
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class __snake_case		(      lowerCAmelCase     ):
    _a		:						torch.FloatTensor
    _a		:						Optional[torch.FloatTensor]=				None







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=0.999					,	SCREAMING_SNAKE_CASE__="cosine"					,	) ->    Optional[Any]:
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__				):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__				):
                     return math.exp(t * -12.0				)

       else:
              raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"				)

       lowercase							:     Tuple       	=      []
       for i in range(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[str]       	=      i / num_diffusion_timesteps
              lowercase							:     List[str]       	=      (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__				) / alpha_bar_fn(SCREAMING_SNAKE_CASE__				)					,	SCREAMING_SNAKE_CASE__				)				)
       return torch.tensor(SCREAMING_SNAKE_CASE__					,	dtype=torch.floataa				)




class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						Union[str, Any]=				1
    @register_to_config
    def __init__(  self		,snake_case = 1000		,snake_case = 0.0_001		,snake_case = 0.02		,snake_case = "linear"		,snake_case = None		,snake_case = True		,snake_case = True		,snake_case = 0		,snake_case = "epsilon"		,snake_case = 1.0		,**snake_case		,):



           '''simple docstring'''







           if kwargs.get("""set_alpha_to_one"""		,snake_case      ) is not None:
                  lowercase							:     Any       	=      (
                      """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
                  )
                  deprecate("""set_alpha_to_one"""		,"""1.0.0"""		,snake_case		,standard_warn=snake_case      )
                  lowercase							:     List[str]       	=      kwargs["""set_alpha_to_one"""]
           if trained_betas is not None:
                  lowercase							:     str       	=      torch.tensor(snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "linear":
                  lowercase							:     Union[str, Any]       	=      torch.linspace(snake_case		,snake_case		,snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  lowercase							:     int       	=      (
                      torch.linspace(beta_start**0.5		,beta_end**0.5		,snake_case		,dtype=torch.floataa      ) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowercase							:     List[Any]       	=      betas_for_alpha_bar(snake_case      )
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

           lowercase							:     Optional[Any]       	=      1.0 - self.betas
           lowercase							:     str       	=      torch.cumprod(self.alphas		,dim=0      )

           # At every step in inverted ddim, we are looking into the next alphas_cumprod
           # For the final step, there is no next alphas_cumprod, and the index is out of bounds
           # `set_alpha_to_zero` decides whether we set this parameter simply to zero
           # in this case, self.step() just output the predicted noise
           # or whether we use the final alpha of the "non-previous" one.
           lowercase							:     Optional[int]       	=      torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

           # standard deviation of the initial noise distribution
           lowercase							:     Dict       	=      1.0

           # setable values
           lowercase							:     List[Any]       	=      None
           lowercase							:     Dict       	=      torch.from_numpy(np.arange(0		,snake_case      ).copy().astype(np.intaa      )      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           return sample
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           if num_inference_steps > self.config.num_train_timesteps:
                  raise ValueError(
                      f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                      f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                      f" maximal {self.config.num_train_timesteps} timesteps."      )

           lowercase							:     Any       	=      num_inference_steps
           lowercase							:     Any       	=      self.config.num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           lowercase							:     Union[str, Any]       	=      (np.arange(0		,snake_case      ) * step_ratio).round().copy().astype(np.intaa      )
           lowercase							:     Dict       	=      torch.from_numpy(snake_case      ).to(snake_case      )
           self.timesteps += self.config.steps_offset
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = 0.0		,snake_case = False		,snake_case = None		,snake_case = True		,):



           '''simple docstring'''







           lowercase							:     List[str]       	=      timestep + self.config.num_train_timesteps // self.num_inference_steps

           # 2. compute alphas, betas
           # change original implementation to exactly match noise levels for analogous forward process
           lowercase							:     Optional[Any]       	=      self.alphas_cumprod[timestep]
           lowercase							:     str       	=      (
               self.alphas_cumprod[prev_timestep]
               if prev_timestep < self.config.num_train_timesteps
               else self.final_alpha_cumprod
           )

           lowercase							:     Any       	=      1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                  lowercase							:     Union[str, Any]       	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  lowercase							:     str       	=      model_output
           elif self.config.prediction_type == "sample":
                  lowercase							:     str       	=      model_output
                  lowercase							:     str       	=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                  lowercase							:     str       	=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  lowercase							:     int       	=      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                  raise ValueError(
                      f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                      """ `v_prediction`"""      )

           # 4. Clip or threshold "predicted x_0"
           if self.config.clip_sample:
                  lowercase							:     int       	=      pred_original_sample.clamp(
                      -self.config.clip_sample_range		,self.config.clip_sample_range      )

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowercase							:     Dict       	=      (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowercase							:     int       	=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                  return (prev_sample, pred_original_sample)
           return DDIMSchedulerOutput(prev_sample=snake_case		,pred_original_sample=snake_case      )
    def __len__(  self      ):



           '''simple docstring'''







           return self.config.num_train_timesteps








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


UpperCAmelCase__				   =		pytest.mark.integration






@require_faiss
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       import faiss

       a		  =      self._create_dummy_dataset()
       a		  =      dset.map(
           lambda __UpperCAmelCase ,					__UpperCAmelCase  : {"vecs": i * np.ones(5 ,					dtype=np.floataa  )} ,					with_indices=__UpperCAmelCase ,					keep_in_memory=__UpperCAmelCase  )
       a		  =      dset.add_faiss_index('''vecs''' ,					batch_size=100 ,					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       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[Any]:




       """simple docstring"""


       import faiss

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







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




       """simple docstring"""


       import faiss

       a		  =      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=__UpperCAmelCase  ) 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       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      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(__UpperCAmelCase ,					partial(dset.get_nearest_examples ,					'''vecs2''' ,					np.ones(5 ,					dtype=np.floataa  )  )  )







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




       """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)] * 30  )
        a		  =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
        a		  =      Elasticsearch()

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






@require_faiss
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Any  )    ->Any:




       """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 ,					10  )

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







      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """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(__UpperCAmelCase  ):
        a		  =      FaissIndex(string_factory='''Flat''' ,					custom_index=faiss.IndexFlat(5  )  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[Any]:




       """simple docstring"""


       import faiss

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







      def       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """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=__UpperCAmelCase  ) 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(__UpperCAmelCase  )
       self.assertGreater(scores[0] ,					0  )
       self.assertEqual(indices[0] ,					1  )
@require_faiss
def 						_a    (      a :Dict )							->			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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						int  )    ->List[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:
        a		  =      Elasticsearch()
        a		  =      {'''acknowledged''': True}
        a		  =      ElasticSearchIndex(es_client=__UpperCAmelCase  )
        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(__UpperCAmelCase  )
        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(__UpperCAmelCase ,					request_timeout=30  )
        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(__UpperCAmelCase  )
        a		  =      [scores[0] for scores in total_scores]
        a		  =      [indices[0] for indices in total_indices]
        self.assertGreater(np.min(__UpperCAmelCase  ) ,					0  )
        self.assertListEqual([1, 1, 1] ,					__UpperCAmelCase  )

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


