






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A__			:    int         =				{
    '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',
}

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






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=False       ):






				'''simple docstring'''
				lowercase__				,		lowercase__     				=    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 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    {}

				lowercase__     				=    r'''.*sequential.(\d+).*'''
				lowercase__     				=    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:
																lowercase__     				=    key.replace(_lowerCAmelCase	,     _lowerCAmelCase       )

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

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

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

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

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

												lowercase__     				=    query_layer
												lowercase__     				=    key_layer
												lowercase__     				=    value_layer
								else:
												lowercase__     				=    value

				return model_state_dict






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_=False       ):






				'''simple docstring'''
				lowercase__				,		lowercase__     				=    init_clap(_lowerCAmelCase	,     enable_fusion=_lowerCAmelCase       )

				clap_model.eval()
				lowercase__     				=    clap_model.state_dict()
				lowercase__     				=    rename_state_dict(_lowerCAmelCase       )

				lowercase__     				=    ClapConfig()
				lowercase__     				=    enable_fusion
				lowercase__     				=    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__":
					A__			:    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')
					A__			:    Dict         =				parser.parse_args()

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A__			:    Any         =				{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Union[str, Any]         =				[
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A__			:    List[Any]         =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




