






"""simple docstring"""





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
  A__       :			List[Any]=						None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  A__       :			Dict=						None
  A__       :			List[Any]=						None
  A__       :			str=						None







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Optional[int]:
    """simple docstring"""

    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        UpperCamelCase__								=     unittest.skip('test requires Datasets, Faiss, psutil'       )(SCREAMING_SNAKE_CASE       )
    return test_case





@require_distributed_retrieval
class  __lowerCamelCase     (	_a	):

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       UpperCamelCase__								=     tempfile.mkdtemp()
       UpperCamelCase__								=     8

       # DPR tok
       UpperCamelCase__								=     [
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'dpr_tokenizer'   )
       os.makedirs(snake_case_			,					exist_ok=snake_case_   )
       UpperCamelCase__								=     os.path.join(snake_case_			,					DPR_VOCAB_FILES_NAMES['vocab_file']   )
       with open(self.vocab_file			,					'w'			,					encoding='utf-8'   ) as vocab_writer:
           vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

       # BART tok
       UpperCamelCase__								=     [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '<unk>',
       ]
       UpperCamelCase__								=     dict(zip(snake_case_			,					range(len(snake_case_   )   )   )   )
       UpperCamelCase__								=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       UpperCamelCase__								=     {'unk_token': '<unk>'}

       UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'bart_tokenizer'   )
       os.makedirs(snake_case_			,					exist_ok=snake_case_   )
       UpperCamelCase__								=     os.path.join(snake_case_			,					BART_VOCAB_FILES_NAMES['vocab_file']   )
       UpperCamelCase__								=     os.path.join(snake_case_			,					BART_VOCAB_FILES_NAMES['merges_file']   )
       with open(self.vocab_file			,					'w'			,					encoding='utf-8'   ) as fp:
           fp.write(json.dumps(snake_case_   ) + '\n'   )
       with open(self.merges_file			,					'w'			,					encoding='utf-8'   ) as fp:
           fp.write('\n'.join(snake_case_   )   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							DPRQuestionEncoderTokenizer:
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,					'dpr_tokenizer'   )   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							BartTokenizer:
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,					'bart_tokenizer'   )   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       shutil.rmtree(self.tmpdirname   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__								=     Dataset.from_dict(
           {
               'id': ['0', '1'],
               'text': ['foo', 'bar'],
               'title': ['Foo', 'Bar'],
               'embeddings': [np.ones(self.retrieval_vector_size   ), 2 * np.ones(self.retrieval_vector_size   )],
           }   )
       dataset.add_faiss_index('embeddings'			,					string_factory='Flat'			,					metric_type=faiss.METRIC_INNER_PRODUCT   )
       return dataset

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=1_2345   )		->							RagPyTorchDistributedRetriever:
       UpperCamelCase__								=     self.get_dummy_dataset()
       UpperCamelCase__								=     RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					)
       with patch('transformers.models.rag.retrieval_rag.load_dataset'   ) as mock_load_dataset:
           UpperCamelCase__								=     dataset
           UpperCamelCase__								=     RagPyTorchDistributedRetriever(
               snake_case_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					)
           if init_retrieval:
               retriever.init_retrieval(snake_case_   )
       return retriever

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							RagRayDistributedRetriever:
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=snake_case_   )
       UpperCamelCase__								=     RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					)
       UpperCamelCase__								=     ray.remote(snake_case_   )
       UpperCamelCase__								=     [remote_cls.remote() for _ in range(1   )]
       with patch('transformers.models.rag.retrieval_rag.load_dataset'   ) as mock_load_dataset:
           UpperCamelCase__								=     self.get_dummy_dataset()
           UpperCamelCase__								=     RagRayDistributedRetriever(
               snake_case_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					retrieval_workers=snake_case_			,					)
           if init_retrieval:
               retriever.init_retrieval()
       return retriever

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_=1_2345   )		->							Tuple:
       UpperCamelCase__								=     self.get_dummy_dataset()
       UpperCamelCase__								=     RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					index_name='custom'			,					)
       if from_disk:
           UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'dataset'   )
           UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'index.faiss'   )
           dataset.get_index('embeddings'   ).save(os.path.join(self.tmpdirname			,					'index.faiss'   )   )
           dataset.drop_index('embeddings'   )
           dataset.save_to_disk(os.path.join(self.tmpdirname			,					'dataset'   )   )
           del dataset
           UpperCamelCase__								=     RagPyTorchDistributedRetriever(
               snake_case_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					)
       else:
           UpperCamelCase__								=     RagPyTorchDistributedRetriever(
               snake_case_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					index=CustomHFIndex(config.retrieval_vector_size			,					snake_case_   )			,					)
       if init_retrieval:
           retriever.init_retrieval(snake_case_   )
       return retriever

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							int:
       # Have to run in local mode because sys.path modifications at top of
       # file are not propogated to remote workers.
       # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
       ray.init(local_mode=snake_case_   )
       UpperCamelCase__								=     self.get_dummy_dataset()
       UpperCamelCase__								=     RagConfig(
           retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					index_name='custom'			,					)
       UpperCamelCase__								=     ray.remote(snake_case_   )
       UpperCamelCase__								=     [remote_cls.remote() for _ in range(1   )]
       if from_disk:
           UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'dataset'   )
           UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'index.faiss'   )
           dataset.get_index('embeddings'   ).save(os.path.join(self.tmpdirname			,					'index.faiss'   )   )
           dataset.drop_index('embeddings'   )
           dataset.save_to_disk(os.path.join(self.tmpdirname			,					'dataset'   )   )
           del dataset
           UpperCamelCase__								=     RagRayDistributedRetriever(
               snake_case_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					retrieval_workers=snake_case_			,					index=CustomHFIndex.load_from_disk(
                   vector_size=config.retrieval_vector_size			,					dataset_path=config.passages_path			,					index_path=config.index_path			,					)			,					)
       else:
           UpperCamelCase__								=     RagRayDistributedRetriever(
               snake_case_			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					retrieval_workers=snake_case_			,					index=CustomHFIndex(config.retrieval_vector_size			,					snake_case_   )			,					)
       if init_retrieval:
           retriever.init_retrieval()
       return retriever

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							None:
       UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     retriever.retrieve(snake_case_			,					n_docs=snake_case_   )
       self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size)   )
       self.assertEqual(len(snake_case_   )			,					2   )
       self.assertEqual(sorted(doc_dicts[0]   )			,					['embeddings', 'id', 'text', 'title']   )
       self.assertEqual(len(doc_dicts[0]['id']   )			,					snake_case_   )
       self.assertEqual(doc_dicts[0]['id'][0]			,					'1'   )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]['id'][0]			,					'0'   )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()			,					[[1], [0]]   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       UpperCamelCase__								=     1
       UpperCamelCase__								=     np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_pytorch_distributed_retriever(init_retrieval=snake_case_   )			,					snake_case_			,					snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__								=     1
       UpperCamelCase__								=     np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_			,					from_disk=snake_case_   )			,					snake_case_			,					snake_case_			,					)

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       UpperCamelCase__								=     1
       UpperCamelCase__								=     np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_			,					from_disk=snake_case_   )			,					snake_case_			,					snake_case_			,					)

   @require_ray
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       UpperCamelCase__								=     1
       UpperCamelCase__								=     np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_ray_distributed_retriever(init_retrieval=snake_case_   )			,					snake_case_			,					snake_case_   )
       ray.shutdown()

   @require_ray
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__								=     1
       UpperCamelCase__								=     np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
       with self.assertRaises(snake_case_   ):
           self.distributed_retriever_check(
               self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_			,					from_disk=snake_case_   )			,					snake_case_			,					snake_case_			,					)
       ray.shutdown()







   @require_ray
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       UpperCamelCase__								=     1
       UpperCamelCase__								=     np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )

       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_			,					from_disk=snake_case_   )			,					snake_case_			,					snake_case_   )
       ray.shutdown()












"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE = 10_00       )       ->						str:
    """simple docstring"""

    if (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (digit_position <= 0):
        raise ValueError('Digit position must be a positive integer'       )
    elif (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (precision < 0):
        raise ValueError('Precision must be a nonnegative integer'       )

    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    UpperCamelCase__								=     (
        4 * _subsum(SCREAMING_SNAKE_CASE			,						1			,						SCREAMING_SNAKE_CASE       )
        - 2 * _subsum(SCREAMING_SNAKE_CASE			,						4			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						5			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						6			,						SCREAMING_SNAKE_CASE       )
    )

    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16       )       )[2:]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						float:
    """simple docstring"""

    UpperCamelCase__								=     0.0
    for sum_index in range(digit_pos_to_extract + precision       ):
        UpperCamelCase__								=     8 * sum_index + denominator_addend
        if sum_index < digit_pos_to_extract:
            # if the exponential term is an integer and we mod it by the denominator
            # before dividing, only the integer part of the sum will change;
            # the fractional part will not
            UpperCamelCase__								=     pow(
                16			,						digit_pos_to_extract - 1 - sum_index			,						SCREAMING_SNAKE_CASE       )
        else:
            UpperCamelCase__								=     pow(16			,						digit_pos_to_extract - 1 - sum_index       )
        total += exponential_term / denominator
    return total


if __name__ == "__main__":
  import doctest

  doctest.testmod()




