
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


UpperCamelCase_						=						pytest.mark.integration

@require_faiss
class      _SCREAMING_SNAKE_CASE				(		UpperCAmelCase__				):





				def 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								A  :	Any				=					Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__lowerCAmelCase				) for x in np.arange(30				).tolist()]}				)
								return dset





				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								import faiss

								A  :	Any				=					self._create_dummy_dataset()
								A  :	Dict				=					dset.map(
								    lambda snake_case_       ,   snake_case_				: {"vecs": i * np.ones(5       ,   dtype=np.floataa				)}       ,   with_indices=__lowerCAmelCase       ,   keep_in_memory=__lowerCAmelCase				)
								A  :	Dict				=					dset.add_faiss_index('''vecs'''       ,   batch_size=100       ,   metric_type=faiss.METRIC_INNER_PRODUCT				)
								A			,							A  :	Dict				=					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 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								import faiss

								A  :	int				=					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       ,   )
								A			,							A  :	Optional[int]				=					dset.get_nearest_examples('''vecs'''       ,   np.ones(5       ,   dtype=np.floataa				)				)
								self.assertEqual(examples['''filename'''][0]       ,   '''my_name-train_29'''				)





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								import faiss

								A  :	List[str]				=					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=__lowerCAmelCase				) as tmp_file:
												dset.save_faiss_index('''vecs'''       ,   tmp_file.name				)
												dset.load_faiss_index('''vecs2'''       ,   tmp_file.name				)
								os.unlink(tmp_file.name				)

								A			,							A  :	Union[str, Any]				=					dset.get_nearest_examples('''vecs2'''       ,   np.ones(5       ,   dtype=np.floataa				)				)
								self.assertEqual(examples['''filename'''][0]       ,   '''my_name-train_29'''				)





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					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(__lowerCAmelCase       ,   partial(dset.get_nearest_examples       ,   '''vecs2'''       ,   np.ones(5       ,   dtype=np.floataa				)				)				)






				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								from elasticsearch import Elasticsearch

								A  :	str				=					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:
												A  :	Optional[Any]				=					{'''acknowledged''': True}
												mocked_bulk.return_value([(True, None)] * 30				)
												A  :	Dict				=					{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
												A  :	Optional[Any]				=					Elasticsearch()

												dset.add_elasticsearch_index('''filename'''       ,   es_client=__lowerCAmelCase				)
												A			,							A  :	str				=					dset.get_nearest_examples('''filename'''       ,   '''my_name-train_29'''				)
												self.assertEqual(examples['''filename'''][0]       ,   '''my_name-train_29'''				)



@require_faiss
class      _SCREAMING_SNAKE_CASE				(		UpperCAmelCase__				):





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								import faiss

								A  :	Optional[Any]				=					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
								A  :	Optional[int]				=					np.zeros(5       ,   dtype=np.floataa				)
								A  :	Union[str, Any]				=					1
								A			,							A  :	Tuple				=					index.search(__lowerCAmelCase				)
								self.assertRaises(__lowerCAmelCase       ,   index.search       ,   query.reshape(-1       ,   1				)				)
								self.assertGreater(scores[0]       ,   0				)
								self.assertEqual(indices[0]       ,   1				)

								# batched queries
								A  :	int				=					np.eye(5       ,   dtype=np.floataa				)[::-1]
								A			,							A  :	Tuple				=					index.search_batch(__lowerCAmelCase				)
								self.assertRaises(__lowerCAmelCase       ,   index.search_batch       ,   queries[0]				)
								A  :	Optional[int]				=					[scores[0] for scores in total_scores]
								A  :	List[str]				=					[indices[0] for indices in total_indices]
								self.assertGreater(np.min(__lowerCAmelCase				)       ,   0				)
								self.assertListEqual([4, 3, 2, 1, 0]       ,   __lowerCAmelCase				)





				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								import faiss

								A  :	List[Any]				=					FaissIndex(string_factory='''Flat'''				)
								index.add_vectors(np.eye(5       ,   dtype=np.floataa				)				)
								self.assertIsInstance(index.faiss_index       ,   faiss.IndexFlat				)
								A  :	Tuple				=					FaissIndex(string_factory='''LSH'''				)
								index.add_vectors(np.eye(5       ,   dtype=np.floataa				)				)
								self.assertIsInstance(index.faiss_index       ,   faiss.IndexLSH				)
								with self.assertRaises(__lowerCAmelCase				):
												A  :	Tuple				=					FaissIndex(string_factory='''Flat'''       ,   custom_index=faiss.IndexFlat(5				)				)





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								import faiss

								A  :	List[Any]				=					faiss.IndexFlat(5				)
								A  :	Tuple				=					FaissIndex(custom_index=__lowerCAmelCase				)
								index.add_vectors(np.eye(5       ,   dtype=np.floataa				)				)
								self.assertIsInstance(index.faiss_index       ,   faiss.IndexFlat				)






				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								import faiss

								A  :	List[Any]				=					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=__lowerCAmelCase				) as tmp_file:
												index.save(tmp_file.name				)
												A  :	str				=					FaissIndex.load(tmp_file.name				)
								os.unlink(tmp_file.name				)

								A  :	List[Any]				=					np.zeros(5       ,   dtype=np.floataa				)
								A  :	Optional[Any]				=					1
								A			,							A  :	Any				=					index.search(__lowerCAmelCase				)
								self.assertGreater(scores[0]       ,   0				)
								self.assertEqual(indices[0]       ,   1				)





@require_faiss
def 				_lowerCamelCase      (       lowerCamelCase_:	Union[str, Any]		):



				'''simple docstring'''







				import faiss

				A  :	List[str]				=					FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
				index.add_vectors(np.eye(5					,						dtype=np.floataa		)		)

				A  :	Dict				=					'''index.faiss'''
				A  :	Any				=					f"""mock://{index_name}"""
				index.save(lowerCamelCase_					,						storage_options=mockfs.storage_options		)
				A  :	int				=					FaissIndex.load(lowerCamelCase_					,						storage_options=mockfs.storage_options		)

				A  :	Union[str, Any]				=					np.zeros(5					,						dtype=np.floataa		)
				A  :	Dict				=					1
				A			,							A  :	Union[str, Any]				=					index.search(lowerCamelCase_		)
				assert scores[0] > 0
				assert indices[0] == 1






@require_elasticsearch
class      _SCREAMING_SNAKE_CASE				(		UpperCAmelCase__				):





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								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:
												A  :	Tuple				=					Elasticsearch()
												A  :	Any				=					{'''acknowledged''': True}
												A  :	Optional[int]				=					ElasticSearchIndex(es_client=__lowerCAmelCase				)
												mocked_bulk.return_value([(True, None)] * 3				)
												index.add_documents(['''foo''', '''bar''', '''foobar''']				)

												# single query
												A  :	int				=					'''foo'''
												A  :	str				=					{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
												A			,							A  :	List[Any]				=					index.search(__lowerCAmelCase				)
												self.assertEqual(scores[0]       ,   1				)
												self.assertEqual(indices[0]       ,   0				)

												# single query with timeout
												A  :	List[Any]				=					'''foo'''
												A  :	List[str]				=					{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
												A			,							A  :	Any				=					index.search(__lowerCAmelCase       ,   request_timeout=30				)
												self.assertEqual(scores[0]       ,   1				)
												self.assertEqual(indices[0]       ,   0				)

												# batched queries
												A  :	Dict				=					['''foo''', '''bar''', '''foobar''']
												A  :	Optional[Any]				=					{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
												A			,							A  :	List[Any]				=					index.search_batch(__lowerCAmelCase				)
												A  :	Dict				=					[scores[0] for scores in total_scores]
												A  :	List[str]				=					[indices[0] for indices in total_indices]
												self.assertGreater(np.min(__lowerCAmelCase				)       ,   0				)
												self.assertListEqual([1, 1, 1]       ,   __lowerCAmelCase				)

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





'''simple docstring'''


import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'ylacombe/bark-small'
							__snake_case						 =   tempfile.mkdtemp()
							__snake_case						 =   'en_speaker_1'
							__snake_case						 =   'This is a test string'
							__snake_case						 =   'speaker_embeddings_path.json'
							__snake_case						 =   'speaker_embeddings'




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  str      ):
							return AutoTokenizer.from_pretrained(self.checkpoint       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							processor.save_pretrained(self.tmpdirname      )
							__snake_case						 =   BarkProcessor.from_pretrained(self.tmpdirname      )

							self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer.get_vocab()      )




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)
							processor.save_pretrained(
							    self.tmpdirname       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		speaker_embeddings_directory=self.speaker_embeddings_directory       ,		)

							__snake_case						 =   self.get_tokenizer(bos_token='(BOS)'       ,		eos_token='(EOS)'      )

							__snake_case						 =   BarkProcessor.from_pretrained(
							    self.tmpdirname       ,		self.speaker_embeddings_dict_path       ,		bos_token='(BOS)'       ,		eos_token='(EOS)'       ,		)

							self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer_add_kwargs.get_vocab()      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

							__snake_case						 =   3_5
							__snake_case						 =   2
							__snake_case						 =   8

							__snake_case						 =   {
							    'semantic_prompt': np.ones(__lowerCAmelCase      ),
							    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)      ),
							    'fine_prompt': np.ones((nb_codebooks_total, seq_len)      ),
							}

							# test providing already loaded voice_preset
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )

							__snake_case						 =   inputs['history_prompt']
							for key in voice_preset:
													self.assertListEqual(voice_preset[key].tolist()       ,		processed_voice_preset.get(__lowerCAmelCase       ,		np.array([]      )      ).tolist()      )

							# test loading voice preset from npz file
							__snake_case						 =   os.path.join(self.tmpdirname       ,		'file.npz'      )
							np.savez(__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )
							__snake_case						 =   inputs['history_prompt']

							for key in voice_preset:
													self.assertListEqual(voice_preset[key].tolist()       ,		processed_voice_preset.get(__lowerCAmelCase       ,		np.array([]      )      ).tolist()      )

							# test loading voice preset from the hub
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=self.voice_preset      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							__snake_case						 =   processor(text=self.input_string      )

							__snake_case						 =   tokenizer(
							    self.input_string       ,		padding='max_length'       ,		max_length=2_5_6       ,		add_special_tokens=__lowerCAmelCase       ,		return_attention_mask=__lowerCAmelCase       ,		return_token_type_ids=__lowerCAmelCase       ,		)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key].squeeze().tolist()      )

