




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCamelCase__    			=			{
    """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__    			=			AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str=False     ):
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       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 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple     ):
					__lowerCAmelCase				   =       {}

					__lowerCAmelCase				   =       R".*sequential.(\d+).*"
					__lowerCAmelCase				   =       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:
																				__lowerCAmelCase				   =       key.replace(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

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

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

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

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

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

															__lowerCAmelCase				   =       query_layer
															__lowerCAmelCase				   =       key_layer
															__lowerCAmelCase				   =       value_layer
										else:
															__lowerCAmelCase				   =       value

					return model_state_dict







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				Any=False     ):
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       init_clap(SCREAMING_SNAKE_CASE_      ,      enable_fusion=SCREAMING_SNAKE_CASE_     )

					clap_model.eval()
					__lowerCAmelCase				   =       clap_model.state_dict()
					__lowerCAmelCase				   =       rename_state_dict(SCREAMING_SNAKE_CASE_     )

					__lowerCAmelCase				   =       ClapConfig()
					__lowerCAmelCase				   =       enable_fusion
					__lowerCAmelCase				   =       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__":
					UpperCamelCase__    			=			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__    			=			parser.parse_args()

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










import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad      ,      model.parameters()     )
					__lowerCAmelCase				   =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					if metric == "rouge2":
										__lowerCAmelCase				   =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__lowerCAmelCase				   =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__lowerCAmelCase				   =       "{val_avg_em:.4f}-{step_count}"
					else:
										raise NotImplementedError(
										    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
										    " function."     )

					__lowerCAmelCase				   =       ModelCheckpoint(
					    dirpath=SCREAMING_SNAKE_CASE_      ,      filename=SCREAMING_SNAKE_CASE_      ,      monitor=F"""val_{metric}"""      ,      mode="max"      ,      save_top_k=3      ,      every_n_epochs=1      ,      )
					return checkpoint_callback







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					return EarlyStopping(
					    monitor=F"""val_{metric}"""      ,      mode="min" if "loss" in metric else "max"      ,      patience=SCREAMING_SNAKE_CASE_      ,      verbose=SCREAMING_SNAKE_CASE_      ,      )


class 			a__    (					pl.Callback      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
											pl_module.logger.log_metrics(_A			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
											__lowerCAmelCase				   =       trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}			)
											# Log results
											__lowerCAmelCase				   =       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																__lowerCAmelCase				   =       od / "test_results.txt"
																__lowerCAmelCase				   =       od / "test_generations.txt"
											else:
																# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
																# If people want this it will be easy enough to add back.
																__lowerCAmelCase				   =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__lowerCAmelCase				   =       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																results_file.parent.mkdir(exist_ok=_A			)
																generations_file.parent.mkdir(exist_ok=_A			)
											with open(_A		,			"a+"			) as writer:
																for key in sorted(_A			):
																					if key in ["log", "progress_bar", "preds"]:
																										continue
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

											if "preds" in metrics:
																__lowerCAmelCase				   =       "\n".join(metrics["preds"]			)
																generations_file.open("w+"			).write(_A			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																__lowerCAmelCase				   =       pl_module.model.model.num_parameters()
											except AttributeError:
																__lowerCAmelCase				   =       pl_module.model.num_parameters()

											__lowerCAmelCase				   =       count_trainable_parameters(_A			)
											# mp stands for million parameters
											trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6}			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											return self._write_logs(_A		,			_A		,			"test"			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")




