


'''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 							__magic_name__	:

     """simple docstring"""






     def __init__(   self		):


          '''simple docstring'''





          __A	:       List[str]      =							False


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          if not self.initialized:
               __A	:       List[Any]      =							RagRetriever(
                   lowerCamelCase			,     question_encoder_tokenizer=lowerCamelCase			,     generator_tokenizer=lowerCamelCase			,     index=lowerCamelCase			,     init_retrieval=lowerCamelCase			,     )
               __A	:       Optional[int]      =							True


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.retriever.index.init_index()


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A  ,__A	:       Optional[Any]      =							self.retriever._main_retrieve(lowerCamelCase			,     lowerCamelCase		)
          return doc_ids, retrieved_doc_embeds







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None		):


          '''simple docstring'''





          if index is not None and index.is_initialized() and len(lowerCamelCase		) > 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__(
              lowerCamelCase			,     question_encoder_tokenizer=lowerCamelCase			,     generator_tokenizer=lowerCamelCase			,     index=lowerCamelCase			,     init_retrieval=lowerCamelCase			,     )
          __A	:       Dict      =							retrieval_workers
          if len(self.retrieval_workers		) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)
                       for worker in self.retrieval_workers
                   ]		)


     def lowerCAmelCase__  (   self		):


          '''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 lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          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	:       Union[str, Any]      =							ray.get(random_worker.retrieve.remote(lowerCamelCase			,     lowerCamelCase		)		)
          else:
               __A  ,__A	:       Optional[int]      =							self._main_retrieve(lowerCamelCase			,     lowerCamelCase		)
          return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase		)


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





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


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							kwargs.pop("config"			,     lowerCamelCase		) or RagConfig.from_pretrained(lowerCamelCase			,     **lowerCamelCase		)
          __A	:       Optional[int]      =							RagTokenizer.from_pretrained(lowerCamelCase			,     config=lowerCamelCase		)
          __A	:       Optional[int]      =							rag_tokenizer.question_encoder
          __A	:       Union[str, Any]      =							rag_tokenizer.generator
          if indexed_dataset is not None:
               __A	:       List[str]      =							"custom"
               __A	:       Any      =							CustomHFIndex(config.retrieval_vector_size			,     lowerCamelCase		)
          else:
               __A	:       Optional[Any]      =							cls._build_index(lowerCamelCase		)
          return cls(
              lowerCamelCase			,     question_encoder_tokenizer=lowerCamelCase			,     generator_tokenizer=lowerCamelCase			,     retrieval_workers=lowerCamelCase			,     index=lowerCamelCase			,     )





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'beit'


     def __init__(   self			,     lowerCamelCase=8192			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=224			,     lowerCamelCase=16			,     lowerCamelCase=3			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=True			,     lowerCamelCase=[3, 5, 7, 11]			,     lowerCamelCase=[1, 2, 3, 6]			,     lowerCamelCase=True			,     lowerCamelCase=0.4			,     lowerCamelCase=256			,     lowerCamelCase=1			,     lowerCamelCase=False			,     lowerCamelCase=255			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       int      =							vocab_size
          __A	:       Tuple      =							hidden_size
          __A	:       List[str]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       str      =							hidden_dropout_prob
          __A	:       Dict      =							attention_probs_dropout_prob
          __A	:       int      =							initializer_range
          __A	:       List[str]      =							layer_norm_eps

          __A	:       str      =							image_size
          __A	:       Optional[int]      =							patch_size
          __A	:       List[Any]      =							num_channels
          __A	:       List[str]      =							use_mask_token
          __A	:       Union[str, Any]      =							use_absolute_position_embeddings
          __A	:       Optional[int]      =							use_relative_position_bias
          __A	:       int      =							use_shared_relative_position_bias
          __A	:       int      =							layer_scale_init_value
          __A	:       Union[str, Any]      =							drop_path_rate
          __A	:       Dict      =							use_mean_pooling
          # decode head attributes (semantic segmentation)
          __A	:       List[str]      =							out_indices
          __A	:       str      =							pool_scales
          # auxiliary head attributes (semantic segmentation)
          __A	:       List[str]      =							use_auxiliary_head
          __A	:       Tuple      =							auxiliary_loss_weight
          __A	:       Dict      =							auxiliary_channels
          __A	:       Tuple      =							auxiliary_num_convs
          __A	:       List[str]      =							auxiliary_concat_input
          __A	:       int      =							semantic_loss_ignore_index







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				version.parse('1.11' )


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 1E-4

