

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase  : 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''',
}

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

def  A_	(				a       , a=False	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  create_model(
          'HTSAT-tiny'       , 'roberta'       , __a       , precision='fp32'       , device='cuda:0' if torch.cuda.is_available() else 'cpu'       , enable_fusion=__a       , fusion_type='aff_2d' if enable_fusion else None       , )
      return model, model_cfg

def  A_	(				a	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	Dict     							=  {}

      SCREAMING_SNAKE_CASE_					:	Dict     							=  R'.*sequential.(\d+).*'
      SCREAMING_SNAKE_CASE_					:	List[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_					:	Optional[int]     							=  key.replace(__a       , __a	)

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

                  SCREAMING_SNAKE_CASE_					:	int     							=  key.replace(f"sequential.{sequential_layer}."       , f"layers.{int(__a	)//3}.linear."	)
            elif re.match(__a       , __a	):
                  SCREAMING_SNAKE_CASE_					:	int     							=  int(re.match(__a       , __a	).group(1	)	)

                  # Because in CLAP they use `nn.Sequential`...
                  SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  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_					:	str     							=  value
                  SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  mixed_qkv.size(0	) // 3

                  SCREAMING_SNAKE_CASE_					:	str     							=  mixed_qkv[:qkv_dim]
                  SCREAMING_SNAKE_CASE_					:	List[str]     							=  mixed_qkv[qkv_dim : qkv_dim * 2]
                  SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  mixed_qkv[qkv_dim * 2 :]

                  SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  query_layer
                  SCREAMING_SNAKE_CASE_					:	str     							=  key_layer
                  SCREAMING_SNAKE_CASE_					:	List[str]     							=  value_layer
            else:
                  SCREAMING_SNAKE_CASE_					:	List[str]     							=  value

      return model_state_dict

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

      """simple docstring"""



      SCREAMING_SNAKE_CASE_							,    SCREAMING_SNAKE_CASE_					:	Dict     							=  init_clap(__a       , enable_fusion=__a	)

      clap_model.eval()
      SCREAMING_SNAKE_CASE_					:	List[Any]     							=  clap_model.state_dict()
      SCREAMING_SNAKE_CASE_					:	str     							=  rename_state_dict(__a	)

      SCREAMING_SNAKE_CASE_					:	Dict     							=  ClapConfig()
      SCREAMING_SNAKE_CASE_					:	Dict     							=  enable_fusion
      SCREAMING_SNAKE_CASE_					:	Any     							=  ClapModel(__a	)

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

      model.save_pretrained(__a	)
      transformers_config.save_pretrained(__a	)


if __name__ == "__main__":
    lowerCAmelCase  : 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')
    lowerCAmelCase  : Optional[Any]      =	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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A       :   Dict					= {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
					from .processing_layoutxlm import LayoutXLMProcessor

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm import LayoutXLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
					import sys

					A       :   int					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



