


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       :		int    =   pytest.mark.integration



@require_faiss
class 							A    (       __a							):
					'''simple docstring'''






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




							"""simple docstring"""


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

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




							"""simple docstring"""


							import faiss

							lowercase__     			=		self._create_dummy_dataset()
							lowercase__     			=		dset.map(
							    lambda _UpperCAmelCase      ,					_UpperCAmelCase		: {"vecs": i * np.ones(5      ,					dtype=np.floataa		)}      ,					with_indices=_A      ,					keep_in_memory=_A		)
							lowercase__     			=		dset.add_faiss_index("""vecs"""      ,					batch_size=100      ,					metric_type=faiss.METRIC_INNER_PRODUCT		)
							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   lowerCamelCase__     (self    :						Tuple		)						->   Optional[Any]:




							"""simple docstring"""


							import faiss

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

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




							"""simple docstring"""


							import faiss

							lowercase__     			=		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		)

							lowercase__     			=		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]		)						->   int:




							"""simple docstring"""


							lowercase__     			=		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   lowerCamelCase__     (self    :						Tuple		)						->   str:




							"""simple docstring"""


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

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






@require_faiss
class 							A    (       __a							):
					'''simple docstring'''






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




							"""simple docstring"""


							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      ,					10		)

							# single query
							lowercase__     			=		np.zeros(5      ,					dtype=np.floataa		)
							lowercase__     			=		1
							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__     			=		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   lowerCamelCase__     (self    :						int		)						->   Union[str, Any]:




							"""simple docstring"""


							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   lowerCamelCase__     (self    :						List[Any]		)						->   Optional[int]:




							"""simple docstring"""


							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   lowerCamelCase__     (self    :						Union[str, Any]		)						->   List[Any]:




							"""simple docstring"""


							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__     			=		index.search(_A		)
							self.assertGreater(scores[0]      ,					0		)
							self.assertEqual(indices[0]      ,					1		)






@require_faiss
def  UpperCamelCase					(	__magic_name__     :							Tuple     )   ->  Optional[Any]:
		"""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__     			=		index.search(lowerCAmelCase__     )
		assert scores[0] > 0
		assert indices[0] == 1







@require_elasticsearch
class 							A    (       __a							):
					'''simple docstring'''






					def   lowerCamelCase__     (self    :						str		)						->   Dict:




							"""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:
									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__     			=		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__     			=		index.search(_A      ,					request_timeout=30		)
									self.assertEqual(scores[0]      ,					1		)
									self.assertEqual(indices[0]      ,					0		)

									# batched queries
									lowercase__     			=		['''foo''', '''bar''', '''foobar''']
									lowercase__     			=		{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
									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__     			=		index.search_batch(_A      ,					request_timeout=30		)
									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		)





'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCamelCase__    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A						,						scheduler=_A )






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_A				:    int = 1						,						_A				:    int = 100						,						_A				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						_A				:    Optional[float] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, Any]													=				2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                  f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                  f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                  ''' process.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, Any]													=				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(_A						,						_A ) and len(_A ) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCAmelCase__			:		Any													=				self.unet(_A						,						_A ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
