
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a__							:	Tuple  						=			{
    '''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__							:	Tuple  						=			AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')







def 							UpperCAmelCase_(  a__      ,		a__=False					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							int								=      create_model(
        '''HTSAT-tiny'''      ,		'''roberta'''      ,		_lowercase      ,		precision='''fp32'''      ,		device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''      ,		enable_fusion=_lowercase      ,		fusion_type='''aff_2d''' if enable_fusion else None      ,		)
    return model, model_cfg







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							str								=      {}

    SCREAMING_SNAKE_CASE							:							Optional[int]								=      r""".*sequential.(\d+).*"""
    SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      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:
                SCREAMING_SNAKE_CASE							:							Tuple								=      key.replace(_lowercase      ,		_lowercase					)

        if re.match(_lowercase      ,		_lowercase					):
            # replace sequential layers with list
            SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      re.match(_lowercase      ,		_lowercase					).group(1					)

            SCREAMING_SNAKE_CASE							:							Optional[int]								=      key.replace(F"""sequential.{sequential_layer}."""      ,		F"""layers.{int(_lowercase					)//3}.linear."""					)
        elif re.match(_lowercase      ,		_lowercase					):
            SCREAMING_SNAKE_CASE							:							List[Any]								=      int(re.match(_lowercase      ,		_lowercase					).group(1					)					)

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

            SCREAMING_SNAKE_CASE							:							Optional[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
            SCREAMING_SNAKE_CASE							:							Dict								=      value
            SCREAMING_SNAKE_CASE							:							str								=      mixed_qkv.size(0					) // 3

            SCREAMING_SNAKE_CASE							:							List[Any]								=      mixed_qkv[:qkv_dim]
            SCREAMING_SNAKE_CASE							:							List[Any]								=      mixed_qkv[qkv_dim : qkv_dim * 2]
            SCREAMING_SNAKE_CASE							:							Dict								=      mixed_qkv[qkv_dim * 2 :]

            SCREAMING_SNAKE_CASE							:							Tuple								=      query_layer
            SCREAMING_SNAKE_CASE							:							Optional[Any]								=      key_layer
            SCREAMING_SNAKE_CASE							:							Any								=      value_layer
        else:
            SCREAMING_SNAKE_CASE							:							Any								=      value

    return model_state_dict







def 							UpperCAmelCase_(  a__      ,		a__      ,		a__      ,		a__=False					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      init_clap(_lowercase      ,		enable_fusion=_lowercase					)

    clap_model.eval()
    SCREAMING_SNAKE_CASE							:							Any								=      clap_model.state_dict()
    SCREAMING_SNAKE_CASE							:							Dict								=      rename_state_dict(_lowercase					)

    SCREAMING_SNAKE_CASE							:							Optional[int]								=      ClapConfig()
    SCREAMING_SNAKE_CASE							:							Optional[int]								=      enable_fusion
    SCREAMING_SNAKE_CASE							:							Dict								=      ClapModel(_lowercase					)

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

    model.save_pretrained(_lowercase					)
    transformers_config.save_pretrained(_lowercase					)


if __name__ == "__main__":
      a__							:	Optional[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__							:	List[Any]  						=			parser.parse_args()

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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a      =				{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "AltCLIPPreTrainedModel",
						    "AltCLIPModel",
						    "AltCLIPTextModel",
						    "AltCLIPVisionModel",
						]


if TYPE_CHECKING:
						from .configuration_altclip import (
						    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    AltCLIPConfig,
						    AltCLIPTextConfig,
						    AltCLIPVisionConfig,
						)
						from .processing_altclip import AltCLIPProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_altclip import (
												    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AltCLIPModel,
												    AltCLIPPreTrainedModel,
												    AltCLIPTextModel,
												    AltCLIPVisionModel,
												)


else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
