






"""simple docstring"""


import logging
import random

import ray

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


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






class __a :



  def __init__(self	:		int )		->				Dict:
   __A						:							List[str]      =						False



  def     A_					(self	:		Dict					,      lowercase_	:		List[Any]					,      lowercase_	:		int					,      lowercase_	:		Optional[int]					,      lowercase_	:		int )		->				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     A_					(self	:		List[str] )		->				List[Any]:
   self.retriever.index.init_index()



  def     A_					(self	:		Dict )		->				List[Any]:
   # delete the old self.retriever object before assigning the new index
   del self.retriever
   __A						:							Dict      =						False



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		str )		->				Optional[int]:
   __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					):



  def __init__(self	:		int					,      lowercase_	:		List[Any]					,      lowercase_	:		int					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		int					,      lowercase_	:		List[str]=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     A_					(self	:		Tuple )		->				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     A_					(self	:		Any					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		List[Any] )		->				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						:							Any      =						ray.get(
        random_worker.retrieve.remote(__UpperCAmelCase					,      __UpperCAmelCase ) )
   else:
    __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     A_					(cls	:		int					,      lowercase_	:		List[Any]					,      lowercase_	:		int=None					,      **lowercase_	:		Dict )		->				int:
   return super(__UpperCAmelCase					,      cls ).get_tokenizers(__UpperCAmelCase					,      __UpperCAmelCase					,      **__UpperCAmelCase )



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









"""simple docstring"""






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= "\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

SCREAMING_SNAKE_CASE__				:  str						= "\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

SCREAMING_SNAKE_CASE__				:  Any						= "\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION   )
class 	A_       (     datasets.Metric   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     return datasets.MetricInfo(
         description=_DESCRIPTION  ,					citation=_CITATION  ,					homepage='https://github.com/chakki-works/seqeval'  ,					inputs_description=_KWARGS_DESCRIPTION  ,					features=datasets.Features(
             {
                 'predictions': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
                 'references': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
             }						)  ,					codebase_urls=['https://github.com/chakki-works/seqeval']  ,					reference_urls=['https://github.com/chakki-works/seqeval']  ,					)






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "warn"  ,					)				->					List[Any]:
     if scheme is not None:
        try:
           a   :	str						=    importlib.import_module('seqeval.scheme'						)
           a   :	List[str]						=    getattr(__UpperCAmelCase  ,					__UpperCAmelCase						)
        except AttributeError:
           raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'						)
     a   :	str						=    classification_report(
         y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase  ,					suffix=__UpperCAmelCase  ,					output_dict=__UpperCAmelCase  ,					scheme=__UpperCAmelCase  ,					mode=__UpperCAmelCase  ,					sample_weight=__UpperCAmelCase  ,					zero_division=__UpperCAmelCase  ,					)
     report.pop('macro avg'						)
     report.pop('weighted avg'						)
     a   :	Optional[Any]						=    report.pop('micro avg'						)

     a   :	Optional[int]						=    {
         type_name: {
             'precision': score['precision'],
             'recall': score['recall'],
             'f1': score['f1-score'],
             'number': score['support'],
         }
         for type_name, score in report.items()
     }
     a   :	int						=    overall_score['precision']
     a   :	int						=    overall_score['recall']
     a   :	int						=    overall_score['f1-score']
     a   :	List[str]						=    accuracy_score(y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase						)

     return scores





