cos       ,				freq_shift=self.freq_shift						)


'''simple docstring'''






import logging
import random

import ray

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


_UpperCAmelCase    :     int         =       logging.getLogger(__name__)







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Tuple     )  ->			int:
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any]     )  ->			Any:
        if not self.initialized:
             _A    		=  RagRetriever(
                 UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, index=UpperCamelCase__, init_retrieval=UpperCamelCase__, )
             _A    		=  True
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        self.retriever.index.init_index()

   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict     )  ->			Optional[Any]:
        _A		,       _A    		=  self.retriever._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return doc_ids, retrieved_doc_embeds







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			int=None     )  ->			Optional[int]:
        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    		=  retrieval_workers
        if len(self.retrieval_workers     ) > 0:
             ray.get(
                 [
                     worker.create_rag_retriever.remote(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )
                     for worker in self.retrieval_workers
                 ]     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			int:
        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 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Tuple     )  ->			Dict:
        if len(self.retrieval_workers     ) > 0:
             # Select a random retrieval actor.
             _A    		=  self.retrieval_workers[random.randint(0, len(self.retrieval_workers     ) - 1     )]
             _A		,       _A    		=  ray.get(random_worker.retrieve.remote(UpperCamelCase__, UpperCamelCase__     )     )
        else:
             _A		,       _A    		=  self._main_retrieve(UpperCamelCase__, UpperCamelCase__     )
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase__     )
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			List[str], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			List[Any]=None, **UpperCamelCase__      :			Optional[int]     )  ->			Optional[Any]:
        return super(UpperCamelCase__, cls     ).get_tokenizers(UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     )

   @classmethod
   def 	__UpperCAmelCase   (      cls      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			List[Any]=None, **UpperCamelCase__      :			List[str]     )  ->			Tuple:
        _A    		=  kwargs.pop('config', UpperCamelCase__     ) or RagConfig.from_pretrained(UpperCamelCase__, **UpperCamelCase__     )
        _A    		=  RagTokenizer.from_pretrained(UpperCamelCase__, config=UpperCamelCase__     )
        _A    		=  rag_tokenizer.question_encoder
        _A    		=  rag_tokenizer.generator
        if indexed_dataset is not None:
             _A    		=  'custom'
             _A    		=  CustomHFIndex(config.retrieval_vector_size, UpperCamelCase__     )
        else:
             _A    		=  cls._build_index(UpperCamelCase__     )
        return cls(
            UpperCamelCase__, question_encoder_tokenizer=UpperCamelCase__, generator_tokenizer=UpperCamelCase__, retrieval_workers=UpperCamelCase__, index=UpperCamelCase__, )



