


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							:					Union[str, Any]     						=     pytest.mark.integration






@require_faiss
class     UpperCamelCase(  _a   ):


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




											'''simple docstring'''

											__snake_case				=		Dataset.from_dict({"filename": ["my_name-train" + "_" + str(SCREAMING_SNAKE_CASE						) for x in np.arange(3_0						).tolist()]}						)
											return dset


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




											'''simple docstring'''

											import faiss

											__snake_case				=		self._create_dummy_dataset()
											__snake_case				=		dset.map(
											    lambda SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						: {"vecs": i * np.ones(5     ,    dtype=np.floataa						)}     ,    with_indices=SCREAMING_SNAKE_CASE     ,    keep_in_memory=SCREAMING_SNAKE_CASE						)
											__snake_case				=		dset.add_faiss_index("vecs"     ,    batch_size=1_0_0     ,    metric_type=faiss.METRIC_INNER_PRODUCT						)
											__snake_case		,					__snake_case				=		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	:		List[str]						)			->	Union[str, Any]:




											'''simple docstring'''

											import faiss

											__snake_case				=		self._create_dummy_dataset()
											dset.add_faiss_index_from_external_arrays(
											    external_arrays=np.ones((3_0, 5)						) * np.arange(3_0						).reshape(-1     ,    1						)     ,    index_name="vecs"     ,    batch_size=1_0_0     ,    metric_type=faiss.METRIC_INNER_PRODUCT     ,    )
											__snake_case		,					__snake_case				=		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	:		str						)			->	str:




											'''simple docstring'''

											import faiss

											__snake_case				=		self._create_dummy_dataset()
											dset.add_faiss_index_from_external_arrays(
											    external_arrays=np.ones((3_0, 5)						) * np.arange(3_0						).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=SCREAMING_SNAKE_CASE						) as tmp_file:
																	dset.save_faiss_index("vecs"     ,    tmp_file.name						)
																	dset.load_faiss_index("vecs2"     ,    tmp_file.name						)
											os.unlink(tmp_file.name						)

											__snake_case		,					__snake_case				=		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	:		List[Any]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self._create_dummy_dataset()
											dset.add_faiss_index_from_external_arrays(
											    external_arrays=np.ones((3_0, 5)						) * np.arange(3_0						).reshape(-1     ,    1						)     ,    index_name="vecs"						)
											dset.drop_index("vecs"						)
											self.assertRaises(SCREAMING_SNAKE_CASE     ,    partial(dset.get_nearest_examples     ,    "vecs2"     ,    np.ones(5     ,    dtype=np.floataa						)						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Any:




											'''simple docstring'''

											from elasticsearch import Elasticsearch

											__snake_case				=		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:
																	__snake_case				=		{"acknowledged": True}
																	mocked_bulk.return_value([(True, None)] * 3_0						)
																	__snake_case				=		{"hits": {"hits": [{"_score": 1, "_id": 2_9}]}}
																	__snake_case				=		Elasticsearch()

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






@require_faiss
class     UpperCamelCase(  _a   ):


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




											'''simple docstring'''

											import faiss

											__snake_case				=		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     ,    1_0						)

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

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	str:




											'''simple docstring'''

											import faiss

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


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




											'''simple docstring'''

											import faiss

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Tuple:




											'''simple docstring'''

											import faiss

											__snake_case				=		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=SCREAMING_SNAKE_CASE						) as tmp_file:
																	index.save(tmp_file.name						)
																	__snake_case				=		FaissIndex.load(tmp_file.name						)
											os.unlink(tmp_file.name						)

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





@require_faiss
def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						import faiss

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

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

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






@require_elasticsearch
class     UpperCamelCase(  _a   ):


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




											'''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:
																	__snake_case				=		Elasticsearch()
																	__snake_case				=		{"acknowledged": True}
																	__snake_case				=		ElasticSearchIndex(es_client=SCREAMING_SNAKE_CASE						)
																	mocked_bulk.return_value([(True, None)] * 3						)
																	index.add_documents(["foo", "bar", "foobar"]						)

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

																	# single query with timeout
																	__snake_case				=		"foo"
																	__snake_case				=		{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
																	__snake_case		,					__snake_case				=		index.search(SCREAMING_SNAKE_CASE     ,    request_timeout=3_0						)
																	self.assertEqual(scores[0]     ,    1						)
																	self.assertEqual(indices[0]     ,    0						)

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

																	# batched queries with timeout
																	__snake_case				=		["foo", "bar", "foobar"]
																	__snake_case				=		{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
																	__snake_case		,					__snake_case				=		index.search_batch(SCREAMING_SNAKE_CASE     ,    request_timeout=3_0						)
																	__snake_case				=		[scores[0] for scores in total_scores]
																	__snake_case				=		[indices[0] for indices in total_indices]
																	self.assertGreater(np.min(SCREAMING_SNAKE_CASE						)     ,    0						)
																	self.assertListEqual([1, 1, 1]     ,    SCREAMING_SNAKE_CASE						)









import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image





def 	_lowerCAmelCase       (    _lowerCAmelCase="" )						->	str:


						'''simple docstring'''






						__snake_case				=		tempfile.mkdtemp()
						return os.path.join(_lowerCAmelCase      ,	str(uuid.uuida() ) + suffix )






@require_soundfile
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)

											del agent_type

											# Ensure the path remains even after the object deletion
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)

											# Ensure that the file contains the same value as the original tensor
											__snake_case		,					__snake_case				=		sf.read(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    torch.tensor(SCREAMING_SNAKE_CASE						)     ,    atol=1e-4						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)
											self.assertEqual(agent_type.to_string()     ,    SCREAMING_SNAKE_CASE						)






@require_vision
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


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




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type._tensor     ,    atol=1e-4						)						)

											self.assertIsInstance(agent_type.to_raw()     ,    Image.Image						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertTrue(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






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




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertFalse(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_string()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)





