

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					:  Any             =      AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def     UpperCAmelCase_       (						_A				,		_A=False    ):
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						_A    ):
     SCREAMING_SNAKE_CASE__   =   {}

     SCREAMING_SNAKE_CASE__   =   R'''.*sequential.(\d+).*'''
     SCREAMING_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:
                    SCREAMING_SNAKE_CASE__   =   key.replace(_A				,		_A    )

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

               SCREAMING_SNAKE_CASE__   =   key.replace(F'''sequential.{sequential_layer}.'''				,		F'''layers.{int(_A    )//3}.linear.'''    )
          elif re.match(_A				,		_A    ):
               SCREAMING_SNAKE_CASE__   =   int(re.match(_A				,		_A    ).group(1    )    )

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

               SCREAMING_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
               SCREAMING_SNAKE_CASE__   =   value
               SCREAMING_SNAKE_CASE__   =   mixed_qkv.size(0    ) // 3

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

               SCREAMING_SNAKE_CASE__   =   query_layer
               SCREAMING_SNAKE_CASE__   =   key_layer
               SCREAMING_SNAKE_CASE__   =   value_layer
          else:
               SCREAMING_SNAKE_CASE__   =   value

     return model_state_dict
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A=False    ):
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   init_clap(_A				,		enable_fusion=_A    )

     clap_model.eval()
     SCREAMING_SNAKE_CASE__   =   clap_model.state_dict()
     SCREAMING_SNAKE_CASE__   =   rename_state_dict(_A    )

     SCREAMING_SNAKE_CASE__   =   ClapConfig()
     SCREAMING_SNAKE_CASE__   =   enable_fusion
     SCREAMING_SNAKE_CASE__   =   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__":
       _SCREAMING_SNAKE_CASE					:  List[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					:  List[Any]             =      parser.parse_args()

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





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_SCREAMING_SNAKE_CASE					:  Tuple             =      data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE					:  Dict             =      data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      data_utils
_SCREAMING_SNAKE_CASE					:  Any             =      data_utils
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(_A				,		'''rb'''    ) as fp:
               SCREAMING_SNAKE_CASE__   =   pickle.load(_A				,		encoding='''latin1'''    )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''    )
          SCREAMING_SNAKE_CASE__   =   corpus.vocab.__dict__
          torch.save(_A				,		_A    )

          SCREAMING_SNAKE_CASE__   =   corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''				,		_A    )
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F'''Save dataset to {pytorch_dataset_dump_path}'''    )
          torch.save(_A				,		_A    )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )

          print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig()
          else:
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig.from_json_file(_A    )
          print(F'''Building PyTorch model from configuration: {config}'''    )
          SCREAMING_SNAKE_CASE__   =   TransfoXLLMHeadModel(_A    )

          SCREAMING_SNAKE_CASE__   =   load_tf_weights_in_transfo_xl(_A				,		_A				,		_A    )
          # Save pytorch-model
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          print(F'''Save PyTorch model to {os.path.abspath(_A    )}'''    )
          torch.save(model.state_dict()				,		_A    )
          print(F'''Save configuration file to {os.path.abspath(_A    )}'''    )
          with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as f:
               f.write(config.to_json_string()    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Any             =      argparse.ArgumentParser()
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--tf_checkpoint_path''',
           default='''''',
           type=str,
           help='''An optional path to a TensorFlow checkpoint path to be converted.''',
       )
       parser.add_argument(
           '''--transfo_xl_config_file''',
           default='''''',
           type=str,
           help=(
               '''An optional config json file corresponding to the pre-trained BERT model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--transfo_xl_dataset_file''',
           default='''''',
           type=str,
           help='''An optional dataset file to be converted in a vocabulary.''',
       )
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      parser.parse_args()
       convert_transfo_xl_checkpoint_to_pytorch(
           args.tf_checkpoint_path,
           args.transfo_xl_config_file,
           args.pytorch_dump_folder_path,
           args.transfo_xl_dataset_file,
       )


