



"""simple docstring"""


import math
import os
import sys







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    ''''''
				try:
								with open(_SCREAMING_SNAKE_CASE					,     '''rb'''				) as binary_file:
												_UpperCAmelCase					=    binary_file.read()
								for dat in data:
												_UpperCAmelCase					=    f'{dat:08b}'
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''				)
								sys.exit()







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict[str, str]					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				lexicon.pop(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    last_match_id

				if math.loga(_SCREAMING_SNAKE_CASE				).is_integer():
								for curr_key in lexicon:
												_UpperCAmelCase					=    '''0''' + lexicon[curr_key]

				_UpperCAmelCase					=    bin(_SCREAMING_SNAKE_CASE				)[2:]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {'''0''': '''0''', '''1''': '''1'''}
				_UpperCAmelCase		,						_UpperCAmelCase					=    '''''', ''''''
				_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE				)

				for i in range(len(_SCREAMING_SNAKE_CASE				)				):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								_UpperCAmelCase					=    lexicon[curr_string]
								result += last_match_id
								add_key_to_lexicon(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								index += 1
								_UpperCAmelCase					=    ''''''

				while curr_string != "" and curr_string not in lexicon:
								curr_string += "0"

				if curr_string != "":
								_UpperCAmelCase					=    lexicon[curr_string]
								result += last_match_id

				return result







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    os.path.getsize(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    bin(_SCREAMING_SNAKE_CASE				)[2:]
				_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE				)

				return "0" * (length_length - 1) + file_length_binary + compressed







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    8
				try:
								with open(_SCREAMING_SNAKE_CASE					,     '''wb'''				) as opened_file:
												_UpperCAmelCase					=    [
												    to_write[i : i + byte_length]
												    for i in range(0					,     len(_SCREAMING_SNAKE_CASE				)					,     _SCREAMING_SNAKE_CASE				)
												]

												if len(result_byte_array[-1]				) % byte_length == 0:
																result_byte_array.append('''10000000'''				)
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]				) - 1
																)

												for elem in result_byte_array:
																opened_file.write(int(_SCREAMING_SNAKE_CASE					,     2				).to_bytes(1					,     byteorder='''big'''				)				)
				except OSError:
								print('''File not accessible'''				)
								sys.exit()







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    read_file_binary(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    compress_data(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    add_file_length(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				write_file_binary(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])








"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A   :							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",
}

__A   :							List[str]										=   AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    r'''.*sequential.(\d+).*'''
				_UpperCAmelCase					=    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:
																_UpperCAmelCase					=    key.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

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

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

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

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

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

												_UpperCAmelCase					=    query_layer
												_UpperCAmelCase					=    key_layer
												_UpperCAmelCase					=    value_layer
								else:
												_UpperCAmelCase					=    value

				return model_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    init_clap(_SCREAMING_SNAKE_CASE					,     enable_fusion=_SCREAMING_SNAKE_CASE				)

				clap_model.eval()
				_UpperCAmelCase					=    clap_model.state_dict()
				_UpperCAmelCase					=    rename_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    ClapConfig()
				_UpperCAmelCase					=    enable_fusion
				_UpperCAmelCase					=    ClapModel(_SCREAMING_SNAKE_CASE				)

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

				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				transformers_config.save_pretrained(_SCREAMING_SNAKE_CASE				)


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

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



