






"""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


lowerCAmelCase_	:    Union[str, Any]            =      pytest.mark.integration





@require_faiss
class   UpperCamelCase_ (       a_  ):



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



         """simple docstring"""





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



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



         """simple docstring"""





         import faiss

         UpperCAmelCase	    =			self._create_dummy_dataset()
         UpperCAmelCase	    =			dset.map(
             lambda snake_case__ ,		snake_case__				: {"vecs": i * np.ones(5 ,		dtype=np.floataa				)} ,		with_indices=snake_case__ ,		keep_in_memory=snake_case__				)
         UpperCAmelCase	    =			dset.add_faiss_index("""vecs""" ,		batch_size=1_00 ,		metric_type=faiss.METRIC_INNER_PRODUCT				)
         UpperCAmelCase    ,       UpperCAmelCase	    =			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				)			->			Optional[Any]:



         """simple docstring"""





         import faiss

         UpperCAmelCase	    =			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=1_00 ,		metric_type=faiss.METRIC_INNER_PRODUCT ,		)
         UpperCAmelCase    ,       UpperCAmelCase	    =			dset.get_nearest_examples("""vecs""" ,		np.ones(5 ,		dtype=np.floataa				)				)
         self.assertEqual(examples["""filename"""][0] ,		"""my_name-train_29"""				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         import faiss

         UpperCAmelCase	    =			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				)

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



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			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 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         from elasticsearch import Elasticsearch

         UpperCAmelCase	    =			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:
                UpperCAmelCase	    =			{"""acknowledged""": True}
                mocked_bulk.return_value([(True, None)] * 30				)
                UpperCAmelCase	    =			{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
                UpperCAmelCase	    =			Elasticsearch()

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





@require_faiss
class   UpperCamelCase_ (       a_  ):



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         import faiss

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



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         import faiss

         UpperCAmelCase	    =			FaissIndex(string_factory="""Flat"""				)
         index.add_vectors(np.eye(5 ,		dtype=np.floataa				)				)
         self.assertIsInstance(index.faiss_index ,		faiss.IndexFlat				)
         UpperCAmelCase	    =			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__				):
                UpperCAmelCase	    =			FaissIndex(string_factory="""Flat""" ,		custom_index=faiss.IndexFlat(5				)				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         import faiss

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



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         import faiss

         UpperCAmelCase	    =			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				)
                UpperCAmelCase	    =			FaissIndex.load(tmp_file.name				)
         os.unlink(tmp_file.name				)

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






@require_faiss
def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       import faiss

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

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

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





@require_elasticsearch
class   UpperCamelCase_ (       a_  ):



  def 		UpperCamelCase_				(      self				)			->			str:



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

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

                # single query with timeout
                UpperCAmelCase	    =			"""foo"""
                UpperCAmelCase	    =			{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
                UpperCAmelCase    ,       UpperCAmelCase	    =			index.search(snake_case__ ,		request_timeout=30				)
                self.assertEqual(scores[0] ,		1				)
                self.assertEqual(indices[0] ,		0				)

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








"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""projector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""projector.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""model.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForXVector.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""connector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              UpperCAmelCase	    =			downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCAmelCase	    =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       UpperCAmelCase	    =			downstream_dict["""objective.W"""]
       return model






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)

       UpperCAmelCase	    =			checkpoint["""Downstream"""]

       UpperCAmelCase	    =			UniSpeechSatConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(
           lowerCAmelCase	,      return_attention_mask=lowerCAmelCase	,      do_normalize=lowerCAmelCase		)

       UpperCAmelCase	    =			hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""		):
              UpperCAmelCase	    =			convert_classification(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForAudioFrameClassification"""		):
              UpperCAmelCase	    =			convert_diarization(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForXVector"""		):
              UpperCAmelCase	    =			convert_xvector(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       else:
              raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''		)

       if hf_config.use_weighted_layer_sum:
              UpperCAmelCase	    =			checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(lowerCAmelCase		)
       hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    List[Any]            =      argparse.ArgumentParser()
 parser.add_argument(
     '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
 )
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
 parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
 lowerCAmelCase_	:    str            =      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
