



'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCAmelCase									=   {
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

__lowerCAmelCase									=   AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Any:
		_a       ,					_a     :      Union[str, Any]        =							create_model(
		    'HTSAT-tiny'						,			'roberta'						,			lowerCAmelCase_						,			precision='fp32'						,			device='cuda:0' if torch.cuda.is_available() else 'cpu'						,			enable_fusion=lowerCAmelCase_						,			fusion_type='aff_2d' if enable_fusion else None						,			)
		return model, model_cfg



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		_a     :      Optional[int]        =							{}

		_a     :      Tuple        =							r'.*sequential.(\d+).*'
		_a     :      int        =							r'.*_projection.(\d+).*'

		for key, value in state_dict.items():
				# check if any key needs to be modified
				for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
						if key_to_modify in key:
								_a     :      Union[str, Any]        =							key.replace(lowerCAmelCase_						,			lowerCAmelCase_  )

				if re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						# replace sequential layers with list
						_a     :      List[str]        =							re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )

						_a     :      Optional[Any]        =							key.replace(f"""sequential.{sequential_layer}."""						,			f"""layers.{int(lowerCAmelCase_  )//3}.linear."""  )
				elif re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						_a     :      str        =							int(re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )  )

						# Because in CLAP they use `nn.Sequential`...
						_a     :      Optional[Any]        =							1 if projecton_layer == 0 else 2

						_a     :      int        =							key.replace(f"""_projection.{projecton_layer}."""						,			f"""_projection.linear{transformers_projection_layer}."""  )

				if "audio" and "qkv" in key:
						# split qkv into query key and value
						_a     :      str        =							value
						_a     :      List[str]        =							mixed_qkv.size(0  ) // 3

						_a     :      str        =							mixed_qkv[:qkv_dim]
						_a     :      int        =							mixed_qkv[qkv_dim : qkv_dim * 2]
						_a     :      Any        =							mixed_qkv[qkv_dim * 2 :]

						_a     :      List[Any]        =							query_layer
						_a     :      Union[str, Any]        =							key_layer
						_a     :      Tuple        =							value_layer
				else:
						_a     :      Dict        =							value

		return model_state_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a       ,					_a     :      Optional[Any]        =							init_clap(lowerCAmelCase_						,			enable_fusion=lowerCAmelCase_  )

		clap_model.eval()
		_a     :      Tuple        =							clap_model.state_dict()
		_a     :      Optional[int]        =							rename_state_dict(lowerCAmelCase_  )

		_a     :      List[str]        =							ClapConfig()
		_a     :      Tuple        =							enable_fusion
		_a     :      int        =							ClapModel(lowerCAmelCase_  )

		# ignore the spectrogram embedding layer
		model.load_state_dict(lowerCAmelCase_						,			strict=lowerCAmelCase_  )

		model.save_pretrained(lowerCAmelCase_  )
		transformers_config.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
							__lowerCAmelCase									=   parser.parse_args()

							convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)






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 )


