
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A				=logging.get_logger(__name__)




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			question_encoder
					lowerCamelCase_						=			generator
					lowerCamelCase_						=			self.question_encoder



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Optional[int]:
					if os.path.isfile(lowercase ):
									raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
					os.makedirs(lowercase						,  exist_ok=lowercase )
					lowerCamelCase_						=			os.path.join(lowercase						,  "question_encoder_tokenizer" )
					lowerCamelCase_						=			os.path.join(lowercase						,  "generator_tokenizer" )
					self.question_encoder.save_pretrained(lowercase )
					self.generator.save_pretrained(lowercase )



	@classmethod
	def     SCREAMING_SNAKE_CASE_(    cls						,  lowercase						,  **lowercase )				->       Union[str, Any]:
					# dynamically import AutoTokenizer
					from ..auto.tokenization_auto import AutoTokenizer

					lowerCamelCase_						=			kwargs.pop("config"						,  lowercase )

					if config is None:
									lowerCamelCase_						=			RagConfig.from_pretrained(lowercase )

					lowerCamelCase_						=			AutoTokenizer.from_pretrained(
					    lowercase						,  config=config.question_encoder						,  subfolder="question_encoder_tokenizer" )
					lowerCamelCase_						=			AutoTokenizer.from_pretrained(
					    lowercase						,  config=config.generator						,  subfolder="generator_tokenizer" )
					return cls(question_encoder=lowercase						,  generator=lowercase )



	def __call__(    self						,  *lowercase						,  **lowercase )				->       Dict:
					return self.current_tokenizer(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       List[Any]:
					return self.generator.batch_decode(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       List[str]:
					return self.generator.decode(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			self.question_encoder



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			self.generator



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = "longest"						,  lowercase = None						,  lowercase = True						,  **lowercase						,  )				->       BatchEncoding:
					warnings.warn(
					    "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
					    "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
					    "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
					    "details"						,  lowercase						,  )
					if max_length is None:
									lowerCamelCase_						=			self.current_tokenizer.model_max_length
					lowerCamelCase_						=			self(
					    lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  max_length=lowercase						,  padding=lowercase						,  truncation=lowercase						,  **lowercase						,  )
					if tgt_texts is None:
									return model_inputs
					# Process tgt_texts
					if max_target_length is None:
									lowerCamelCase_						=			self.current_tokenizer.model_max_length
					lowerCamelCase_						=			self(
					    text_target=lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  padding=lowercase						,  max_length=lowercase						,  truncation=lowercase						,  **lowercase						,  )
					lowerCamelCase_						=			labels["input_ids"]
					return model_inputs




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


__A				=pytest.mark.integration




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			Dataset.from_dict({"filename": ["my_name-train" + "_" + str(lowercase ) for x in np.arange(30 ).tolist()]} )
					return dset



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					import faiss

					lowerCamelCase_						=			self._create_dummy_dataset()
					lowerCamelCase_						=			dset.map(
					    lambda lowercase						,  lowercase : {"vecs": i * np.ones(5						,  dtype=np.floataa )}						,  with_indices=lowercase						,  keep_in_memory=lowercase )
					lowerCamelCase_						=			dset.add_faiss_index("vecs"						,  batch_size=100						,  metric_type=faiss.METRIC_INNER_PRODUCT )
					lowerCamelCase_	,    lowerCamelCase_						=			dset.get_nearest_examples("vecs"						,  np.ones(5						,  dtype=np.floataa ) )
					self.assertEqual(examples["filename"][0]						,  "my_name-train_29" )
					dset.drop_index("vecs" )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					import faiss

					lowerCamelCase_						=			self._create_dummy_dataset()
					dset.add_faiss_index_from_external_arrays(
					    external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1						,  1 )						,  index_name="vecs"						,  batch_size=100						,  metric_type=faiss.METRIC_INNER_PRODUCT						,  )
					lowerCamelCase_	,    lowerCamelCase_						=			dset.get_nearest_examples("vecs"						,  np.ones(5						,  dtype=np.floataa ) )
					self.assertEqual(examples["filename"][0]						,  "my_name-train_29" )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					import faiss

					lowerCamelCase_						=			self._create_dummy_dataset()
					dset.add_faiss_index_from_external_arrays(
					    external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1						,  1 )						,  index_name="vecs"						,  metric_type=faiss.METRIC_INNER_PRODUCT						,  )

					# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
					# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
					# see https://bugs.python.org/issue14243 and
					# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
					with tempfile.NamedTemporaryFile(delete=lowercase ) as tmp_file:
									dset.save_faiss_index("vecs"						,  tmp_file.name )
									dset.load_faiss_index("vecs2"						,  tmp_file.name )
					os.unlink(tmp_file.name )

					lowerCamelCase_	,    lowerCamelCase_						=			dset.get_nearest_examples("vecs2"						,  np.ones(5						,  dtype=np.floataa ) )
					self.assertEqual(examples["filename"][0]						,  "my_name-train_29" )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			self._create_dummy_dataset()
					dset.add_faiss_index_from_external_arrays(
					    external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1						,  1 )						,  index_name="vecs" )
					dset.drop_index("vecs" )
					self.assertRaises(lowercase						,  partial(dset.get_nearest_examples						,  "vecs2"						,  np.ones(5						,  dtype=np.floataa ) ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					from elasticsearch import Elasticsearch

					lowerCamelCase_						=			self._create_dummy_dataset()
					with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
					    "elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
									lowerCamelCase_						=			{"acknowledged": True}
									mocked_bulk.return_value([(True, None)] * 30 )
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 29}]}}
									lowerCamelCase_						=			Elasticsearch()

									dset.add_elasticsearch_index("filename"						,  es_client=lowercase )
									lowerCamelCase_	,    lowerCamelCase_						=			dset.get_nearest_examples("filename"						,  "my_name-train_29" )
									self.assertEqual(examples["filename"][0]						,  "my_name-train_29" )




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					import faiss

					lowerCamelCase_						=			FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )

					# add vectors
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsNotNone(index.faiss_index )
					self.assertEqual(index.faiss_index.ntotal						,  5 )
					index.add_vectors(np.zeros((5, 5)						,  dtype=np.floataa ) )
					self.assertEqual(index.faiss_index.ntotal						,  10 )

					# single query
					lowerCamelCase_						=			np.zeros(5						,  dtype=np.floataa )
					lowerCamelCase_						=			1
					lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
					self.assertRaises(lowercase						,  index.search						,  query.reshape(-1						,  1 ) )
					self.assertGreater(scores[0]						,  0 )
					self.assertEqual(indices[0]						,  1 )

					# batched queries
					lowerCamelCase_						=			np.eye(5						,  dtype=np.floataa )[::-1]
					lowerCamelCase_	,    lowerCamelCase_						=			index.search_batch(lowercase )
					self.assertRaises(lowercase						,  index.search_batch						,  queries[0] )
					lowerCamelCase_						=			[scores[0] for scores in total_scores]
					lowerCamelCase_						=			[indices[0] for indices in total_indices]
					self.assertGreater(np.min(lowercase )						,  0 )
					self.assertListEqual([4, 3, 2, 1, 0]						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					import faiss

					lowerCamelCase_						=			FaissIndex(string_factory="Flat" )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexFlat )
					lowerCamelCase_						=			FaissIndex(string_factory="LSH" )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexLSH )
					with self.assertRaises(lowercase ):
									lowerCamelCase_						=			FaissIndex(string_factory="Flat"						,  custom_index=faiss.IndexFlat(5 ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					import faiss

					lowerCamelCase_						=			faiss.IndexFlat(5 )
					lowerCamelCase_						=			FaissIndex(custom_index=lowercase )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexFlat )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					import faiss

					lowerCamelCase_						=			FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )

					# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
					# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
					# see https://bugs.python.org/issue14243 and
					# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
					with tempfile.NamedTemporaryFile(delete=lowercase ) as tmp_file:
									index.save(tmp_file.name )
									lowerCamelCase_						=			FaissIndex.load(tmp_file.name )
					os.unlink(tmp_file.name )

					lowerCamelCase_						=			np.zeros(5						,  dtype=np.floataa )
					lowerCamelCase_						=			1
					lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
					self.assertGreater(scores[0]						,  0 )
					self.assertEqual(indices[0]						,  1 )





@require_faiss
def 			lowerCamelCase_ (				lowerCamelCase__    ):
				import faiss

				lowerCamelCase_						=			FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT    )
				index.add_vectors(np.eye(5	, dtype=np.floataa    )    )

				lowerCamelCase_						=			"index.faiss"
				lowerCamelCase_						=			F'mock://{index_name}'
				index.save(lowerCamelCase__	, storage_options=mockfs.storage_options    )
				lowerCamelCase_						=			FaissIndex.load(lowerCamelCase__	, storage_options=mockfs.storage_options    )

				lowerCamelCase_						=			np.zeros(5	, dtype=np.floataa    )
				lowerCamelCase_						=			1
				lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowerCamelCase__    )
				assert scores[0] > 0
				assert indices[0] == 1





@require_elasticsearch
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					from elasticsearch import Elasticsearch

					with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
					    "elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
									lowerCamelCase_						=			Elasticsearch()
									lowerCamelCase_						=			{"acknowledged": True}
									lowerCamelCase_						=			ElasticSearchIndex(es_client=lowercase )
									mocked_bulk.return_value([(True, None)] * 3 )
									index.add_documents(["foo", "bar", "foobar"] )

									# single query
									lowerCamelCase_						=			"foo"
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
									self.assertEqual(scores[0]						,  1 )
									self.assertEqual(indices[0]						,  0 )

									# single query with timeout
									lowerCamelCase_						=			"foo"
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase						,  request_timeout=30 )
									self.assertEqual(scores[0]						,  1 )
									self.assertEqual(indices[0]						,  0 )

									# batched queries
									lowerCamelCase_						=			["foo", "bar", "foobar"]
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search_batch(lowercase )
									lowerCamelCase_						=			[scores[0] for scores in total_scores]
									lowerCamelCase_						=			[indices[0] for indices in total_indices]
									self.assertGreater(np.min(lowercase )						,  0 )
									self.assertListEqual([1, 1, 1]						,  lowercase )

									# batched queries with timeout
									lowerCamelCase_						=			["foo", "bar", "foobar"]
									lowerCamelCase_						=			{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
									lowerCamelCase_	,    lowerCamelCase_						=			index.search_batch(lowercase						,  request_timeout=30 )
									lowerCamelCase_						=			[scores[0] for scores in total_scores]
									lowerCamelCase_						=			[indices[0] for indices in total_indices]
									self.assertGreater(np.min(lowercase )						,  0 )
									self.assertListEqual([1, 1, 1]						,  lowercase )


