






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''wavlm'''







      def __init__(      self      :						str ,					__UpperCAmelCase      :						Optional[int]=32 ,					__UpperCAmelCase      :						Dict=768 ,					__UpperCAmelCase      :						Any=12 ,					__UpperCAmelCase      :						Union[str, Any]=12 ,					__UpperCAmelCase      :						Any=3_072 ,					__UpperCAmelCase      :						List[Any]="gelu" ,					__UpperCAmelCase      :						str=0.1 ,					__UpperCAmelCase      :						int=0.1 ,					__UpperCAmelCase      :						Optional[int]=0.1 ,					__UpperCAmelCase      :						Any=0.0 ,					__UpperCAmelCase      :						Optional[Any]=0.1 ,					__UpperCAmelCase      :						List[Any]=0.1 ,					__UpperCAmelCase      :						Optional[Any]=0.02 ,					__UpperCAmelCase      :						Tuple=1e-5 ,					__UpperCAmelCase      :						Dict="group" ,					__UpperCAmelCase      :						Optional[int]="gelu" ,					__UpperCAmelCase      :						Optional[Any]=(512, 512, 512, 512, 512, 512, 512) ,					__UpperCAmelCase      :						Any=(5, 2, 2, 2, 2, 2, 2) ,					__UpperCAmelCase      :						Tuple=(10, 3, 3, 3, 3, 2, 2) ,					__UpperCAmelCase      :						Any=False ,					__UpperCAmelCase      :						List[Any]=128 ,					__UpperCAmelCase      :						Tuple=16 ,					__UpperCAmelCase      :						str=320 ,					__UpperCAmelCase      :						List[Any]=800 ,					__UpperCAmelCase      :						Optional[Any]=False ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						Dict=0.05 ,					__UpperCAmelCase      :						Union[str, Any]=10 ,					__UpperCAmelCase      :						Optional[int]=2 ,					__UpperCAmelCase      :						Dict=0.0 ,					__UpperCAmelCase      :						Any=10 ,					__UpperCAmelCase      :						Union[str, Any]=320 ,					__UpperCAmelCase      :						str=2 ,					__UpperCAmelCase      :						Any=0.1 ,					__UpperCAmelCase      :						Union[str, Any]=100 ,					__UpperCAmelCase      :						str=256 ,					__UpperCAmelCase      :						Any=256 ,					__UpperCAmelCase      :						Any=0.1 ,					__UpperCAmelCase      :						Union[str, Any]="mean" ,					__UpperCAmelCase      :						Tuple=False ,					__UpperCAmelCase      :						Optional[Any]=False ,					__UpperCAmelCase      :						List[Any]=256 ,					__UpperCAmelCase      :						Optional[int]=(512, 512, 512, 512, 1_500) ,					__UpperCAmelCase      :						Any=(5, 3, 3, 1, 1) ,					__UpperCAmelCase      :						Dict=(1, 2, 3, 1, 1) ,					__UpperCAmelCase      :						Optional[int]=512 ,					__UpperCAmelCase      :						Optional[Any]=80 ,					__UpperCAmelCase      :						Tuple=0 ,					__UpperCAmelCase      :						Union[str, Any]=1 ,					__UpperCAmelCase      :						Union[str, Any]=2 ,					__UpperCAmelCase      :						List[Any]=False ,					__UpperCAmelCase      :						List[str]=3 ,					__UpperCAmelCase      :						Union[str, Any]=2 ,					__UpperCAmelCase      :						Optional[Any]=3 ,					__UpperCAmelCase      :						str=None ,					**__UpperCAmelCase      :						Dict ,					)    ->Dict:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase ,					pad_token_id=__UpperCAmelCase ,					bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase  )
       a		  =      hidden_size
       a		  =      feat_extract_norm
       a		  =      feat_extract_activation
       a		  =      list(__UpperCAmelCase  )
       a		  =      list(__UpperCAmelCase  )
       a		  =      list(__UpperCAmelCase  )
       a		  =      conv_bias
       a		  =      num_buckets
       a		  =      max_bucket_distance
       a		  =      num_conv_pos_embeddings
       a		  =      num_conv_pos_embedding_groups
       a		  =      len(self.conv_dim  )
       a		  =      num_hidden_layers
       a		  =      intermediate_size
       a		  =      hidden_act
       a		  =      num_attention_heads
       a		  =      hidden_dropout
       a		  =      attention_dropout
       a		  =      activation_dropout
       a		  =      feat_proj_dropout
       a		  =      final_dropout
       a		  =      layerdrop
       a		  =      layer_norm_eps
       a		  =      initializer_range
       a		  =      num_ctc_classes
       a		  =      vocab_size
       a		  =      do_stable_layer_norm
       a		  =      use_weighted_layer_sum
       a		  =      classifier_proj_size

       if (
           (len(self.conv_stride  ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
           or (len(self.conv_dim  ) != self.num_feat_extract_layers)
       ):
        raise ValueError(
            '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
            ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
            F""" {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,"""
            F""" `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       a		  =      apply_spec_augment
       a		  =      mask_time_prob
       a		  =      mask_time_length
       a		  =      mask_time_min_masks
       a		  =      mask_feature_prob
       a		  =      mask_feature_length

       # parameters for pretraining with codevector quantized representations
       a		  =      num_codevectors_per_group
       a		  =      num_codevector_groups
       a		  =      contrastive_logits_temperature
       a		  =      num_negatives
       a		  =      codevector_dim
       a		  =      proj_codevector_dim
       a		  =      diversity_loss_weight

       # ctc loss
       a		  =      ctc_loss_reduction
       a		  =      ctc_zero_infinity

       # adapter
       a		  =      add_adapter
       a		  =      adapter_kernel_size
       a		  =      adapter_stride
       a		  =      num_adapter_layers
       a		  =      output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       a		  =      classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       a		  =      list(__UpperCAmelCase  )
       a		  =      list(__UpperCAmelCase  )
       a		  =      list(__UpperCAmelCase  )
       a		  =      xvector_output_dim







      @property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[int]:




       """simple docstring"""


       return functools.reduce(operator.mul ,					self.conv_stride ,					1  )




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     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			Dataset.from_dict({"filename": ["my_name-train" + "_" + str(lowercase ) for x in np.arange(30 ).tolist()]} )
					return dset



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					import faiss

					lowerCamelCase_						=			self._create_dummy_dataset()
					lowerCamelCase_						=			dset.map(
					    lambda lowercase						,  lowercase : {"vecs": i * np.ones(5						,  dtype=np.floataa )}						,  with_indices=lowercase						,  keep_in_memory=lowercase )
					lowerCamelCase_						=			dset.add_faiss_index("vecs"						,  batch_size=100						,  metric_type=faiss.METRIC_INNER_PRODUCT )
					lowerCamelCase_	,    lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					import faiss

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					import faiss

					lowerCamelCase_						=			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=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 )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			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(lowercase						,  partial(dset.get_nearest_examples						,  "vecs2"						,  np.ones(5						,  dtype=np.floataa ) ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					from elasticsearch import Elasticsearch

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

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




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					import faiss

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					import faiss

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					import faiss

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					import faiss

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

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





@require_faiss
def 			lowerCamelCase_ (				lowerCamelCase__    ):
				import faiss

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

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

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





@require_elasticsearch
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					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:
									lowerCamelCase_						=			Elasticsearch()
									lowerCamelCase_						=			{"acknowledged": True}
									lowerCamelCase_						=			ElasticSearchIndex(es_client=lowercase )
									mocked_bulk.return_value([(True, None)] * 3 )
									index.add_documents(["foo", "bar", "foobar"] )

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

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

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

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


