




'''simple docstring'''



import logging
import random

import ray

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


UpperCAmelCase_       :  Dict      =		logging.getLogger(__name__)





class     lowerCAmelCase   :


       def __init__(   self  )      ->  Union[str, Any]:



            '''simple docstring'''




            __snake_case    					=  False


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




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


       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            self.retriever.index.init_index()


       def       lowerCAmelCase      (   self  )      ->  str:



            '''simple docstring'''




            del self.retriever
            __snake_case    					=  False




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            __snake_case    					=  self.retriever.index.get_doc_dicts(__SCREAMING_SNAKE_CASE  )
            return doc_ids, retrieved_doc_embeds, doc_dicts





class     lowerCAmelCase   ( a_):


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None  )      ->  Optional[int]:



            '''simple docstring'''




            if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE  ) > 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__(
                __SCREAMING_SNAKE_CASE    ,  question_encoder_tokenizer=__SCREAMING_SNAKE_CASE    ,  generator_tokenizer=__SCREAMING_SNAKE_CASE    ,  index=__SCREAMING_SNAKE_CASE    ,  init_retrieval=__SCREAMING_SNAKE_CASE    ,  )

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


       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''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    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  List[Any]:



            '''simple docstring'''




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


       @classmethod
       def       lowerCAmelCase      (   cls    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None    ,  **__SCREAMING_SNAKE_CASE  )      ->  List[Any]:



            '''simple docstring'''




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


       @classmethod
       def       lowerCAmelCase      (   cls    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=None    ,  **__SCREAMING_SNAKE_CASE  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  kwargs.pop('''config'''    ,  __SCREAMING_SNAKE_CASE  ) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    ,  config=__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  rag_tokenizer.question_encoder
            __snake_case    					=  rag_tokenizer.generator

            if indexed_dataset is not None:
                 __snake_case    					=  'custom'
                 __snake_case    					=  CustomHFIndex(config.retrieval_vector_size    ,  __SCREAMING_SNAKE_CASE  )
            else:
                 __snake_case    					=  cls._build_index(__SCREAMING_SNAKE_CASE  )

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




       def       lowerCAmelCase      (   self  )      ->  Tuple:



            '''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
            __snake_case    					=  self._build_index(self.config  )

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





'''simple docstring'''



from datetime import date

import requests

UpperCAmelCase_       :  str      =		'''https://api.carbonintensity.org.uk/intensity'''







def 			_UpperCamelCase ()->   str:




     '''simple docstring'''





     __snake_case    					=  requests.get(_lowerCamelCase	).json()['''data'''][0]
     return last_half_hour["intensity"]["actual"]







def 			_UpperCamelCase (_lowerCamelCase							:     Dict   ,	_lowerCamelCase							:     List[Any]	)->   list:




     '''simple docstring'''





     return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
     for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
          print('''from {from} to {to}: {intensity[actual]}'''.format(**entry))
     print(F"""{fetch_last_half_hour() = }""")






