




"""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


A_						=				pytest.mark.integration






@require_faiss
class 					lowercase(     __a ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		str								=  Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(a_		) for x in np.arange(30		).tolist()]}		)
   return dset


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   import faiss

   _snake_case							:		Dataset								=  self._create_dummy_dataset()
   _snake_case							:		Any								=  dset.map(
       lambda a_,      a_		: {"vecs": i * np.ones(5,      dtype=np.floataa		)},      with_indices=a_,      keep_in_memory=a_		)
   _snake_case							:		List[Any]								=  dset.add_faiss_index("""vecs""",      batch_size=100,      metric_type=faiss.METRIC_INNER_PRODUCT		)
   _snake_case		,					_snake_case							:		Optional[Any]								=  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:		str		):




   '''simple docstring'''



   import faiss

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


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   import faiss

   _snake_case							:		Dataset								=  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=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		)

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


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Dataset								=  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(a_,      partial(dset.get_nearest_examples,      """vecs2""",      np.ones(5,      dtype=np.floataa		)		)		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   from elasticsearch import Elasticsearch

   _snake_case							:		Dataset								=  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							:		Tuple								=  {"""acknowledged""": True}
     mocked_bulk.return_value([(True, None)] * 30		)
     _snake_case							:		List[str]								=  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
     _snake_case							:		Dict								=  Elasticsearch()

     dset.add_elasticsearch_index("""filename""",      es_client=a_		)
     _snake_case		,					_snake_case							:		int								=  dset.get_nearest_examples("""filename""",      """my_name-train_29"""		)
     self.assertEqual(examples["""filename"""][0],      """my_name-train_29"""		)







@require_faiss
class 					lowercase(     __a ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   import faiss

   _snake_case							:		int								=  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							:		List[Any]								=  np.zeros(5,      dtype=np.floataa		)
   _snake_case							:		List[str]								=  1
   _snake_case		,					_snake_case							:		str								=  index.search(a_		)
   self.assertRaises(a_,      index.search,      query.reshape(-1,      1		)		)
   self.assertGreater(scores[0],      0		)
   self.assertEqual(indices[0],      1		)

   # batched queries
   _snake_case							:		Union[str, Any]								=  np.eye(5,      dtype=np.floataa		)[::-1]
   _snake_case		,					_snake_case							:		Optional[Any]								=  index.search_batch(a_		)
   self.assertRaises(a_,      index.search_batch,      queries[0]		)
   _snake_case							:		Optional[Any]								=  [scores[0] for scores in total_scores]
   _snake_case							:		Dict								=  [indices[0] for indices in total_indices]
   self.assertGreater(np.min(a_		),      0		)
   self.assertListEqual([4, 3, 2, 1, 0],      a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   import faiss

   _snake_case							:		List[str]								=  FaissIndex(string_factory="""Flat"""		)
   index.add_vectors(np.eye(5,      dtype=np.floataa		)		)
   self.assertIsInstance(index.faiss_index,      faiss.IndexFlat		)
   _snake_case							:		Any								=  FaissIndex(string_factory="""LSH"""		)
   index.add_vectors(np.eye(5,      dtype=np.floataa		)		)
   self.assertIsInstance(index.faiss_index,      faiss.IndexLSH		)
   with self.assertRaises(a_		):
     _snake_case							:		Dict								=  FaissIndex(string_factory="""Flat""",      custom_index=faiss.IndexFlat(5		)		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   import faiss

   _snake_case							:		Any								=  faiss.IndexFlat(5		)
   _snake_case							:		Tuple								=  FaissIndex(custom_index=a_		)
   index.add_vectors(np.eye(5,      dtype=np.floataa		)		)
   self.assertIsInstance(index.faiss_index,      faiss.IndexFlat		)


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




   '''simple docstring'''



   import faiss

   _snake_case							:		List[str]								=  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		)
     _snake_case							:		str								=  FaissIndex.load(tmp_file.name		)
   os.unlink(tmp_file.name		)

   _snake_case							:		int								=  np.zeros(5,      dtype=np.floataa		)
   _snake_case							:		List[str]								=  1
   _snake_case		,					_snake_case							:		List[Any]								=  index.search(a_		)
   self.assertGreater(scores[0],      0		)
   self.assertEqual(indices[0],      1		)







@require_faiss
def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  import faiss

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

  _snake_case							:		Any								=  """index.faiss"""
  _snake_case							:		int								=  F"mock://{index_name}"
  index.save(snake_case__		,   storage_options=mockfs.storage_options    )
  _snake_case							:		Optional[Any]								=  FaissIndex.load(snake_case__		,   storage_options=mockfs.storage_options    )

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






@require_elasticsearch
class 					lowercase(     __a ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''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							:		Tuple								=  Elasticsearch()
     _snake_case							:		Union[str, Any]								=  {"""acknowledged""": True}
     _snake_case							:		List[str]								=  ElasticSearchIndex(es_client=a_		)
     mocked_bulk.return_value([(True, None)] * 3		)
     index.add_documents(["""foo""", """bar""", """foobar"""]		)

     # single query
     _snake_case							:		str								=  """foo"""
     _snake_case							:		Dict								=  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
     _snake_case		,					_snake_case							:		Tuple								=  index.search(a_		)
     self.assertEqual(scores[0],      1		)
     self.assertEqual(indices[0],      0		)

     # single query with timeout
     _snake_case							:		Union[str, Any]								=  """foo"""
     _snake_case							:		Tuple								=  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
     _snake_case		,					_snake_case							:		int								=  index.search(a_,      request_timeout=30		)
     self.assertEqual(scores[0],      1		)
     self.assertEqual(indices[0],      0		)

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

     # batched queries with timeout
     _snake_case							:		Optional[int]								=  ["""foo""", """bar""", """foobar"""]
     _snake_case							:		List[Any]								=  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
     _snake_case		,					_snake_case							:		Tuple								=  index.search_batch(a_,      request_timeout=30		)
     _snake_case							:		Union[str, Any]								=  [scores[0] for scores in total_scores]
     _snake_case							:		Dict								=  [indices[0] for indices in total_indices]
     self.assertGreater(np.min(a_		),      0		)
     self.assertListEqual([1, 1, 1],      a_		)



"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

            # standard deviation of the initial noise distribution
            lowercase														=	1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            lowercase														=	4

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

            if self.config.trained_betas is not None:
                  lowercase														=	torch.tensor(self.config.trained_betas		,       dtype=torch.floataa						)
            else:
                  lowercase														=	torch.sin(steps * math.pi / 2						) ** 2

            lowercase														=	(1.0 - self.betas**2) ** 0.5

            lowercase														=	(torch.atana(self.betas		,       self.alphas						) / math.pi * 2)[:-1]
            lowercase														=	timesteps.to(__lowerCAmelCase						)

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if self.num_inference_steps is None:
                  raise ValueError(
                      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

            lowercase														=	(self.timesteps == timestep).nonzero().item()
            lowercase														=	timestep_index + 1

            lowercase														=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__lowerCAmelCase						)

            if len(self.ets						) == 1:
                  lowercase														=	self.ets[-1]
            elif len(self.ets						) == 2:
                  lowercase														=	(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets						) == 3:
                  lowercase														=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                  lowercase														=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

            lowercase														=	self.alphas[prev_timestep_index]
            lowercase														=	self.betas[prev_timestep_index]

            lowercase														=	(sample - sigma * ets) / max(__lowerCAmelCase		,       1E-8						)
            lowercase														=	next_alpha * pred + ets * next_sigma

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



