



"""simple docstring"""







import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
							import faiss
@require_faiss
class        __A       (	A_    ):







		'''simple docstring'''





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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			tempfile.mkdtemp()
				lowercase__   :    Tuple				=			8

				# DPR tok
				lowercase__   :    Optional[int]				=			[
				    '''[UNK]''',
				    '''[CLS]''',
				    '''[SEP]''',
				    '''[PAD]''',
				    '''[MASK]''',
				    '''want''',
				    '''##want''',
				    '''##ed''',
				    '''wa''',
				    '''un''',
				    '''runn''',
				    '''##ing''',
				    ''',''',
				    '''low''',
				    '''lowest''',
				]
				lowercase__   :    List[Any]				=			os.path.join(self.tmpdirname			,'''dpr_tokenizer'''						)
				os.makedirs(_snake_case			,exist_ok=_snake_case						)
				lowercase__   :    Dict				=			os.path.join(_snake_case			,DPR_VOCAB_FILES_NAMES['''vocab_file''']						)
				with open(self.vocab_file			,'''w'''			,encoding='''utf-8'''						) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]						)						)

				# BART tok
				lowercase__   :    List[str]				=			[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''\u0120''',
				    '''\u0120l''',
				    '''\u0120n''',
				    '''\u0120lo''',
				    '''\u0120low''',
				    '''er''',
				    '''\u0120lowest''',
				    '''\u0120newer''',
				    '''\u0120wider''',
				    '''<unk>''',
				]
				lowercase__   :    Union[str, Any]				=			dict(zip(_snake_case			,range(len(_snake_case						)						)						)						)
				lowercase__   :    Dict				=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
				lowercase__   :    List[Any]				=			{'''unk_token''': '''<unk>'''}

				lowercase__   :    Optional[int]				=			os.path.join(self.tmpdirname			,'''bart_tokenizer'''						)
				os.makedirs(_snake_case			,exist_ok=_snake_case						)
				lowercase__   :    Optional[Any]				=			os.path.join(_snake_case			,BART_VOCAB_FILES_NAMES['''vocab_file''']						)
				lowercase__   :    Dict				=			os.path.join(_snake_case			,BART_VOCAB_FILES_NAMES['''merges_file''']						)
				with open(self.vocab_file			,'''w'''			,encoding='''utf-8'''						) as fp:
						fp.write(json.dumps(_snake_case						) + '''\n'''						)
				with open(self.merges_file			,'''w'''			,encoding='''utf-8'''						) as fp:
						fp.write('''\n'''.join(_snake_case						)						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		DPRQuestionEncoderTokenizer:






				"""simple docstring"""



				return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,'''dpr_tokenizer'''						)						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		DPRContextEncoderTokenizer:






				"""simple docstring"""



				return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,'''dpr_tokenizer'''						)						)







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		BartTokenizer:






				"""simple docstring"""



				return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,'''bart_tokenizer'''						)						)







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






				"""simple docstring"""



				shutil.rmtree(self.tmpdirname						)







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






				"""simple docstring"""



				lowercase__   :    Any				=			Dataset.from_dict(
				    {
				        '''id''': ['''0''', '''1'''],
				        '''text''': ['''foo''', '''bar'''],
				        '''title''': ['''Foo''', '''Bar'''],
				        '''embeddings''': [np.ones(self.retrieval_vector_size						), 2 * np.ones(self.retrieval_vector_size						)],
				    }						)
				dataset.add_faiss_index('''embeddings'''			,string_factory='''Flat'''			,metric_type=faiss.METRIC_INNER_PRODUCT						)
				return dataset







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






				"""simple docstring"""



				lowercase__   :    int				=			self.get_dummy_dataset()
				lowercase__   :    Dict				=			RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size			,question_encoder=DPRConfig().to_dict()			,generator=BartConfig().to_dict()			,)
				with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
						lowercase__   :    str				=			dataset
						lowercase__   :    List[Any]				=			RagRetriever(
						    _snake_case			,question_encoder_tokenizer=self.get_dpr_tokenizer()			,generator_tokenizer=self.get_bart_tokenizer()			,)
				return retriever







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  bool						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.get_dummy_dataset()
				lowercase__   :    Optional[int]				=			RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size			,question_encoder=DPRConfig().to_dict()			,generator=BartConfig().to_dict()			,index_name='''custom'''			,)
				if from_disk:
						lowercase__   :    List[str]				=			os.path.join(self.tmpdirname			,'''dataset'''						)
						lowercase__   :    Any				=			os.path.join(self.tmpdirname			,'''index.faiss'''						)
						dataset.get_index('''embeddings'''						).save(os.path.join(self.tmpdirname			,'''index.faiss'''						)						)
						dataset.drop_index('''embeddings'''						)
						dataset.save_to_disk(os.path.join(self.tmpdirname			,'''dataset'''						)						)
						del dataset
						lowercase__   :    List[str]				=			RagRetriever(
						    _snake_case			,question_encoder_tokenizer=self.get_dpr_tokenizer()			,generator_tokenizer=self.get_bart_tokenizer()			,)
				else:
						lowercase__   :    str				=			RagRetriever(
						    _snake_case			,question_encoder_tokenizer=self.get_dpr_tokenizer()			,generator_tokenizer=self.get_bart_tokenizer()			,index=CustomHFIndex(config.retrieval_vector_size			,_snake_case						)			,)
				return retriever







		def 					UpperCAmelCase    (	self						:  Dict						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			Dataset.from_dict(
				    {
				        '''id''': ['''0''', '''1'''],
				        '''text''': ['''foo''', '''bar'''],
				        '''title''': ['''Foo''', '''Bar'''],
				        '''embeddings''': [np.ones(self.retrieval_vector_size + 1						), 2 * np.ones(self.retrieval_vector_size + 1						)],
				    }						)
				dataset.add_faiss_index('''embeddings'''			,string_factory='''Flat'''			,metric_type=faiss.METRIC_INNER_PRODUCT						)

				lowercase__   :    str				=			os.path.join(self.tmpdirname			,'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''						)
				dataset.save_faiss_index('''embeddings'''			,index_file_name + '''.index.dpr'''						)
				pickle.dump(dataset['''id''']			,open(index_file_name + '''.index_meta.dpr'''			,'''wb'''						)						)

				lowercase__   :    Union[str, Any]				=			os.path.join(self.tmpdirname			,'''psgs_w100.tsv.pkl'''						)
				lowercase__   :    Optional[Any]				=			{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
				pickle.dump(_snake_case			,open(_snake_case			,'''wb'''						)						)

				lowercase__   :    Dict				=			RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size			,question_encoder=DPRConfig().to_dict()			,generator=BartConfig().to_dict()			,index_name='''legacy'''			,index_path=self.tmpdirname			,)
				lowercase__   :    List[str]				=			RagRetriever(
				    _snake_case			,question_encoder_tokenizer=self.get_dpr_tokenizer()			,generator_tokenizer=self.get_bart_tokenizer()						)
				return retriever







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    Dict				=			1
				lowercase__   :    Tuple				=			self.get_dummy_canonical_hf_index_retriever()
				lowercase__   :    Dict				=			np.array(
				    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
				lowercase__		,			lowercase__		,			lowercase__   :    Union[str, Any]				=			retriever.retrieve(_snake_case			,n_docs=_snake_case						)
				self.assertEqual(retrieved_doc_embeds.shape			,(2, n_docs, self.retrieval_vector_size)						)
				self.assertEqual(len(_snake_case						)			,2						)
				self.assertEqual(sorted(doc_dicts[0]						)			,['''embeddings''', '''id''', '''text''', '''title''']						)
				self.assertEqual(len(doc_dicts[0]['''id''']						)			,_snake_case						)
				self.assertEqual(doc_dicts[0]['''id'''][0]			,'''1'''						)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['''id'''][0]			,'''0'''						)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()			,[[1], [0]]						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    int				=			self.get_dummy_canonical_hf_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
						with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
								lowercase__   :    Optional[Any]				=			self.get_dummy_dataset()
								retriever.save_pretrained(_snake_case						)
								lowercase__   :    Optional[int]				=			RagRetriever.from_pretrained(_snake_case						)
								self.assertIsInstance(_snake_case			,_snake_case						)
								lowercase__   :    Dict				=			np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
								lowercase__   :    int				=			retriever.retrieve(_snake_case			,n_docs=1						)
								self.assertTrue(out is not None						)







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






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			1
				lowercase__   :    str				=			self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case						)
				lowercase__   :    int				=			np.array(
				    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
				lowercase__		,			lowercase__		,			lowercase__   :    Dict				=			retriever.retrieve(_snake_case			,n_docs=_snake_case						)
				self.assertEqual(retrieved_doc_embeds.shape			,(2, n_docs, self.retrieval_vector_size)						)
				self.assertEqual(len(_snake_case						)			,2						)
				self.assertEqual(sorted(doc_dicts[0]						)			,['''embeddings''', '''id''', '''text''', '''title''']						)
				self.assertEqual(len(doc_dicts[0]['''id''']						)			,_snake_case						)
				self.assertEqual(doc_dicts[0]['''id'''][0]			,'''1'''						)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['''id'''][0]			,'''0'''						)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()			,[[1], [0]]						)







		def 					UpperCAmelCase    (	self						:  str						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case						)
				with tempfile.TemporaryDirectory() as tmp_dirname:
						retriever.save_pretrained(_snake_case						)
						lowercase__   :    Optional[int]				=			RagRetriever.from_pretrained(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)
						lowercase__   :    Dict				=			np.array(
						    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
						lowercase__   :    str				=			retriever.retrieve(_snake_case			,n_docs=1						)
						self.assertTrue(out is not None						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Any				=			1
				lowercase__   :    List[str]				=			self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case						)
				lowercase__   :    Optional[int]				=			np.array(
				    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
				lowercase__		,			lowercase__		,			lowercase__   :    List[str]				=			retriever.retrieve(_snake_case			,n_docs=_snake_case						)
				self.assertEqual(retrieved_doc_embeds.shape			,(2, n_docs, self.retrieval_vector_size)						)
				self.assertEqual(len(_snake_case						)			,2						)
				self.assertEqual(sorted(doc_dicts[0]						)			,['''embeddings''', '''id''', '''text''', '''title''']						)
				self.assertEqual(len(doc_dicts[0]['''id''']						)			,_snake_case						)
				self.assertEqual(doc_dicts[0]['''id'''][0]			,'''1'''						)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['''id'''][0]			,'''0'''						)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()			,[[1], [0]]						)







		def 					UpperCAmelCase    (	self						:  str						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Any				=			self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case						)
				with tempfile.TemporaryDirectory() as tmp_dirname:
						retriever.save_pretrained(_snake_case						)
						lowercase__   :    Optional[int]				=			RagRetriever.from_pretrained(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)
						lowercase__   :    List[str]				=			np.array(
						    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
						lowercase__   :    int				=			retriever.retrieve(_snake_case			,n_docs=1						)
						self.assertTrue(out is not None						)







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






				"""simple docstring"""



				lowercase__   :    str				=			1
				lowercase__   :    Dict				=			self.get_dummy_legacy_index_retriever()
				lowercase__   :    str				=			np.array(
				    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
				lowercase__		,			lowercase__		,			lowercase__   :    int				=			retriever.retrieve(_snake_case			,n_docs=_snake_case						)
				self.assertEqual(retrieved_doc_embeds.shape			,(2, n_docs, self.retrieval_vector_size)						)
				self.assertEqual(len(_snake_case						)			,2						)
				self.assertEqual(sorted(doc_dicts[0]						)			,['''text''', '''title''']						)
				self.assertEqual(len(doc_dicts[0]['''text''']						)			,_snake_case						)
				self.assertEqual(doc_dicts[0]['''text'''][0]			,'''bar'''						)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['''text'''][0]			,'''foo'''						)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()			,[[1], [0]]						)







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






				"""simple docstring"""



				lowercase__   :    Dict				=			self.get_dummy_legacy_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
						retriever.save_pretrained(_snake_case						)
						lowercase__   :    Optional[int]				=			RagRetriever.from_pretrained(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)
						lowercase__   :    Union[str, Any]				=			np.array(
						    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
						lowercase__   :    Dict				=			retriever.retrieve(_snake_case			,n_docs=1						)
						self.assertTrue(out is not None						)







		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		List[str]:






				"""simple docstring"""



				import torch

				lowercase__   :    int				=			1
				lowercase__   :    Tuple				=			self.get_dummy_canonical_hf_index_retriever()
				lowercase__   :    Union[str, Any]				=			[[5, 7], [10, 11]]
				lowercase__   :    str				=			np.array(
				    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
				lowercase__   :    Tuple				=			retriever(_snake_case			,_snake_case			,prefix=retriever.config.generator.prefix			,n_docs=_snake_case						)
				lowercase__		,			lowercase__		,			lowercase__   :    int				=			(
				    out['''context_input_ids'''],
				    out['''context_attention_mask'''],
				    out['''retrieved_doc_embeds'''],
				)
				self.assertEqual(retrieved_doc_embeds.shape			,(2, n_docs, self.retrieval_vector_size)						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertIsInstance(_snake_case			,np.ndarray						)

				lowercase__   :    Optional[Any]				=			retriever(
				    _snake_case			,_snake_case			,prefix=retriever.config.generator.prefix			,n_docs=_snake_case			,return_tensors='''pt'''			,)
				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Tuple				=			(  # noqa: F841
				    out['''context_input_ids'''],
				    out['''context_attention_mask'''],
				    out['''retrieved_doc_embeds'''],
				    out['''doc_ids'''],
				)
				self.assertEqual(retrieved_doc_embeds.shape			,(2, n_docs, self.retrieval_vector_size)						)
				self.assertIsInstance(_snake_case			,torch.Tensor						)
				self.assertIsInstance(_snake_case			,torch.Tensor						)
				self.assertIsInstance(_snake_case			,torch.Tensor						)







		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def 					UpperCAmelCase    (	self						:  Dict						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.get_dpr_ctx_encoder_tokenizer()
				lowercase__   :    Dict				=			1
				lowercase__   :    str				=			self.get_dummy_custom_hf_index_retriever(from_disk=_snake_case						)
				retriever.set_ctx_encoder_tokenizer(_snake_case						)

				lowercase__   :    Any				=			[[5, 7], [10, 11]]
				lowercase__   :    Dict				=			np.array(
				    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]			,dtype=np.floataa						)
				lowercase__   :    Union[str, Any]				=			retriever(_snake_case			,_snake_case			,prefix=retriever.config.generator.prefix			,n_docs=_snake_case						)

				self.assertEqual(
				    len(_snake_case						)			,6						)  # check whether the retriever output consist of 6 attributes including tokenized docs
				self.assertEqual(
				    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')						)			,_snake_case						)  # check for doc token related keys in dictionary.







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase       :   Any				       =							{
    "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",
}

__UpperCAmelCase       :   List[str]				       =							AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







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







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Optional[Any]   =						{}

  __snake_case:						int   =						r""".*sequential.(\d+).*"""
  __snake_case:						List[str]   =						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:
        __snake_case:						Tuple   =						key.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

    if re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      # replace sequential layers with list
      __snake_case:						Optional[int]   =						re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__).group(1)

      __snake_case:						str   =						key.replace(F'''sequential.{sequential_layer}.'''					,					F'''layers.{int(SCREAMING_SNAKE_CASE__)//3}.linear.''')
    elif re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__):
      __snake_case:						Any   =						int(re.match(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__).group(1))

      # Because in CLAP they use `nn.Sequential`...
      __snake_case:						Dict   =						1 if projecton_layer == 0 else 2

      __snake_case:						Any   =						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
      __snake_case:						List[str]   =						value
      __snake_case:						Optional[Any]   =						mixed_qkv.size(0) // 3

      __snake_case:						Union[str, Any]   =						mixed_qkv[:qkv_dim]
      __snake_case:						Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
      __snake_case:						int   =						mixed_qkv[qkv_dim * 2 :]

      __snake_case:						Optional[Any]   =						query_layer
      __snake_case:						str   =						key_layer
      __snake_case:						int   =						value_layer
    else:
      __snake_case:						Dict   =						value

  return model_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Optional[Any]:
  __snake_case  ,   __snake_case:						List[str]   =						init_clap(SCREAMING_SNAKE_CASE__					,					enable_fusion=SCREAMING_SNAKE_CASE__)

  clap_model.eval()
  __snake_case:						List[str]   =						clap_model.state_dict()
  __snake_case:						Optional[int]   =						rename_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any   =						ClapConfig()
  __snake_case:						Dict   =						enable_fusion
  __snake_case:						List[str]   =						ClapModel(SCREAMING_SNAKE_CASE__)

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

  model.save_pretrained(SCREAMING_SNAKE_CASE__)
  transformers_config.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase       :   Union[str, Any]				       =							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")
      __UpperCAmelCase       :   Tuple				       =							parser.parse_args()

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






