
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					random.seed(lowerCAmelCase__		)
					np.random.seed(lowerCAmelCase__		)
					torch.manual_seed(lowerCAmelCase__		)
					torch.cuda.manual_seed_all(lowerCAmelCase__		)
					# ^^ safe to call this function even if cuda is not available







class 				lowercase		:




							def __init__(				self   ,A__   ,A__ = 0.9999   ,A__ = 0.0   ,A__ = 0   ,A__ = False   ,A__ = 1.0   ,A__ = 2 / 3   ,A__ = None   ,A__ = None   ,**A__   ,):

												if isinstance(A__   ,torch.nn.Module):
																	lowercase							=						(
																	    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
																	    '''Please pass the parameters of the module instead.'''
																	)
																	deprecate(
																	    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''   ,'''1.0.0'''   ,A__   ,standard_warn=A__   ,)
																	lowercase							=						parameters.parameters()

																	# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																	lowercase							=						True

												if kwargs.get('''max_value'''   ,A__) is not None:
																	lowercase							=						'''The `max_value` argument is deprecated. Please use `decay` instead.'''
																	deprecate('''max_value'''   ,'''1.0.0'''   ,A__   ,standard_warn=A__)
																	lowercase							=						kwargs['''max_value''']

												if kwargs.get('''min_value'''   ,A__) is not None:
																	lowercase							=						'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
																	deprecate('''min_value'''   ,'''1.0.0'''   ,A__   ,standard_warn=A__)
																	lowercase							=						kwargs['''min_value''']

												lowercase							=						list(A__)
												lowercase							=						[p.clone().detach() for p in parameters]

												if kwargs.get('''device'''   ,A__) is not None:
																	lowercase							=						'''The `device` argument is deprecated. Please use `to` instead.'''
																	deprecate('''device'''   ,'''1.0.0'''   ,A__   ,standard_warn=A__)
																	self.to(device=kwargs['''device'''])

												lowercase							=						None

												lowercase							=						decay
												lowercase							=						min_decay
												lowercase							=						update_after_step
												lowercase							=						use_ema_warmup
												lowercase							=						inv_gamma
												lowercase							=						power
												lowercase							=						0
												lowercase							=						None  # set in `step()`

												lowercase							=						model_cls
												lowercase							=						model_config




							@classmethod
							def  A__      (				cls   ,A__   ,A__):
												lowercase	,						lowercase							=						model_cls.load_config(A__   ,return_unused_kwargs=A__)
												lowercase							=						model_cls.from_pretrained(A__)

												lowercase							=						cls(model.parameters()   ,model_cls=A__   ,model_config=model.config)

												ema_model.load_state_dict(A__)
												return ema_model




							def  A__      (				self   ,A__):
												if self.model_cls is None:
																	raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''')

												if self.model_config is None:
																	raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''')

												lowercase							=						self.model_cls.from_config(self.model_config)
												lowercase							=						self.state_dict()
												state_dict.pop('''shadow_params'''   ,A__)

												model.register_to_config(**A__)
												self.copy_to(model.parameters())
												model.save_pretrained(A__)




							def  A__      (				self   ,A__):
												lowercase							=						max(0   ,optimization_step - self.update_after_step - 1)

												if step <= 0:
																	return 0.0

												if self.use_ema_warmup:
																	lowercase							=						1 - (1 + step / self.inv_gamma) ** -self.power
												else:
																	lowercase							=						(1 + step) / (1_0 + step)

												lowercase							=						min(A__   ,self.decay)
												# make sure decay is not smaller than min_decay
												lowercase							=						max(A__   ,self.min_decay)
												return cur_decay_value




							@torch.no_grad()
							def  A__      (				self   ,A__):
												if isinstance(A__   ,torch.nn.Module):
																	lowercase							=						(
																	    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
																	    '''Please pass the parameters of the module instead.'''
																	)
																	deprecate(
																	    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''   ,'''1.0.0'''   ,A__   ,standard_warn=A__   ,)
																	lowercase							=						parameters.parameters()

												lowercase							=						list(A__)

												self.optimization_step += 1

												# Compute the decay factor for the exponential moving average.
												lowercase							=						self.get_decay(self.optimization_step)
												lowercase							=						decay
												lowercase							=						1 - decay

												lowercase							=						contextlib.nullcontext
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																	import deepspeed

												for s_param, param in zip(self.shadow_params   ,A__):
																	if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						lowercase							=						deepspeed.zero.GatheredParameters(A__   ,modifier_rank=A__)

																	with context_manager():
																						if param.requires_grad:
																											s_param.sub_(one_minus_decay * (s_param - param))
																						else:
																											s_param.copy_(A__)




							def  A__      (				self   ,A__):
												lowercase							=						list(A__)
												for s_param, param in zip(self.shadow_params   ,A__):
																	param.data.copy_(s_param.to(param.device).data)




							def  A__      (				self   ,A__=None   ,A__=None):
												lowercase							=						[
												    p.to(device=A__   ,dtype=A__) if p.is_floating_point() else p.to(device=A__)
												    for p in self.shadow_params
												]




							def  A__      (				self):
												return {
												    "decay": self.decay,
												    "min_decay": self.min_decay,
												    "optimization_step": self.optimization_step,
												    "update_after_step": self.update_after_step,
												    "use_ema_warmup": self.use_ema_warmup,
												    "inv_gamma": self.inv_gamma,
												    "power": self.power,
												    "shadow_params": self.shadow_params,
												}




							def  A__      (				self   ,A__):
												lowercase							=						[param.detach().cpu().clone() for param in parameters]




							def  A__      (				self   ,A__):
												if self.temp_stored_params is None:
																	raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''')
												for c_param, param in zip(self.temp_stored_params   ,A__):
																	param.data.copy_(c_param.data)

												# Better memory-wise.
												lowercase							=						None





							def  A__      (				self   ,A__):
												lowercase							=						copy.deepcopy(A__)

												lowercase							=						state_dict.get('''decay'''   ,self.decay)
												if self.decay < 0.0 or self.decay > 1.0:
																	raise ValueError('''Decay must be between 0 and 1''')

												lowercase							=						state_dict.get('''min_decay'''   ,self.min_decay)
												if not isinstance(self.min_decay   ,A__):
																	raise ValueError('''Invalid min_decay''')

												lowercase							=						state_dict.get('''optimization_step'''   ,self.optimization_step)
												if not isinstance(self.optimization_step   ,A__):
																	raise ValueError('''Invalid optimization_step''')

												lowercase							=						state_dict.get('''update_after_step'''   ,self.update_after_step)
												if not isinstance(self.update_after_step   ,A__):
																	raise ValueError('''Invalid update_after_step''')

												lowercase							=						state_dict.get('''use_ema_warmup'''   ,self.use_ema_warmup)
												if not isinstance(self.use_ema_warmup   ,A__):
																	raise ValueError('''Invalid use_ema_warmup''')

												lowercase							=						state_dict.get('''inv_gamma'''   ,self.inv_gamma)
												if not isinstance(self.inv_gamma   ,(float, int)):
																	raise ValueError('''Invalid inv_gamma''')

												lowercase							=						state_dict.get('''power'''   ,self.power)
												if not isinstance(self.power   ,(float, int)):
																	raise ValueError('''Invalid power''')

												lowercase							=						state_dict.get('''shadow_params'''   ,A__)
												if shadow_params is not None:
																	lowercase							=						shadow_params
																	if not isinstance(self.shadow_params   ,A__):
																						raise ValueError('''shadow_params must be a list''')
																	if not all(isinstance(A__   ,torch.Tensor) for p in self.shadow_params):
																						raise ValueError('''shadow_params must all be Tensors''')






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


lowercase__		:Optional[int]   =    pytest.mark.integration







@require_faiss
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												lowercase							=						Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(A__) for x in np.arange(3_0).tolist()]})
												return dset




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												lowercase							=						dset.map(
												    lambda A__   ,A__: {"vecs": i * np.ones(5   ,dtype=np.floataa)}   ,with_indices=A__   ,keep_in_memory=A__)
												lowercase							=						dset.add_faiss_index('''vecs'''   ,batch_size=1_0_0   ,metric_type=faiss.METRIC_INNER_PRODUCT)
												lowercase	,						lowercase							=						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  A__      (				self):
												import faiss

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




							def  A__      (				self):
												import faiss

												lowercase							=						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=A__) as tmp_file:
																	dset.save_faiss_index('''vecs'''   ,tmp_file.name)
																	dset.load_faiss_index('''vecs2'''   ,tmp_file.name)
												os.unlink(tmp_file.name)

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




							def  A__      (				self):
												lowercase							=						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(A__   ,partial(dset.get_nearest_examples   ,'''vecs2'''   ,np.ones(5   ,dtype=np.floataa)))





							def  A__      (				self):
												from elasticsearch import Elasticsearch

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

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







@require_faiss
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												import faiss

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

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




							def  A__      (				self):
												import faiss

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




							def  A__      (				self):
												import faiss

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





							def  A__      (				self):
												import faiss

												lowercase							=						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=A__) as tmp_file:
																	index.save(tmp_file.name)
																	lowercase							=						FaissIndex.load(tmp_file.name)
												os.unlink(tmp_file.name)

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



@require_faiss
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					import faiss

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

					lowercase							=						'''index.faiss'''
					lowercase							=						f'mock://{index_name}'
					index.save(lowerCAmelCase__ ,			storage_options=mockfs.storage_options		)
					lowercase							=						FaissIndex.load(lowerCAmelCase__ ,			storage_options=mockfs.storage_options		)

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







@require_elasticsearch
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												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:
																	lowercase							=						Elasticsearch()
																	lowercase							=						{'''acknowledged''': True}
																	lowercase							=						ElasticSearchIndex(es_client=A__)
																	mocked_bulk.return_value([(True, None)] * 3)
																	index.add_documents(['''foo''', '''bar''', '''foobar'''])

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

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

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

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




