"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
       from huggingface_hub import snapshot_download
       from pyctcdecode import BeamSearchDecoderCTC

       from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
       from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
       from transformers import WavaVecaForCTC





@require_pyctcdecode
class 					_A		(			unittest.TestCase      ):

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
            lowercase								=      dict(zip(a_						,			range(len(a_		)		)		)		)

            lowercase								=      {
                "unk_token": "<unk>",
                "bos_token": "<s>",
                "eos_token": "</s>",
            }
            lowercase								=      {
                "feature_size": 1,
                "padding_value": 0.0,
                "sampling_rate": 1_6000,
                "return_attention_mask": False,
                "do_normalize": True,
            }

            lowercase								=      tempfile.mkdtemp()
            lowercase								=      os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]		)
            lowercase								=      os.path.join(self.tmpdirname						,			a_		)
            with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""		) as fp:
                  fp.write(json.dumps(a_		) + """\n"""		)

            with open(self.feature_extraction_file						,			"""w"""						,			encoding="""utf-8"""		) as fp:
                  fp.write(json.dumps(a_		) + """\n"""		)

            # load decoder from hub
            lowercase								=      "hf-internal-testing/ngram-beam-search-decoder"

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.add_kwargs_tokens_map.copy()
            kwargs.update(a_		)
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,			**a_		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,			**a_		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name						,			**a_		)

      def 							A__			(		self		):







            """simple docstring"""



            shutil.rmtree(self.tmpdirname		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            processor.save_pretrained(self.tmpdirname		)
            lowercase								=      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname		)

            # tokenizer
            self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer.get_vocab()		)
            self.assertIsInstance(processor.tokenizer						,			a_		)

            # feature extractor
            self.assertEqual(processor.feature_extractor.to_json_string()						,			feature_extractor.to_json_string()		)
            self.assertIsInstance(processor.feature_extractor						,			a_		)

            # decoder
            self.assertEqual(processor.decoder._alphabet.labels						,			decoder._alphabet.labels		)
            self.assertEqual(
                processor.decoder.model_container[decoder._model_key]._unigram_set						,			decoder.model_container[decoder._model_key]._unigram_set						,			)
            self.assertIsInstance(processor.decoder						,			a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessorWithLM(
                tokenizer=self.get_tokenizer()						,			feature_extractor=self.get_feature_extractor()						,			decoder=self.get_decoder()		)
            processor.save_pretrained(self.tmpdirname		)

            # make sure that error is thrown when decoder alphabet doesn't match
            lowercase								=      WavaVecaProcessorWithLM.from_pretrained(
                self.tmpdirname						,			alpha=5.0						,			beta=3.0						,			score_boundary=-7.0						,			unk_score_offset=3		)

            # decoder
            self.assertEqual(processor.language_model.alpha						,			5.0		)
            self.assertEqual(processor.language_model.beta						,			3.0		)
            self.assertEqual(processor.language_model.score_boundary						,			-7.0		)
            self.assertEqual(processor.language_model.unk_score_offset						,			3		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizer()
            # add token to trigger raise
            tokenizer.add_tokens(["""xx"""]		)
            with self.assertRaisesRegex(a_						,			"""include"""		):
                  WavaVecaProcessorWithLM(
                      tokenizer=a_						,			feature_extractor=self.get_feature_extractor()						,			decoder=self.get_decoder()		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            lowercase								=      floats_list((3, 1000)		)

            lowercase								=      feature_extractor(a_						,			return_tensors="""np"""		)
            lowercase								=      processor(a_						,			return_tensors="""np"""		)

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()						,			input_processor[key].sum()						,			delta=1E-2		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            lowercase								=      "This is a test string"

            lowercase								=      processor(text=a_		)

            lowercase								=      tokenizer(a_		)

            for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]		)

      def 							A__			(		self						,			__lowerCAmelCase=(2, 10, 16)						,			__lowerCAmelCase=77		):







            """simple docstring"""



            np.random.seed(a_		)
            return np.random.rand(*a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            lowercase								=      self._get_dummy_logits(shape=(10, 16)						,			seed=13		)

            lowercase								=      processor.decode(a_		)

            lowercase								=      decoder.decode_beams(a_		)[0]

            self.assertEqual(decoded_decoder[0]						,			decoded_processor.text		)
            self.assertEqual("""</s> <s> </s>"""						,			decoded_processor.text		)
            self.assertEqual(decoded_decoder[-2]						,			decoded_processor.logit_score		)
            self.assertEqual(decoded_decoder[-1]						,			decoded_processor.lm_score		)

      @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]		)
      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            lowercase								=      self._get_dummy_logits()

            # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
            #       otherwise, the LM won't be available to the pool's sub-processes.
            # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
            if pool_context is None:
                  lowercase								=      processor.batch_decode(a_		)
            else:
                  with get_context(a_		).Pool() as pool:
                        lowercase								=      processor.batch_decode(a_						,			a_		)

            lowercase								=      list(a_		)

            with get_context("""fork"""		).Pool() as p:
                  lowercase								=      decoder.decode_beams_batch(a_						,			a_		)

            lowercase								=      [], [], []
            for beams in decoded_beams:
                  texts_decoder.append(beams[0][0]		)
                  logit_scores_decoder.append(beams[0][-2]		)
                  lm_scores_decoder.append(beams[0][-1]		)

            self.assertListEqual(a_						,			decoded_processor.text		)
            self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]						,			decoded_processor.text		)
            self.assertListEqual(a_						,			decoded_processor.logit_score		)
            self.assertListEqual(a_						,			decoded_processor.lm_score		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            lowercase								=      self._get_dummy_logits()

            lowercase								=      15
            lowercase								=      -2_0.0
            lowercase								=      -4.0

            lowercase								=      processor.batch_decode(
                a_						,			beam_width=a_						,			beam_prune_logp=a_						,			token_min_logp=a_						,			)
            lowercase								=      decoded_processor_out.text

            lowercase								=      list(a_		)

            with get_context("""fork"""		).Pool() as pool:
                  lowercase								=      decoder.decode_beams_batch(
                      a_						,			a_						,			beam_width=a_						,			beam_prune_logp=a_						,			token_min_logp=a_						,			)

            lowercase								=      [d[0][0] for d in decoded_decoder_out]
            lowercase								=      [d[0][2] for d in decoded_decoder_out]
            lowercase								=      [d[0][3] for d in decoded_decoder_out]

            self.assertListEqual(a_						,			a_		)
            self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]						,			a_		)

            self.assertTrue(np.array_equal(a_						,			decoded_processor_out.logit_score		)		)
            self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7]						,			a_						,			atol=1E-3		)		)

            self.assertTrue(np.array_equal(a_						,			decoded_processor_out.lm_score		)		)
            self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]						,			a_						,			atol=1E-3		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            lowercase								=      self._get_dummy_logits()

            lowercase								=      2.0
            lowercase								=      5.0
            lowercase								=      -2_0.0
            lowercase								=      True

            lowercase								=      processor.batch_decode(
                a_						,			alpha=a_						,			beta=a_						,			unk_score_offset=a_						,			lm_score_boundary=a_						,			)
            lowercase								=      decoded_processor_out.text

            lowercase								=      list(a_		)
            decoder.reset_params(
                alpha=a_						,			beta=a_						,			unk_score_offset=a_						,			lm_score_boundary=a_						,			)

            with get_context("""fork"""		).Pool() as pool:
                  lowercase								=      decoder.decode_beams_batch(
                      a_						,			a_						,			)

            lowercase								=      [d[0][0] for d in decoded_decoder_out]

            self.assertListEqual(a_						,			a_		)
            self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]						,			a_		)
            lowercase								=      processor.decoder.model_container[processor.decoder._model_key]
            self.assertEqual(lm_model.alpha						,			2.0		)
            self.assertEqual(lm_model.beta						,			5.0		)
            self.assertEqual(lm_model.unk_score_offset						,			-2_0.0		)
            self.assertEqual(lm_model.score_boundary						,			a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""		)

            lowercase								=      processor.decoder.model_container[processor.decoder._model_key]
            lowercase								=      Path(language_model._kenlm_model.path.decode("""utf-8"""		)		).parent.parent.absolute()

            lowercase								=      os.listdir(a_		)
            lowercase								=      ["alphabet.json", "language_model"]

            downloaded_decoder_files.sort()
            expected_decoder_files.sort()

            # test that only decoder relevant files from
            # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
            # are downloaded and none of the rest (e.g. README.md, ...)
            self.assertListEqual(a_						,			a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      snapshot_download("""hf-internal-testing/processor_with_lm"""		)

            lowercase								=      WavaVecaProcessorWithLM.from_pretrained(a_		)

            lowercase								=      processor.decoder.model_container[processor.decoder._model_key]
            lowercase								=      Path(language_model._kenlm_model.path.decode("""utf-8"""		)		).parent.parent.absolute()

            lowercase								=      os.listdir(a_		)
            lowercase								=      os.listdir(a_		)

            local_decoder_files.sort()
            expected_decoder_files.sort()

            # test that both decoder form hub and local files in cache are the same
            self.assertListEqual(a_						,			a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""		)
            lowercase								=      AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""		)

            lowercase								=      floats_list((3, 1000)		)

            lowercase								=      processor_wavaveca(a_						,			return_tensors="""np"""		)
            lowercase								=      processor_auto(a_						,			return_tensors="""np"""		)

            for key in input_wavaveca.keys():
                  self.assertAlmostEqual(input_wavaveca[key].sum()						,			input_auto[key].sum()						,			delta=1E-2		)

            lowercase								=      self._get_dummy_logits()

            lowercase								=      processor_wavaveca.batch_decode(a_		)
            lowercase								=      processor_auto.batch_decode(a_		)

            self.assertListEqual(decoded_wavaveca.text						,			decoded_auto.text		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_feature_extractor()
            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_decoder()

            lowercase								=      WavaVecaProcessorWithLM(tokenizer=a_						,			feature_extractor=a_						,			decoder=a_		)

            self.assertListEqual(
                processor.model_input_names						,			feature_extractor.model_input_names						,			msg="""`processor` and `feature_extractor` model input names do not match"""						,			)

      @staticmethod
      def 							A__			(		__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      [d[key] for d in offsets]
            return retrieved_list

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""		)
            lowercase								=      self._get_dummy_logits()[0]

            lowercase								=      processor.decode(a_						,			output_word_offsets=a_		)
            # check Wav2Vec2CTCTokenizerOutput keys for word
            self.assertEqual(len(outputs.keys()		)						,			4		)
            self.assertTrue("""text""" in outputs		)
            self.assertTrue("""word_offsets""" in outputs		)
            self.assertTrue(isinstance(a_						,			a_		)		)

            self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]						,			"""word"""		)		)						,			outputs.text		)
            self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]						,			"""word"""		)						,			["""<s>""", """<s>""", """</s>"""]		)
            self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]						,			"""start_offset"""		)						,			[0, 2, 4]		)
            self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]						,			"""end_offset"""		)						,			[1, 3, 5]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""		)
            lowercase								=      self._get_dummy_logits()

            lowercase								=      processor.batch_decode(a_						,			output_word_offsets=a_		)

            # check Wav2Vec2CTCTokenizerOutput keys for word
            self.assertEqual(len(outputs.keys()		)						,			4		)
            self.assertTrue("""text""" in outputs		)
            self.assertTrue("""word_offsets""" in outputs		)
            self.assertTrue(isinstance(a_						,			a_		)		)

            self.assertListEqual(
                [""" """.join(self.get_from_offsets(a_						,			"""word"""		)		) for o in outputs["""word_offsets"""]]						,			outputs.text		)
            self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]						,			"""word"""		)						,			["""<s>""", """<s>""", """</s>"""]		)
            self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]						,			"""start_offset"""		)						,			[0, 2, 4]		)
            self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]						,			"""end_offset"""		)						,			[1, 3, 5]		)







      @slow
      @require_torch
      @require_torchaudio
      def 							A__			(		self		):







            """simple docstring"""



            import torch

            lowercase								=      load_dataset("""common_voice"""						,			"""en"""						,			split="""train"""						,			streaming=a_		)
            lowercase								=      ds.cast_column("""audio"""						,			datasets.Audio(sampling_rate=1_6000		)		)
            lowercase								=      iter(a_		)
            lowercase								=      next(a_		)

            lowercase								=      AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""		)
            lowercase								=      WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""		)

            # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
            lowercase								=      processor(sample["""audio"""]["""array"""]						,			return_tensors="""pt"""		).input_values

            with torch.no_grad():
                  lowercase								=      model(a_		).logits.cpu().numpy()

            lowercase								=      processor.decode(logits[0]						,			output_word_offsets=a_		)

            lowercase								=      model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
            lowercase								=      [
                {
                    "start_time": d["start_offset"] * time_offset,
                    "end_time": d["end_offset"] * time_offset,
                    "word": d["word"],
                }
                for d in output["word_offsets"]
            ]

            lowercase								=      "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

            # output words
            self.assertEqual(""" """.join(self.get_from_offsets(a_						,			"""word"""		)		)						,			a_		)
            self.assertEqual(""" """.join(self.get_from_offsets(a_						,			"""word"""		)		)						,			output.text		)

            # output times
            lowercase								=      torch.tensor(self.get_from_offsets(a_						,			"""start_time"""		)		)
            lowercase								=      torch.tensor(self.get_from_offsets(a_						,			"""end_time"""		)		)

            # fmt: off
            lowercase								=      torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]		)
            lowercase								=      torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]		)
            # fmt: on

            self.assertTrue(torch.allclose(a_						,			a_						,			atol=0.0_1		)		)
            self.assertTrue(torch.allclose(a_						,			a_						,			atol=0.0_1		)		)










"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]=False			)    ->			List[str]:



			'''simple docstring'''
			a__ ,	a__  :	Optional[int]		 =			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      lowercase__ (					lowerCAmelCase__   :			Optional[int]			)    ->			Dict:



			'''simple docstring'''
			a__  :	Any		 =			{}

			a__  :	Tuple		 =			R".*sequential.(\d+).*"
			a__  :	Tuple		 =			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__  :	str		 =			key.replace(lowerCAmelCase__							,			lowerCAmelCase__			)

						if re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									# replace sequential layers with list
									a__  :	Optional[Any]		 =			re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)

									a__  :	int		 =			key.replace(F"sequential.{sequential_layer}."							,			F"layers.{int(lowerCAmelCase__			)//3}.linear."			)
						elif re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									a__  :	Tuple		 =			int(re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)			)

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

									a__  :	Dict		 =			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__  :	List[Any]		 =			value
									a__  :	List[Any]		 =			mixed_qkv.size(0			) // 3

									a__  :	Optional[int]		 =			mixed_qkv[:qkv_dim]
									a__  :	List[str]		 =			mixed_qkv[qkv_dim : qkv_dim * 2]
									a__  :	Optional[Any]		 =			mixed_qkv[qkv_dim * 2 :]

									a__  :	Tuple		 =			query_layer
									a__  :	int		 =			key_layer
									a__  :	Optional[int]		 =			value_layer
						else:
									a__  :	List[str]		 =			value

			return model_state_dict






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Tuple=False			)    ->			Tuple:



			'''simple docstring'''
			a__ ,	a__  :	Tuple		 =			init_clap(lowerCAmelCase__							,			enable_fusion=lowerCAmelCase__			)

			clap_model.eval()
			a__  :	Optional[int]		 =			clap_model.state_dict()
			a__  :	Optional[Any]		 =			rename_state_dict(lowerCAmelCase__			)

			a__  :	Union[str, Any]		 =			ClapConfig()
			a__  :	Dict		 =			enable_fusion
			a__  :	Any		 =			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__":
		__UpperCAmelCase						=			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						=			parser.parse_args()

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