





"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Optional[int]=1			)    ->			Optional[int]:



			'''simple docstring'''
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split("."			)[n_shave_prefix_segments:]			)
			else:
						return ".".join(path.split("."			)[:n_shave_prefix_segments]			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			Any=0			)    ->			Any:



			'''simple docstring'''
			a__  :	str		 =			[]
			for old_item in old_list:
						a__  :	str		 =			old_item.replace("in_layers.0"							,			"norm1"			)
						a__  :	Tuple		 =			new_item.replace("in_layers.2"							,			"conv1"			)

						a__  :	Dict		 =			new_item.replace("out_layers.0"							,			"norm2"			)
						a__  :	Union[str, Any]		 =			new_item.replace("out_layers.3"							,			"conv2"			)

						a__  :	str		 =			new_item.replace("emb_layers.1"							,			"time_emb_proj"			)
						a__  :	str		 =			new_item.replace("skip_connection"							,			"conv_shortcut"			)

						a__  :	List[Any]		 =			shave_segments(lowerCAmelCase__							,			n_shave_prefix_segments=lowerCAmelCase__			)

						mapping.append({"old": old_item, "new": new_item}			)

			return mapping






def      lowercase__ (					lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]=0			)    ->			Optional[Any]:



			'''simple docstring'''
			a__  :	str		 =			[]
			for old_item in old_list:
						a__  :	Union[str, Any]		 =			old_item

						a__  :	List[Any]		 =			new_item.replace("norm.weight"							,			"group_norm.weight"			)
						a__  :	Union[str, Any]		 =			new_item.replace("norm.bias"							,			"group_norm.bias"			)

						a__  :	List[Any]		 =			new_item.replace("proj_out.weight"							,			"proj_attn.weight"			)
						a__  :	str		 =			new_item.replace("proj_out.bias"							,			"proj_attn.bias"			)

						a__  :	Optional[int]		 =			shave_segments(lowerCAmelCase__							,			n_shave_prefix_segments=lowerCAmelCase__			)

						mapping.append({"old": old_item, "new": new_item}			)

			return mapping






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Dict=None							,			lowerCAmelCase__   :			str=None							,			lowerCAmelCase__   :			Tuple=None			)    ->			Any:



			'''simple docstring'''
			assert isinstance(lowerCAmelCase__							,			lowerCAmelCase__			), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									a__  :	List[str]		 =			old_checkpoint[path]
									a__  :	Tuple		 =			old_tensor.shape[0] // 3

									a__  :	Union[str, Any]		 =			(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

									a__  :	Optional[Any]		 =			old_tensor.shape[0] // config["num_head_channels"] // 3

									a__  :	List[str]		 =			old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
									a__ ,	a__ ,	a__  :	List[Any]		 =			old_tensor.split(channels // num_heads							,			dim=1			)

									a__  :	Tuple		 =			query.reshape(lowerCAmelCase__			)
									a__  :	Optional[Any]		 =			key.reshape(lowerCAmelCase__			)
									a__  :	Any		 =			value.reshape(lowerCAmelCase__			)

			for path in paths:
						a__  :	Dict		 =			path["new"]

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						a__  :	Optional[int]		 =			new_path.replace("middle_block.0"							,			"mid_block.resnets.0"			)
						a__  :	int		 =			new_path.replace("middle_block.1"							,			"mid_block.attentions.0"			)
						a__  :	Optional[int]		 =			new_path.replace("middle_block.2"							,			"mid_block.resnets.1"			)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												a__  :	Optional[Any]		 =			new_path.replace(replacement["old"]							,			replacement["new"]			)

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									a__  :	Tuple		 =			old_checkpoint[path["old"]][:, :, 0]
						else:
									a__  :	List[Any]		 =			old_checkpoint[path["old"]]






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			a__  :	Optional[int]		 =			{}

			a__  :	List[Any]		 =			checkpoint["time_embed.0.weight"]
			a__  :	Any		 =			checkpoint["time_embed.0.bias"]
			a__  :	Optional[Any]		 =			checkpoint["time_embed.2.weight"]
			a__  :	str		 =			checkpoint["time_embed.2.bias"]

			a__  :	Any		 =			checkpoint["input_blocks.0.0.weight"]
			a__  :	Optional[Any]		 =			checkpoint["input_blocks.0.0.bias"]

			a__  :	List[str]		 =			checkpoint["out.0.weight"]
			a__  :	Union[str, Any]		 =			checkpoint["out.0.bias"]
			a__  :	List[Any]		 =			checkpoint["out.2.weight"]
			a__  :	Tuple		 =			checkpoint["out.2.bias"]

			# Retrieves the keys for the input blocks only
			a__  :	Any		 =			len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "input_blocks" in layer}			)
			a__  :	Union[str, Any]		 =			{
			    layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
			    for layer_id in range(lowerCAmelCase__			)
			}

			# Retrieves the keys for the middle blocks only
			a__  :	int		 =			len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "middle_block" in layer}			)
			a__  :	Optional[int]		 =			{
			    layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
			    for layer_id in range(lowerCAmelCase__			)
			}

			# Retrieves the keys for the output blocks only
			a__  :	Optional[Any]		 =			len({".".join(layer.split("."			)[:2]			) for layer in checkpoint if "output_blocks" in layer}			)
			a__  :	Optional[int]		 =			{
			    layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
			    for layer_id in range(lowerCAmelCase__			)
			}

			for i in range(1							,			lowerCAmelCase__			):
						a__  :	List[str]		 =			(i - 1) // (config["num_res_blocks"] + 1)
						a__  :	Dict		 =			(i - 1) % (config["num_res_blocks"] + 1)

						a__  :	int		 =			[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
						a__  :	Dict		 =			[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

						if F"input_blocks.{i}.0.op.weight" in checkpoint:
									a__  :	Tuple		 =			checkpoint[
									    F"input_blocks.{i}.0.op.weight"
									]
									a__  :	Tuple		 =			checkpoint[
									    F"input_blocks.{i}.0.op.bias"
									]
									continue

						a__  :	str		 =			renew_resnet_paths(lowerCAmelCase__			)
						a__  :	Tuple		 =			{"old": F"input_blocks.{i}.0", "new": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
						a__  :	Optional[int]		 =			{"old": "resnets.2.op", "new": "downsamplers.0.op"}
						assign_to_checkpoint(
						    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			additional_replacements=[meta_path, resnet_op]							,			config=lowerCAmelCase__			)

						if len(lowerCAmelCase__			):
									a__  :	List[Any]		 =			renew_attention_paths(lowerCAmelCase__			)
									a__  :	int		 =			{
									    "old": F"input_blocks.{i}.1",
									    "new": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
									}
									a__  :	List[Any]		 =			{
									    F"input_blocks.{i}.1.qkv.bias": {
									        "key": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
									        "query": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
									        "value": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
									    },
									    F"input_blocks.{i}.1.qkv.weight": {
									        "key": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
									        "query": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
									        "value": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
									    },
									}
									assign_to_checkpoint(
									    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			additional_replacements=[meta_path]							,			attention_paths_to_split=lowerCAmelCase__							,			config=lowerCAmelCase__							,			)

			a__  :	Tuple		 =			middle_blocks[0]
			a__  :	Union[str, Any]		 =			middle_blocks[1]
			a__  :	int		 =			middle_blocks[2]

			a__  :	Any		 =			renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			config=lowerCAmelCase__			)

			a__  :	Tuple		 =			renew_resnet_paths(lowerCAmelCase__			)
			assign_to_checkpoint(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			config=lowerCAmelCase__			)

			a__  :	Optional[int]		 =			renew_attention_paths(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			{
			    "middle_block.1.qkv.bias": {
			        "key": "mid_block.attentions.0.key.bias",
			        "query": "mid_block.attentions.0.query.bias",
			        "value": "mid_block.attentions.0.value.bias",
			    },
			    "middle_block.1.qkv.weight": {
			        "key": "mid_block.attentions.0.key.weight",
			        "query": "mid_block.attentions.0.query.weight",
			        "value": "mid_block.attentions.0.value.weight",
			    },
			}
			assign_to_checkpoint(
			    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			attention_paths_to_split=lowerCAmelCase__							,			config=lowerCAmelCase__			)

			for i in range(lowerCAmelCase__			):
						a__  :	Optional[int]		 =			i // (config["num_res_blocks"] + 1)
						a__  :	int		 =			i % (config["num_res_blocks"] + 1)
						a__  :	str		 =			[shave_segments(lowerCAmelCase__							,			2			) for name in output_blocks[i]]
						a__  :	List[str]		 =			{}

						for layer in output_block_layers:
									a__ ,	a__  :	List[str]		 =			layer.split("."			)[0], shave_segments(lowerCAmelCase__							,			1			)
									if layer_id in output_block_list:
												output_block_list[layer_id].append(lowerCAmelCase__			)
									else:
												a__  :	Union[str, Any]		 =			[layer_name]

						if len(lowerCAmelCase__			) > 1:
									a__  :	Dict		 =			[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
									a__  :	str		 =			[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

									a__  :	Any		 =			renew_resnet_paths(lowerCAmelCase__			)
									a__  :	List[Any]		 =			renew_resnet_paths(lowerCAmelCase__			)

									a__  :	Optional[int]		 =			{"old": F"output_blocks.{i}.0", "new": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
									assign_to_checkpoint(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			additional_replacements=[meta_path]							,			config=lowerCAmelCase__			)

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												a__  :	str		 =			list(output_block_list.values()			).index(["conv.weight", "conv.bias"]			)
												a__  :	Union[str, Any]		 =			checkpoint[
												    F"output_blocks.{i}.{index}.conv.weight"
												]
												a__  :	List[Any]		 =			checkpoint[
												    F"output_blocks.{i}.{index}.conv.bias"
												]

												# Clear attentions as they have been attributed above.
												if len(lowerCAmelCase__			) == 2:
															a__  :	Optional[Any]		 =			[]

									if len(lowerCAmelCase__			):
												a__  :	Dict		 =			renew_attention_paths(lowerCAmelCase__			)
												a__  :	Union[str, Any]		 =			{
												    "old": F"output_blocks.{i}.1",
												    "new": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
												}
												a__  :	List[str]		 =			{
												    F"output_blocks.{i}.1.qkv.bias": {
												        "key": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
												        "query": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
												        "value": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
												    },
												    F"output_blocks.{i}.1.qkv.weight": {
												        "key": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
												        "query": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
												        "value": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
												    },
												}
												assign_to_checkpoint(
												    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			additional_replacements=[meta_path]							,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions			) else None							,			config=lowerCAmelCase__							,			)
						else:
									a__  :	str		 =			renew_resnet_paths(lowerCAmelCase__							,			n_shave_prefix_segments=1			)
									for path in resnet_0_paths:
												a__  :	Optional[int]		 =			".".join(["output_blocks", str(lowerCAmelCase__			), path["old"]]			)
												a__  :	Optional[Any]		 =			".".join(["up_blocks", str(lowerCAmelCase__			), "resnets", str(lowerCAmelCase__			), path["new"]]			)

												a__  :	Any		 =			checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		__UpperCAmelCase						=			parser.parse_args()

		__UpperCAmelCase						=			torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				__UpperCAmelCase						=			json.loads(f.read())

		__UpperCAmelCase						=			convert_ldm_checkpoint(checkpoint, config)

		if "ldm" in config:
				del config["ldm"]

		__UpperCAmelCase						=			UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				__UpperCAmelCase						=			DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
				__UpperCAmelCase						=			VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__UpperCAmelCase						=			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)






"""simple docstring"""






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      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]=False			)    ->			List[str]:



			'''simple docstring'''
			a__ ,	a__  :	Optional[int]		 =			create_model(
			    "HTSAT-tiny"							,			"roberta"							,			lowerCAmelCase__							,			precision="fp32"							,			device="cuda:0" if torch.cuda.is_available() else "cpu"							,			enable_fusion=lowerCAmelCase__							,			fusion_type="aff_2d" if enable_fusion else None							,			)
			return model, model_cfg






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]			)    ->			Dict:



			'''simple docstring'''
			a__  :	Any		 =			{}

			a__  :	Tuple		 =			R".*sequential.(\d+).*"
			a__  :	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:
												a__  :	str		 =			key.replace(lowerCAmelCase__							,			lowerCAmelCase__			)

						if re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									# replace sequential layers with list
									a__  :	Optional[Any]		 =			re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)

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

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

									a__  :	Dict		 =			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
									a__  :	List[Any]		 =			value
									a__  :	List[Any]		 =			mixed_qkv.size(0			) // 3

									a__  :	Optional[int]		 =			mixed_qkv[:qkv_dim]
									a__  :	List[str]		 =			mixed_qkv[qkv_dim : qkv_dim * 2]
									a__  :	Optional[Any]		 =			mixed_qkv[qkv_dim * 2 :]

									a__  :	Tuple		 =			query_layer
									a__  :	int		 =			key_layer
									a__  :	Optional[int]		 =			value_layer
						else:
									a__  :	List[str]		 =			value

			return model_state_dict






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Tuple=False			)    ->			Tuple:



			'''simple docstring'''
			a__ ,	a__  :	Tuple		 =			init_clap(lowerCAmelCase__							,			enable_fusion=lowerCAmelCase__			)

			clap_model.eval()
			a__  :	Optional[int]		 =			clap_model.state_dict()
			a__  :	Optional[Any]		 =			rename_state_dict(lowerCAmelCase__			)

			a__  :	Union[str, Any]		 =			ClapConfig()
			a__  :	Dict		 =			enable_fusion
			a__  :	Any		 =			ClapModel(lowerCAmelCase__			)

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

			model.save_pretrained(lowerCAmelCase__			)
			transformers_config.save_pretrained(lowerCAmelCase__			)


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)