




'''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'''





import logging
import os

from .state import PartialState




class 			_snake_case		(     logging.LoggerAdapter							):




			@staticmethod
			def        lowerCAmelCase__    ( a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				PartialState()
										return not main_process_only or (main_process_only and state.is_main_process)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							*a__    ,							**a__							)    ->    List[Any]:

										'''simple docstring'''




										if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."							)
										snake_case_      =				kwargs.pop("main_process_only"    ,							a__							)
										snake_case_      =				kwargs.pop("in_order"    ,							a__							)

										if self.isEnabledFor(a__							):
																	if self._should_log(a__							):
																								snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																								self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)

																	elif in_order:
																								snake_case_      =				PartialState()
																								for i in range(state.num_processes							):
																															if i == state.process_index:
																																						snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																																						self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)
																															state.wait_for_everyone()


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str = None					):

							'''simple docstring'''






							if log_level is None:
														snake_case_      =				os.environ.get("ACCELERATE_LOG_LEVEL"							,    snake_case					)
							snake_case_      =				logging.getLogger(snake_case					)
							if log_level is not None:
														logger.setLevel(log_level.upper()					)
														logger.root.setLevel(log_level.upper()					)
							return MultiProcessAdapter(snake_case							,    {}					)




