import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


SCREAMING_SNAKE_CASE		:     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",
}

SCREAMING_SNAKE_CASE		:     Tuple         =	AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Any=False			):
						UpperCamelCase_,UpperCamelCase_		:							Any  =		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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any			):
						UpperCamelCase_		:							Dict  =		{}

						UpperCamelCase_		:							Optional[int]  =		R""".*sequential.(\d+).*"""
						UpperCamelCase_		:							Tuple  =		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_		:							Optional[int]  =		key.replace(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

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

																		UpperCamelCase_		:							Tuple  =		key.replace(f'''sequential.{sequential_layer}.'''		, f'''layers.{int(_SCREAMING_SNAKE_CASE			)//3}.linear.'''			)
												elif re.match(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
																		UpperCamelCase_		:							List[str]  =		int(re.match(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			).group(1			)			)

																		# Because in CLAP they use `nn.Sequential`...
																		UpperCamelCase_		:							List[Any]  =		1 if projecton_layer == 0 else 2

																		UpperCamelCase_		:							List[str]  =		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_		:							Dict  =		value
																		UpperCamelCase_		:							Optional[int]  =		mixed_qkv.size(0			) // 3

																		UpperCamelCase_		:							Union[str, Any]  =		mixed_qkv[:qkv_dim]
																		UpperCamelCase_		:							Dict  =		mixed_qkv[qkv_dim : qkv_dim * 2]
																		UpperCamelCase_		:							Any  =		mixed_qkv[qkv_dim * 2 :]

																		UpperCamelCase_		:							List[str]  =		query_layer
																		UpperCamelCase_		:							Any  =		key_layer
																		UpperCamelCase_		:							Any  =		value_layer
												else:
																		UpperCamelCase_		:							List[Any]  =		value

						return model_state_dict






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]=False			):
						UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		init_clap(_SCREAMING_SNAKE_CASE		, enable_fusion=_SCREAMING_SNAKE_CASE			)

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

						UpperCamelCase_		:							Optional[Any]  =		ClapConfig()
						UpperCamelCase_		:							Tuple  =		enable_fusion
						UpperCamelCase_		:							str  =		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__":
	SCREAMING_SNAKE_CASE		:     Optional[int]         =	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		:     List[Any]         =	parser.parse_args()

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







from heapq import heappop, heappush

import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.ndarray		, _SCREAMING_SNAKE_CASE     :				tuple[int, int]		, _SCREAMING_SNAKE_CASE     :				tuple[int, int]		, _SCREAMING_SNAKE_CASE     :				bool		, ):
						UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		grid.shape
						UpperCamelCase_		:							List[str]  =		[-1, 1, 0, 0]
						UpperCamelCase_		:							Dict  =		[0, 0, -1, 1]
						if allow_diagonal:
												dx += [-1, -1, 1, 1]
												dy += [-1, 1, -1, 1]

						UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		[(0, source)], set()
						UpperCamelCase_		:							Any  =		np.full((rows, cols)		, np.inf			)
						UpperCamelCase_		:							List[str]  =		0
						UpperCamelCase_		:							Union[str, Any]  =		np.empty((rows, cols)		, dtype=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[int]  =		None

						while queue:
												((UpperCamelCase_),(UpperCamelCase_))		:							Any  =		heappop(_SCREAMING_SNAKE_CASE			)
												if (x, y) in visited:
																		continue
												visited.add((x, y)			)

												if (x, y) == destination:
																		UpperCamelCase_		:							Tuple  =		[]
																		while (x, y) != source:
																								path.append((x, y)			)
																								UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		predecessors[x, y]
																		path.append(_SCREAMING_SNAKE_CASE			)  # add the source manually
																		path.reverse()
																		return matrix[destination], path

												for i in range(len(_SCREAMING_SNAKE_CASE			)			):
																		UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		x + dx[i], y + dy[i]
																		if 0 <= nx < rows and 0 <= ny < cols:
																								UpperCamelCase_		:							Union[str, Any]  =		grid[nx][ny]
																								if next_node == 1 and matrix[nx, ny] > dist + 1:
																														heappush(_SCREAMING_SNAKE_CASE		, (dist + 1, (nx, ny))			)
																														UpperCamelCase_		:							Optional[Any]  =		dist + 1
																														UpperCamelCase_		:							List[Any]  =		(x, y)

						return np.inf, []


if __name__ == "__main__":
	import doctest

	doctest.testmod()






