import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__a  :     Optional[Any]       =  {
    """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  :     Optional[int]       =  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")





def 				UpperCAmelCase						(    lowercase							,			lowercase=False							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{}

				__lowercase 			=							r'''.*sequential.(\d+).*'''
				__lowercase 			=							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:
																__lowercase 			=							key.replace(lowercase							,			lowercase							)

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

												__lowercase 			=							key.replace(F"sequential.{sequential_layer}."							,			F"layers.{int(lowercase							)//3}.linear."							)
								elif re.match(lowercase							,			lowercase							):
												__lowercase 			=							int(re.match(lowercase							,			lowercase							).group(1							)							)

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

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

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

												__lowercase 			=							query_layer
												__lowercase 			=							key_layer
												__lowercase 			=							value_layer
								else:
												__lowercase 			=							value

				return model_state_dict





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=False							):







				"""simple docstring"""



				__lowercase			,     __lowercase 			=							init_clap(lowercase							,			enable_fusion=lowercase							)

				clap_model.eval()
				__lowercase 			=							clap_model.state_dict()
				__lowercase 			=							rename_state_dict(lowercase							)

				__lowercase 			=							ClapConfig()
				__lowercase 			=							enable_fusion
				__lowercase 			=							ClapModel(lowercase							)

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

				model.save_pretrained(lowercase							)
				transformers_config.save_pretrained(lowercase							)


if __name__ == "__main__":
		__a  :     Optional[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  :     Tuple       =  parser.parse_args()

		convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							max(self.process.memory_info().rss      ,   self.cpu_memory_peak						)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
															break





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							torch.cuda.memory_allocated(lowercase							)
				torch.cuda.reset_peak_memory_stats()

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__lowercase 			=							(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							(torch.cuda.memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20
								__lowercase 			=							(torch.cuda.max_memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				print(F"{description}:"							)
				print(F"- Time: {measures['time']:.2f}s"							)
				for i in range(torch.cuda.device_count()							):
								print(F"- GPU {i} allocated: {measures[str(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							measures[F"{i}-peak"]
								print(F"- GPU {i} peak: {peak:.2f}MiB"							)
				print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
				print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)