




'''simple docstring'''
import logging
import random

import ray

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


lowerCamelCase			:				Union[str, Any] 			=					logging.getLogger(__name__)







class 							A__   :





  def __init__(		self				:     str			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =False





  def 						A  (		self				:     Tuple							, _a				:     List[Any]							, _a				:     Union[str, Any]							, _a				:     Optional[Any]							, _a				:     List[str]			)     ->   List[str]:





       '''simple docstring'''






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





  def 						A  (		self				:     Any			)     ->   Tuple:





       '''simple docstring'''






       self.retriever.index.init_index()





  def 						A  (		self				:     Union[str, Any]							, _a				:     str							, _a				:     Tuple			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.retriever._main_retrieve(__UpperCAmelCase							, __UpperCAmelCase			)
       return doc_ids, retrieved_doc_embeds







class 							A__   (	A__  ):





  def __init__(		self				:     int							, _a				:     Tuple							, _a				:     Tuple							, _a				:     Dict							, _a				:     Dict							, _a				:     Dict=None			)     ->   Optional[Any]:





       '''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							, )
       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     List[Any]			)     ->   Tuple:





       '''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 						A  (		self				:     Optional[Any]							, _a				:     Any							, _a				:     Tuple			)     ->   Dict:





       '''simple docstring'''






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





  @classmethod
  def 						A  (		cls				:     List[str]							, _a				:     Union[str, Any]							, _a				:     Tuple=None							, **_a				:     Optional[int]			)     ->   List[Any]:





       '''simple docstring'''






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





  @classmethod
  def 						A  (		cls				:     Optional[int]							, _a				:     int							, _a				:     List[str]							, _a				:     Optional[Any]=None							, **_a				:     int			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =kwargs.pop('config'							, __UpperCAmelCase			) or RagConfig.from_pretrained(__UpperCAmelCase							, **__UpperCAmelCase			)
       _SCREAMING_SNAKE_CASE							   =RagTokenizer.from_pretrained(__UpperCAmelCase							, config=__UpperCAmelCase			)
       _SCREAMING_SNAKE_CASE							   =rag_tokenizer.question_encoder
       _SCREAMING_SNAKE_CASE							   =rag_tokenizer.generator
       if indexed_dataset is not None:
            _SCREAMING_SNAKE_CASE							   ='custom'
            _SCREAMING_SNAKE_CASE							   =CustomHFIndex(config.retrieval_vector_size							, __UpperCAmelCase			)
       else:
            _SCREAMING_SNAKE_CASE							   =cls._build_index(__UpperCAmelCase			)
       return cls(
           __UpperCAmelCase							, question_encoder_tokenizer=__UpperCAmelCase							, generator_tokenizer=__UpperCAmelCase							, retrieval_workers=__UpperCAmelCase							, index=__UpperCAmelCase							, )



'''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


lowerCamelCase			:				Any 			=					logging.get_logger(__name__)

lowerCamelCase			:				int 			=					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}







class 							A__   (	A__  ):
  A__					=    'data2vec-vision'





  def __init__(		self				:     str							, _a				:     Optional[int]=768							, _a				:     List[str]=12							, _a				:     Any=12							, _a				:     Tuple=3072							, _a				:     Optional[Any]="gelu"							, _a				:     Tuple=0.0							, _a				:     str=0.0							, _a				:     Dict=0.02							, _a				:     int=1e-12							, _a				:     List[str]=224							, _a				:     str=16							, _a				:     Dict=3							, _a				:     int=False							, _a				:     List[str]=False							, _a				:     str=False							, _a				:     str=False							, _a				:     Optional[int]=0.1							, _a				:     List[str]=0.1							, _a				:     Dict=True							, _a				:     str=[3, 5, 7, 11]							, _a				:     List[Any]=[1, 2, 3, 6]							, _a				:     Any=True							, _a				:     Optional[Any]=0.4							, _a				:     str=256							, _a				:     Dict=1							, _a				:     List[str]=False							, _a				:     List[Any]=255							, **_a				:     Any							, )     ->   Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a			)

       _SCREAMING_SNAKE_CASE							   =hidden_size
       _SCREAMING_SNAKE_CASE							   =num_hidden_layers
       _SCREAMING_SNAKE_CASE							   =num_attention_heads
       _SCREAMING_SNAKE_CASE							   =intermediate_size
       _SCREAMING_SNAKE_CASE							   =hidden_act
       _SCREAMING_SNAKE_CASE							   =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							   =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							   =initializer_range
       _SCREAMING_SNAKE_CASE							   =layer_norm_eps

       _SCREAMING_SNAKE_CASE							   =image_size
       _SCREAMING_SNAKE_CASE							   =patch_size
       _SCREAMING_SNAKE_CASE							   =num_channels
       _SCREAMING_SNAKE_CASE							   =use_mask_token
       _SCREAMING_SNAKE_CASE							   =use_absolute_position_embeddings
       _SCREAMING_SNAKE_CASE							   =use_relative_position_bias
       _SCREAMING_SNAKE_CASE							   =use_shared_relative_position_bias
       _SCREAMING_SNAKE_CASE							   =layer_scale_init_value
       _SCREAMING_SNAKE_CASE							   =drop_path_rate
       _SCREAMING_SNAKE_CASE							   =use_mean_pooling
       # decode head attributes (semantic segmentation)
       _SCREAMING_SNAKE_CASE							   =out_indices
       _SCREAMING_SNAKE_CASE							   =pool_scales
       # auxiliary head attributes (semantic segmentation)
       _SCREAMING_SNAKE_CASE							   =use_auxiliary_head
       _SCREAMING_SNAKE_CASE							   =auxiliary_loss_weight
       _SCREAMING_SNAKE_CASE							   =auxiliary_channels
       _SCREAMING_SNAKE_CASE							   =auxiliary_num_convs
       _SCREAMING_SNAKE_CASE							   =auxiliary_concat_input
       _SCREAMING_SNAKE_CASE							   =semantic_loss_ignore_index







class 							A__   (	A__  ):
  A__					=    version.parse('1.11'  )





  @property
  def 						A  (		self				:     str			)     ->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     List[Any]			)     ->   float:





       '''simple docstring'''






       return 1e-4
