
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase__		:     List[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',
}

UpperCAmelCase__		:     Optional[Any]     			=      AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')






def   lowerCamelCase__							(					a		,		a=False      )      ->   Optional[Any]:
   _A			,  _A:  Dict					=       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   lowerCamelCase__							(					a      )      ->   List[str]:
   _A:  str					=       {}

   _A:  List[str]					=       R'''.*sequential.(\d+).*'''
   _A:  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:
            _A:  Any					=       key.replace(__lowerCAmelCase		,		__lowerCAmelCase      )

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

         _A:  List[str]					=       key.replace(f"""sequential.{sequential_layer}."""		,		f"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
      elif re.match(__lowerCAmelCase		,		__lowerCAmelCase      ):
         _A:  int					=       int(re.match(__lowerCAmelCase		,		__lowerCAmelCase      ).group(1      )      )

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

         _A:  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
         _A:  Union[str, Any]					=       value
         _A:  Dict					=       mixed_qkv.size(0      ) // 3

         _A:  Optional[Any]					=       mixed_qkv[:qkv_dim]
         _A:  Tuple					=       mixed_qkv[qkv_dim : qkv_dim * 2]
         _A:  str					=       mixed_qkv[qkv_dim * 2 :]

         _A:  List[Any]					=       query_layer
         _A:  List[str]					=       key_layer
         _A:  Tuple					=       value_layer
      else:
         _A:  Optional[Any]					=       value

   return model_state_dict






def   lowerCamelCase__							(					a		,		a		,		a		,		a=False      )      ->   int:
   _A			,  _A:  Optional[Any]					=       init_clap(__lowerCAmelCase		,		enable_fusion=__lowerCAmelCase      )

   clap_model.eval()
   _A:  Union[str, Any]					=       clap_model.state_dict()
   _A:  Dict					=       rename_state_dict(__lowerCAmelCase      )

   _A:  List[Any]					=       ClapConfig()
   _A:  Dict					=       enable_fusion
   _A:  List[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__":
   UpperCAmelCase__		:     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')
   UpperCAmelCase__		:     Tuple     			=      parser.parse_args()

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











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      TransfoXLTokenizer
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase									=			[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']    )
							with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'    ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<unk> UNwanted , running'
							_UpperCAmelCase									=			'<unk> unwanted, running'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(vocab_file=self.vocab_file       ,    lower_case=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('<unk> UNwanted , running'    )
							self.assertListEqual(UpperCAmelCase       ,    ['<unk>', 'unwanted', ',', 'running']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [0, 4, 8, 7]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['hello', '!', 'how', 'are', 'you', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )
							_UpperCAmelCase									=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							_UpperCAmelCase									=			[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(UpperCAmelCase    )       ,    UpperCAmelCase    )

							self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			len(UpperCAmelCase    )

							tokenizer.add_tokens(['new1', 'new2']    )
							tokenizer.move_added_token('new1'       ,    1    )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(UpperCAmelCase    )       ,    original_len + 2    )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'    )       ,    [1]    )
							self.assertEqual(tokenizer.decode([1]    )       ,    'new1'    )
