
"""simple docstring"""







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


_SCREAMING_SNAKE_CASE     :   List[Any]	       =      pytest.mark.integration







@require_faiss
class 							__magic_name__     (		__lowercase   ):





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       Dataset.from_dict({"filename": ["my_name-train" + "_" + str(snake_case_    ) for x in np.arange(30    ).tolist()]}    )
												return dset





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												import faiss

												__snake_case     			=       self._create_dummy_dataset()
												__snake_case     			=       dset.map(
												    lambda snake_case_							,						snake_case_    : {"vecs": i * np.ones(5							,						dtype=np.floataa    )}							,						with_indices=snake_case_							,						keep_in_memory=snake_case_    )
												__snake_case     			=       dset.add_faiss_index("vecs"							,						batch_size=100							,						metric_type=faiss.METRIC_INNER_PRODUCT    )
												__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 							lowerCAmelCase      (				self   :							int    ):
												import faiss

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





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												import faiss

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       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(snake_case_							,						partial(dset.get_nearest_examples							,						"vecs2"							,						np.ones(5							,						dtype=np.floataa    )    )    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												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)] * 30    )
																			__snake_case     			=       {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
																			__snake_case     			=       Elasticsearch()

																			dset.add_elasticsearch_index("filename"							,						es_client=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 							__magic_name__     (		__lowercase   ):





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												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							,						10    )

												# single query
												__snake_case     			=       np.zeros(5							,						dtype=np.floataa    )
												__snake_case     			=       1
												__snake_case     			=       index.search(snake_case_    )
												self.assertRaises(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     			=       index.search_batch(snake_case_    )
												self.assertRaises(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(snake_case_    )							,						0    )
												self.assertListEqual([4, 3, 2, 1, 0]							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												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(snake_case_    ):
																			__snake_case     			=       FaissIndex(string_factory="Flat"							,						custom_index=faiss.IndexFlat(5    )    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												import faiss

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





					def 							lowerCAmelCase      (				self   :							Any    ):
												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=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     			=       index.search(snake_case_    )
												self.assertGreater(scores[0]							,						0    )
												self.assertEqual(indices[0]							,						1    )




@require_faiss
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Any:







							"""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     			=       index.search(_lowerCamelCase )
							assert scores[0] > 0
							assert indices[0] == 1







@require_elasticsearch
class 							__magic_name__     (		__lowercase   ):





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												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=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     			=       index.search(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     			=       index.search(snake_case_							,						request_timeout=30    )
																			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     			=       index.search_batch(snake_case_    )
																			__snake_case     			=       [scores[0] for scores in total_scores]
																			__snake_case     			=       [indices[0] for indices in total_indices]
																			self.assertGreater(np.min(snake_case_    )							,						0    )
																			self.assertListEqual([1, 1, 1]							,						snake_case_    )

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


"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput







@dataclass
class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1





					@register_to_config
					def __init__(				self   :							List[Any]							,						snake_case_   :							int = 2000							,						snake_case_   :							float = 0.15							,						snake_case_   :							float = 0.01							,						snake_case_   :							float = 1348.0							,						snake_case_   :							float = 1e-5							,						snake_case_   :							int = 1							,						):
												# standard deviation of the initial noise distribution
												__snake_case     			=       sigma_max

												# setable values
												__snake_case     			=       None

												self.set_sigmas(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[int] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							Union[str, torch.device] = None    ):
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__snake_case     			=       torch.linspace(1							,						snake_case_							,						snake_case_							,						device=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							float = None							,						snake_case_   :							float = None    ):
												__snake_case     			=       sigma_min if sigma_min is not None else self.config.sigma_min
												__snake_case     			=       sigma_max if sigma_max is not None else self.config.sigma_max
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																			self.set_timesteps(snake_case_							,						snake_case_    )

												__snake_case     			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												__snake_case     			=       torch.exp(torch.linspace(math.log(snake_case_    )							,						math.log(snake_case_    )							,						snake_case_    )    )
												__snake_case     			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Dict    ):
												return torch.where(
												    timesteps == 0							,						torch.zeros_like(t.to(timesteps.device    )    )							,						self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,						)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												__snake_case     			=       timestep * torch.ones(
												    sample.shape[0]							,						device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
												__snake_case     			=       (timestep * (len(self.timesteps    ) - 1)).long()

												# mps requires indices to be in the same device, so we use cpu as is the default with cuda
												__snake_case     			=       timesteps.to(self.discrete_sigmas.device    )

												__snake_case     			=       self.discrete_sigmas[timesteps].to(sample.device    )
												__snake_case     			=       self.get_adjacent_sigma(snake_case_							,						snake_case_    ).to(sample.device    )
												__snake_case     			=       torch.zeros_like(snake_case_    )
												__snake_case     			=       (sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												__snake_case     			=       diffusion.flatten()
												while len(diffusion.shape    ) < len(sample.shape    ):
																			__snake_case     			=       diffusion.unsqueeze(-1    )
												__snake_case     			=       drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__snake_case     			=       randn_tensor(
												    sample.shape							,						layout=sample.layout							,						generator=snake_case_							,						device=sample.device							,						dtype=sample.dtype    )
												__snake_case     			=       sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__snake_case     			=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																			return (prev_sample, prev_sample_mean)

												return SdeVeOutput(prev_sample=snake_case_							,						prev_sample_mean=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												__snake_case     			=       randn_tensor(sample.shape							,						layout=sample.layout							,						generator=snake_case_    ).to(sample.device    )

												# compute step size from the model_output, the noise, and the snr
												__snake_case     			=       torch.norm(model_output.reshape(model_output.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       torch.norm(noise.reshape(noise.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__snake_case     			=       step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
												# self.repeat_scalar(step_size, sample.shape[0])

												# compute corrected sample: model_output term and noise term
												__snake_case     			=       step_size.flatten()
												while len(step_size.shape    ) < len(sample.shape    ):
																			__snake_case     			=       step_size.unsqueeze(-1    )
												__snake_case     			=       sample + step_size * model_output
												__snake_case     			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												__snake_case     			=       timesteps.to(original_samples.device    )
												__snake_case     			=       self.discrete_sigmas.to(original_samples.device    )[timesteps]
												__snake_case     			=       (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
												)
												__snake_case     			=       noise + original_samples
												return noisy_samples





					def __len__(				self   :							Tuple    ):
												return self.config.num_train_timesteps
