




'''simple docstring'''





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_SCREAMING_SNAKE_CASE							:       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",
}

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


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	Tuple=False					):

       '''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	int					):

       '''simple docstring'''






       snake_case_      =				{}

       snake_case_      =				r".*sequential.(\d+).*"
       snake_case_      =				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:
                            snake_case_      =				key.replace(snake_case							,    snake_case					)

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

                     snake_case_      =				key.replace(f'sequential.{sequential_layer}.'							,    f'layers.{int(snake_case					)//3}.linear.'					)
              elif re.match(snake_case							,    snake_case					):
                     snake_case_      =				int(re.match(snake_case							,    snake_case					).group(1					)					)

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

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

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

                     snake_case_      =				query_layer
                     snake_case_      =				key_layer
                     snake_case_      =				value_layer
              else:
                     snake_case_      =				value

       return model_state_dict


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	List[str]							,    snake_case							:	Tuple							,    snake_case							:	Any=False					):

       '''simple docstring'''






       snake_case_ ,				snake_case_      =				init_clap(snake_case							,    enable_fusion=snake_case					)

       clap_model.eval()
       snake_case_      =				clap_model.state_dict()
       snake_case_      =				rename_state_dict(snake_case					)

       snake_case_      =				ClapConfig()
       snake_case_      =				enable_fusion
       snake_case_      =				ClapModel(snake_case					)

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

       model.save_pretrained(snake_case					)
       transformers_config.save_pretrained(snake_case					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE							:       str						=			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")
  _SCREAMING_SNAKE_CASE							:       int						=			parser.parse_args()

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










'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




