


'''simple docstring'''

import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
	from .features import FeatureType


_UpperCAmelCase				: List[str]							    =				False, False, False


@dataclass
class       a__				:







				"""simple docstring"""



				__UpperCamelCase    :		Optional[int]	=	None
				__UpperCamelCase    :		Dict	=	True
				__UpperCamelCase    :		Union[str, Any]	=	True
				__UpperCamelCase    :		Union[str, Any]	=	None
				# Automatically constructed
				__UpperCamelCase    :		int	=	"dict"
				__UpperCamelCase    :		int	=	pa.struct({"bytes": pa.binary(), "path": pa.string()}      )
				__UpperCamelCase    :		List[Any]	=	field(default="Audio"							,						init=__UpperCAmelCase							,						repr=__UpperCAmelCase      )




				def __call__(self		):
										return self.pa_type




				def 					_snake_case  (self       ,			__lowercase		):







										try:
																import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
										except ImportError as err:
																raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''		) from err
										if isinstance(lowerCAmelCase_       ,			lowerCAmelCase_		):
																return {"bytes": None, "path": value}
										elif isinstance(lowerCAmelCase_       ,			lowerCAmelCase_		):
																return {"bytes": value, "path": None}
										elif "array" in value:
																# convert the audio array to wav bytes
																__lowerCAmelCase							       =				BytesIO()
																sf.write(lowerCAmelCase_       ,			value['''array''']       ,			value['''sampling_rate''']       ,			format='''wav'''		)
																return {"bytes": buffer.getvalue(), "path": None}
										elif value.get('''path'''		) is not None and os.path.isfile(value['''path''']		):
																# we set "bytes": None to not duplicate the data if they're already available locally
																if value["path"].endswith('''pcm'''		):
																						# "PCM" only has raw audio bytes
																						if value.get('''sampling_rate'''		) is None:
																												# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																												raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''		)
																						if value.get('''bytes'''		):
																												# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																												__lowerCAmelCase							       =				np.frombuffer(value['''bytes''']       ,			dtype=np.intaa		).astype(np.floataa		) / 3_27_67
																						else:
																												__lowerCAmelCase							       =				np.memmap(value['''path''']       ,			dtype='''h'''       ,			mode='''r'''		).astype(np.floataa		) / 3_27_67

																						__lowerCAmelCase							       =				BytesIO(bytes()		)
																						sf.write(lowerCAmelCase_       ,			lowerCAmelCase_       ,			value['''sampling_rate''']       ,			format='''wav'''		)
																						return {"bytes": buffer.getvalue(), "path": None}
																else:
																						return {"bytes": None, "path": value.get('''path'''		)}
										elif value.get('''bytes'''		) is not None or value.get('''path'''		) is not None:
																# store the audio bytes, and path is used to infer the audio format using the file extension
																return {"bytes": value.get('''bytes'''		), "path": value.get('''path'''		)}
										else:
																raise ValueError(
																    F"""An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}."""		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):







										if not self.decode:
																raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''		)

										__lowerCAmelCase       ,					__lowerCAmelCase							       =				(value['''path'''], BytesIO(value['''bytes''']		)) if value['''bytes'''] is not None else (value['''path'''], None)
										if path is None and file is None:
																raise ValueError(F"""An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}."""		)

										try:
																import librosa
																import soundfile as sf
										except ImportError as err:
																raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''		) from err

										__lowerCAmelCase							       =				xsplitext(lowerCAmelCase_		)[1][1:].lower() if path is not None else None
										if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
																raise RuntimeError(
																    '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
																    '''You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. '''		)
										elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
																raise RuntimeError(
																    '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
																    '''You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. '''		)

										if file is None:
																__lowerCAmelCase							       =				token_per_repo_id or {}
																__lowerCAmelCase							       =				path.split('''::'''		)[-1]
																try:
																						__lowerCAmelCase							       =				string_to_dict(lowerCAmelCase_       ,			config.HUB_DATASETS_URL		)['''repo_id''']
																						__lowerCAmelCase							       =				token_per_repo_id[repo_id]
																except (ValueError, KeyError):
																						__lowerCAmelCase							       =				None

																with xopen(lowerCAmelCase_       ,			'''rb'''       ,			use_auth_token=lowerCAmelCase_		) as f:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				sf.read(lowerCAmelCase_		)

										else:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				sf.read(lowerCAmelCase_		)

										__lowerCAmelCase							       =				array.T
										if self.mono:
																__lowerCAmelCase							       =				librosa.to_mono(lowerCAmelCase_		)
										if self.sampling_rate and self.sampling_rate != sampling_rate:
																__lowerCAmelCase							       =				librosa.resample(lowerCAmelCase_       ,			orig_sr=lowerCAmelCase_       ,			target_sr=self.sampling_rate		)
																__lowerCAmelCase							       =				self.sampling_rate

										return {"path": path, "array": array, "sampling_rate": sampling_rate}




				def 					_snake_case  (self		):
										from .features import Value

										if self.decode:
																raise ValueError('''Cannot flatten a decoded Audio feature.'''		)
										return {
										    "bytes": Value('''binary'''		),
										    "path": Value('''string'''		),
										}




				def 					_snake_case  (self       ,			__lowercase		):







										if pa.types.is_string(storage.type		):
																__lowerCAmelCase							       =				pa.array([None] * len(lowerCAmelCase_		)       ,			type=pa.binary()		)
																__lowerCAmelCase							       =				pa.StructArray.from_arrays([bytes_array, storage]       ,			['''bytes''', '''path''']       ,			mask=storage.is_null()		)
										elif pa.types.is_binary(storage.type		):
																__lowerCAmelCase							       =				pa.array([None] * len(lowerCAmelCase_		)       ,			type=pa.string()		)
																__lowerCAmelCase							       =				pa.StructArray.from_arrays([storage, path_array]       ,			['''bytes''', '''path''']       ,			mask=storage.is_null()		)
										elif pa.types.is_struct(storage.type		) and storage.type.get_all_field_indices('''array'''		):
																__lowerCAmelCase							       =				pa.array([Audio().encode_example(lowerCAmelCase_		) if x is not None else None for x in storage.to_pylist()]		)
										elif pa.types.is_struct(storage.type		):
																if storage.type.get_field_index('''bytes'''		) >= 0:
																						__lowerCAmelCase							       =				storage.field('''bytes'''		)
																else:
																						__lowerCAmelCase							       =				pa.array([None] * len(lowerCAmelCase_		)       ,			type=pa.binary()		)
																if storage.type.get_field_index('''path'''		) >= 0:
																						__lowerCAmelCase							       =				storage.field('''path'''		)
																else:
																						__lowerCAmelCase							       =				pa.array([None] * len(lowerCAmelCase_		)       ,			type=pa.string()		)
																__lowerCAmelCase							       =				pa.StructArray.from_arrays([bytes_array, path_array]       ,			['''bytes''', '''path''']       ,			mask=storage.is_null()		)
										return array_cast(lowerCAmelCase_       ,			self.pa_type		)




				def 					_snake_case  (self       ,			__lowercase		):







										@no_op_if_value_is_null
										def path_to_bytes(__lowercase		):
																with xopen(lowerCAmelCase_       ,			'''rb'''		) as f:
																						__lowerCAmelCase							       =				f.read()
																return bytes_

										__lowerCAmelCase							       =				pa.array(
										    [
										        (path_to_bytes(x['''path''']		) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
										        for x in storage.to_pylist()
										    ]       ,			type=pa.binary()       ,			)
										__lowerCAmelCase							       =				pa.array(
										    [os.path.basename(lowerCAmelCase_		) if path is not None else None for path in storage.field('''path'''		).to_pylist()]       ,			type=pa.string()       ,			)
										__lowerCAmelCase							       =				pa.StructArray.from_arrays([bytes_array, path_array]       ,			['''bytes''', '''path''']       ,			mask=bytes_array.is_null()		)
										return array_cast(lowerCAmelCase_       ,			self.pa_type		)





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCAmelCase				: str							    =				{
    """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				: Optional[int]							    =				AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				{}

						__lowerCAmelCase							       =				r'''.*sequential.(\d+).*'''
						__lowerCAmelCase							       =				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:
																								__lowerCAmelCase							       =				key.replace(lowerCamelCase,				lowerCamelCase)

												if re.match(lowerCamelCase,				lowerCamelCase):
																		# replace sequential layers with list
																		__lowerCAmelCase							       =				re.match(lowerCamelCase,				lowerCamelCase).group(1)

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

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

																		__lowerCAmelCase							       =				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
																		__lowerCAmelCase							       =				value
																		__lowerCAmelCase							       =				mixed_qkv.size(0) // 3

																		__lowerCAmelCase							       =				mixed_qkv[:qkv_dim]
																		__lowerCAmelCase							       =				mixed_qkv[qkv_dim : qkv_dim * 2]
																		__lowerCAmelCase							       =				mixed_qkv[qkv_dim * 2 :]

																		__lowerCAmelCase							       =				query_layer
																		__lowerCAmelCase							       =				key_layer
																		__lowerCAmelCase							       =				value_layer
												else:
																		__lowerCAmelCase							       =				value

						return model_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				init_clap(lowerCamelCase,				enable_fusion=lowerCamelCase)

						clap_model.eval()
						__lowerCAmelCase							       =				clap_model.state_dict()
						__lowerCAmelCase							       =				rename_state_dict(lowerCamelCase)

						__lowerCAmelCase							       =				ClapConfig()
						__lowerCAmelCase							       =				enable_fusion
						__lowerCAmelCase							       =				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				: 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				: Optional[int]							    =				parser.parse_args()

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

