



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case	=						{
    "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",
}

_snake_case	=						AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





       '''simple docstring'''




       _lowerCAmelCase  :							Optional[Any]				     =					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'''




       _lowerCAmelCase  :							Dict				     =					{}

       _lowerCAmelCase  :							List[Any]				     =					r".*sequential.(\d+).*"
       _lowerCAmelCase  :							Optional[int]				     =					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  :							Union[str, Any]				     =					key.replace(_lowerCamelCase       ,				_lowerCamelCase						)

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

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

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

                     _lowerCAmelCase  :							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
                     _lowerCAmelCase  :							int				     =					value
                     _lowerCAmelCase  :							str				     =					mixed_qkv.size(0						) // 3

                     _lowerCAmelCase  :							Optional[int]				     =					mixed_qkv[:qkv_dim]
                     _lowerCAmelCase  :							Tuple				     =					mixed_qkv[qkv_dim : qkv_dim * 2]
                     _lowerCAmelCase  :							Dict				     =					mixed_qkv[qkv_dim * 2 :]

                     _lowerCAmelCase  :							Tuple				     =					query_layer
                     _lowerCAmelCase  :							Optional[int]				     =					key_layer
                     _lowerCAmelCase  :							Optional[Any]				     =					value_layer
              else:
                     _lowerCAmelCase  :							str				     =					value

       return model_state_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=False						):





       '''simple docstring'''




       _lowerCAmelCase  :							Any				     =					init_clap(_lowerCamelCase       ,				enable_fusion=_lowerCamelCase						)

       clap_model.eval()
       _lowerCAmelCase  :							Tuple				     =					clap_model.state_dict()
       _lowerCAmelCase  :							List[Any]				     =					rename_state_dict(_lowerCamelCase						)

       _lowerCAmelCase  :							List[str]				     =					ClapConfig()
       _lowerCAmelCase  :							Tuple				     =					enable_fusion
       _lowerCAmelCase  :							str				     =					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__":
      _snake_case	=						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")
      _snake_case	=						parser.parse_args()

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





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'xlnet'
							lowerCamelCase__         =	['mems']
							lowerCamelCase__         =	{
							    'n_token': 'vocab_size',  # Backward compatibility
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(     self,    __a=3_2000,    __a=1024,    __a=24,    __a=16,    __a=4096,    __a="gelu",    __a=True,    __a="bi",    __a=0.02,    __a=1E-12,    __a=0.1,    __a=512,    __a=None,    __a=True,    __a=False,    __a=False,    __a=-1,    __a=False,    __a="last",    __a=True,    __a="tanh",    __a=0.1,    __a=5,    __a=5,    __a=5,    __a=1,    __a=2,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					d_model
														_lowerCAmelCase  :							Tuple				     =					n_layer
														_lowerCAmelCase  :							List[Any]				     =					n_head
														if d_model % n_head != 0:
																					raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0")
														if "d_head" in kwargs:
																					if kwargs["d_head"] != d_model // n_head:
																												raise ValueError(
																												    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})")
														_lowerCAmelCase  :							Optional[int]				     =					d_model // n_head
														_lowerCAmelCase  :							List[str]				     =					ff_activation
														_lowerCAmelCase  :							Tuple				     =					d_inner
														_lowerCAmelCase  :							List[Any]				     =					untie_r
														_lowerCAmelCase  :							List[str]				     =					attn_type

														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Any				     =					layer_norm_eps

														_lowerCAmelCase  :							List[Any]				     =					dropout
														_lowerCAmelCase  :							Optional[int]				     =					mem_len
														_lowerCAmelCase  :							Union[str, Any]				     =					reuse_len
														_lowerCAmelCase  :							List[str]				     =					bi_data
														_lowerCAmelCase  :							List[str]				     =					clamp_len
														_lowerCAmelCase  :							Any				     =					same_length

														_lowerCAmelCase  :							List[str]				     =					summary_type
														_lowerCAmelCase  :							int				     =					summary_use_proj
														_lowerCAmelCase  :							Optional[Any]				     =					summary_activation
														_lowerCAmelCase  :							Tuple				     =					summary_last_dropout
														_lowerCAmelCase  :							Union[str, Any]				     =					start_n_top
														_lowerCAmelCase  :							Optional[int]				     =					end_n_top

														_lowerCAmelCase  :							Tuple				     =					bos_token_id
														_lowerCAmelCase  :							List[Any]				     =					pad_token_id
														_lowerCAmelCase  :							Dict				     =					eos_token_id

														if "use_cache" in kwargs:
																					warnings.warn(
																					    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
																					    " instead.",    __a,    )
																					_lowerCAmelCase  :							Union[str, Any]				     =					kwargs["use_cache"]

														_lowerCAmelCase  :							Union[str, Any]				     =					use_mems_eval
														_lowerCAmelCase  :							Any				     =					use_mems_train
														super().__init__(pad_token_id=__a,    bos_token_id=__a,    eos_token_id=__a,    **__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
														return -1







							@max_position_embeddings.setter
							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														raise NotImplementedError(
														    f"The model {self.model_type} is one of the few models that has no sequence length limit.")
