

'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Dict							     =     (DDPMScheduler,)






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,**lowercase__  :	int     ):
   __lowercase				    =					{
       '''num_train_timesteps''': 1_0_0_0,
       '''beta_start''': 0.0_0_0_1,
       '''beta_end''': 0.0_2,
       '''beta_schedule''': '''linear''',
       '''variance_type''': '''fixed_small''',
       '''clip_sample''': True,
   }

   config.update(**lowercase__     )
   return config






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
    self.check_over_configs(num_train_timesteps=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,[0.0_0_2, 0.0_2, 0.2, 2]     ):
    self.check_over_configs(beta_start=lowercase__   ,beta_end=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for schedule in ["linear", "squaredcos_cap_v2"]:
    self.check_over_configs(beta_schedule=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   for variance in ["fixed_small", "fixed_large", "other"]:
    self.check_over_configs(variance_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   for clip_sample in [True, False]:
    self.check_over_configs(clip_sample=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   self.check_over_configs(thresholding=lowercase__     )
   for threshold in [0.5, 1.0, 2.0]:
    for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(
         thresholding=lowercase__   ,prediction_type=lowercase__   ,sample_max_value=lowercase__   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for prediction_type in ["epsilon", "sample", "v_prediction"]:
    self.check_over_configs(prediction_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   for t in [0, 5_0_0, 9_9_9]:
    self.check_over_forward(time_step=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.0_0_9_7_9     )     ) < 1e-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.0_2     )     ) < 1e-5






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					len(lowercase__     )

   __lowercase				    =					self.dummy_model()
   __lowercase				    =					self.dummy_sample_deter
   __lowercase				    =					torch.manual_seed(0     )

   for t in reversed(range(lowercase__     )     ):
    # 1. predict noise residual
    __lowercase				    =					model(lowercase__   ,lowercase__     )

    # 2. predict previous mean of sample x_t-1
    __lowercase				    =					scheduler.step(lowercase__   ,lowercase__   ,lowercase__   ,generator=lowercase__     ).prev_sample

    # if t > 0:
    #     noise = self.dummy_sample_deter
    #     variance = scheduler.get_variance(t) ** (0.5) * noise
    #
    # sample = pred_prev_sample + variance
    __lowercase				    =					pred_prev_sample

   __lowercase				    =					torch.sum(torch.abs(lowercase__     )     )
   __lowercase				    =					torch.mean(torch.abs(lowercase__     )     )

   assert abs(result_sum.item() - 2_5_8.9_6_0_6     ) < 1e-2
   assert abs(result_mean.item() - 0.3_3_7_2     ) < 1e-3






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config(prediction_type='''v_prediction'''     )
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					len(lowercase__     )

   __lowercase				    =					self.dummy_model()
   __lowercase				    =					self.dummy_sample_deter
   __lowercase				    =					torch.manual_seed(0     )

   for t in reversed(range(lowercase__     )     ):
    # 1. predict noise residual
    __lowercase				    =					model(lowercase__   ,lowercase__     )

    # 2. predict previous mean of sample x_t-1
    __lowercase				    =					scheduler.step(lowercase__   ,lowercase__   ,lowercase__   ,generator=lowercase__     ).prev_sample

    # if t > 0:
    #     noise = self.dummy_sample_deter
    #     variance = scheduler.get_variance(t) ** (0.5) * noise
    #
    # sample = pred_prev_sample + variance
    __lowercase				    =					pred_prev_sample

   __lowercase				    =					torch.sum(torch.abs(lowercase__     )     )
   __lowercase				    =					torch.mean(torch.abs(lowercase__     )     )

   assert abs(result_sum.item() - 2_0_2.0_2_9_6     ) < 1e-2
   assert abs(result_mean.item() - 0.2_6_3_1     ) < 1e-3






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[1_0_0, 8_7, 5_0, 1, 0]

   scheduler.set_timesteps(timesteps=lowercase__     )

   __lowercase				    =					scheduler.timesteps

   for i, timestep in enumerate(lowercase__     ):
    if i == len(lowercase__     ) - 1:
     __lowercase				    =					-1
    else:
     __lowercase				    =					timesteps[i + 1]

    __lowercase				    =					scheduler.previous_timestep(lowercase__     )
    __lowercase				    =					prev_t.item()

    self.assertEqual(lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[1_0_0, 8_7, 5_0, 5_1, 0]

   with self.assertRaises(lowercase__   ,msg='''`custom_timesteps` must be in descending order.'''     ):
    scheduler.set_timesteps(timesteps=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[1_0_0, 8_7, 5_0, 1, 0]
   __lowercase				    =					len(lowercase__     )

   with self.assertRaises(lowercase__   ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''     ):
    scheduler.set_timesteps(num_inference_steps=lowercase__   ,timesteps=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					self.scheduler_classes[0]
   __lowercase				    =					self.get_scheduler_config()
   __lowercase				    =					scheduler_class(**lowercase__     )

   __lowercase				    =					[scheduler.config.num_train_timesteps]

   with self.assertRaises(
       lowercase__   ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,):
    scheduler.set_timesteps(timesteps=lowercase__     )




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


_lowerCamelCase      :					List[str]					    =      pytest.mark.integration





@require_faiss
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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







             '''simple docstring'''



             import faiss

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =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(lowercase__ ,   partial(dset.get_nearest_examples ,   '''vecs2''' ,   np.ones(5 ,   dtype=np.floataa     )     )     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             from elasticsearch import Elasticsearch

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

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





@require_faiss
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             import faiss

             __A    =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=lowercase__     ) as tmp_file:
                   index.save(tmp_file.name     )
                   __A    =FaissIndex.load(tmp_file.name     )
             os.unlink(tmp_file.name     )

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





@require_faiss
def      A__					(			__A  :						Dict			)					->List[Any]:
      import faiss

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

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

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





@require_elasticsearch
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







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

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

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

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

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


