
"""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:
	snake_case					  =						None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	snake_case					  =						None
	snake_case					  =						None
	snake_case					  =						None

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):




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




@require_distributed_retrieval
class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE           =      8

									# DPR tok
									SCREAMING_SNAKE_CASE           =      [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									SCREAMING_SNAKE_CASE           =      os.path.join(self.tmpdirname						,      'dpr_tokenizer'     )
									os.makedirs(lowercase__						,      exist_ok=lowercase__     )
									SCREAMING_SNAKE_CASE           =      os.path.join(lowercase__						,      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
									SCREAMING_SNAKE_CASE           =      [
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '<unk>',
									]
									SCREAMING_SNAKE_CASE           =      dict(zip(lowercase__						,      range(len(lowercase__     )     )     )     )
									SCREAMING_SNAKE_CASE           =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									SCREAMING_SNAKE_CASE           =      {'unk_token': '<unk>'}

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


				def 				A   (							self     )       ->       DPRQuestionEncoderTokenizer:
									"""simple docstring"""







									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,      'dpr_tokenizer'     )     )


				def 				A   (							self     )       ->       BartTokenizer:
									"""simple docstring"""







									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,      'bart_tokenizer'     )     )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									shutil.rmtree(self.tmpdirname     )


				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      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 				A   (							self						,      lowercase__						,      lowercase__=12345     )       ->       RagPyTorchDistributedRetriever:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE           =      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:
														SCREAMING_SNAKE_CASE           =      dataset
														SCREAMING_SNAKE_CASE           =      RagPyTorchDistributedRetriever(
														    lowercase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
														if init_retrieval:
																			retriever.init_retrieval(lowercase__     )
									return retriever


				def 				A   (							self						,      lowercase__     )       ->       RagRayDistributedRetriever:
									"""simple docstring"""







									ray.init(local_mode=lowercase__     )
									SCREAMING_SNAKE_CASE           =      RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      )
									SCREAMING_SNAKE_CASE           =      ray.remote(lowercase__     )
									SCREAMING_SNAKE_CASE           =      [remote_cls.remote() for _ in range(1     )]
									with patch('transformers.models.rag.retrieval_rag.load_dataset'     ) as mock_load_dataset:
														SCREAMING_SNAKE_CASE           =      self.get_dummy_dataset()
														SCREAMING_SNAKE_CASE           =      RagRayDistributedRetriever(
														    lowercase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowercase__						,      )
														if init_retrieval:
																			retriever.init_retrieval()
									return retriever


				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__=12345     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE           =      RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name='custom'						,      )
									if from_disk:
														SCREAMING_SNAKE_CASE           =      os.path.join(self.tmpdirname						,      'dataset'     )
														SCREAMING_SNAKE_CASE           =      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
														SCREAMING_SNAKE_CASE           =      RagPyTorchDistributedRetriever(
														    lowercase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
									else:
														SCREAMING_SNAKE_CASE           =      RagPyTorchDistributedRetriever(
														    lowercase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      index=CustomHFIndex(config.retrieval_vector_size						,      lowercase__     )						,      )
									if init_retrieval:
														retriever.init_retrieval(lowercase__     )
									return retriever


				def 				A   (							self						,      lowercase__						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									ray.init(local_mode=lowercase__     )
									SCREAMING_SNAKE_CASE           =      self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE           =      RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name='custom'						,      )
									SCREAMING_SNAKE_CASE           =      ray.remote(lowercase__     )
									SCREAMING_SNAKE_CASE           =      [remote_cls.remote() for _ in range(1     )]
									if from_disk:
														SCREAMING_SNAKE_CASE           =      os.path.join(self.tmpdirname						,      'dataset'     )
														SCREAMING_SNAKE_CASE           =      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
														SCREAMING_SNAKE_CASE           =      RagRayDistributedRetriever(
														    lowercase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowercase__						,      index=CustomHFIndex.load_from_disk(
														        vector_size=config.retrieval_vector_size						,      dataset_path=config.passages_path						,      index_path=config.index_path						,      )						,      )
									else:
														SCREAMING_SNAKE_CASE           =      RagRayDistributedRetriever(
														    lowercase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowercase__						,      index=CustomHFIndex(config.retrieval_vector_size						,      lowercase__     )						,      )
									if init_retrieval:
														retriever.init_retrieval()
									return retriever


				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      retriever.retrieve(lowercase__						,      n_docs=lowercase__     )
									self.assertEqual(retrieved_doc_embeds.shape						,      (2, n_docs, self.retrieval_vector_size)     )
									self.assertEqual(len(lowercase__     )						,      2     )
									self.assertEqual(sorted(doc_dicts[0]     )						,      ['embeddings', 'id', 'text', 'title']     )
									self.assertEqual(len(doc_dicts[0]['id']     )						,      lowercase__     )
									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 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      1
									SCREAMING_SNAKE_CASE           =      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=lowercase__     )						,      lowercase__						,      lowercase__     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      1
									SCREAMING_SNAKE_CASE           =      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=lowercase__						,      from_disk=lowercase__     )						,      lowercase__						,      lowercase__						,      )


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      1
									SCREAMING_SNAKE_CASE           =      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=lowercase__						,      from_disk=lowercase__     )						,      lowercase__						,      lowercase__						,      )


				@require_ray
				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      1
									SCREAMING_SNAKE_CASE           =      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=lowercase__     )						,      lowercase__						,      lowercase__     )
									ray.shutdown()


				@require_ray
				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      1
									SCREAMING_SNAKE_CASE           =      np.array(
									    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]						,      dtype=np.floataa     )
									with self.assertRaises(lowercase__     ):
														self.distributed_retriever_check(
														    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowercase__						,      from_disk=lowercase__     )						,      lowercase__						,      lowercase__						,      )
									ray.shutdown()




				@require_ray
				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      1
									SCREAMING_SNAKE_CASE           =      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=lowercase__						,      from_disk=lowercase__     )						,      lowercase__						,      lowercase__     )
									ray.shutdown()







'''simple docstring'''


from typing import Callable




def       A							(      _UpperCAmelCase   :				Callable					)  ->				Dict:


  '''simple docstring'''




  def wrapper(_UpperCAmelCase   :				Optional[Any]					):
    __lowerCAmelCase		:    List[Any]			     =  function_with_docstring.__doc__
    return target_function

  return wrapper





