





"""simple docstring"""






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase						=			logging.get_logger(__name__)

__UpperCAmelCase						=			{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Optional[Any]				     =				"umt5"
				__lowerCamelCase				:	Optional[int]				     =				["past_key_values"]
				def __init__(		self    :			Optional[Any]	, a_    :			Any=25_01_12	, a_    :			Optional[int]=5_12	, a_    :			Any=64	, a_    :			Tuple=10_24	, a_    :			Optional[Any]=8	, a_    :			Optional[int]=None	, a_    :			Union[str, Any]=6	, a_    :			List[str]=32	, a_    :			Any=1_28	, a_    :			Tuple=0.1	, a_    :			int=1E-6	, a_    :			Dict=1.0	, a_    :			str="gated-gelu"	, a_    :			Optional[int]=True	, a_    :			List[str]=True	, a_    :			Any="T5Tokenizer"	, a_    :			List[Any]=True	, a_    :			Dict=0	, a_    :			Union[str, Any]=1	, a_    :			Tuple=0	, **a_    :			List[Any]	, )	->	Any:



							'''simple docstring'''



							super().__init__(
							    is_encoder_decoder=a_	, tokenizer_class=a_	, tie_word_embeddings=a_	, pad_token_id=a_	, eos_token_id=a_	, decoder_start_token_id=a_	, **a_	, )
							a__  :	Dict		 =			vocab_size
							a__  :	Union[str, Any]		 =			d_model
							a__  :	Dict		 =			d_kv
							a__  :	Dict		 =			d_ff
							a__  :	Dict		 =			num_layers
							a__  :	List[str]		 =			(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							a__  :	List[str]		 =			num_heads
							a__  :	Tuple		 =			relative_attention_num_buckets
							a__  :	Optional[int]		 =			relative_attention_max_distance
							a__  :	List[Any]		 =			dropout_rate
							a__  :	Tuple		 =			layer_norm_epsilon
							a__  :	Dict		 =			initializer_factor
							a__  :	Union[str, Any]		 =			feed_forward_proj
							a__  :	int		 =			use_cache

							a__  :	Dict		 =			self.feed_forward_proj.split("-"    )
							a__  :	Dict		 =			act_info[-1]
							a__  :	int		 =			act_info[0] == "gated"

							if len(a_    ) > 1 and act_info[0] != "gated" or len(a_    ) > 2:
										raise ValueError(
										    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
										    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
										    "'gated-gelu' or 'relu'"    )

							if feed_forward_proj == "gated-gelu":
										a__  :	Tuple		 =			"gelu_new"
				@property
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[str]:



							'''simple docstring'''



							return self.d_model
				@property
				def       UpperCAmelCase      (		self    :			Any    )	->	List[Any]:



							'''simple docstring'''



							return self.num_heads






				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[str]:



							'''simple docstring'''



							return self.num_layers



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Mapping[str, Mapping[int, str]]:



							'''simple docstring'''



							a__  :	Optional[int]		 =			{
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
										a__  :	List[Any]		 =			"past_encoder_sequence + sequence"
										a__  :	Any		 =			{0: "batch"}
										a__  :	int		 =			{0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
										a__  :	int		 =			{0: "batch", 1: "decoder_sequence"}
										a__  :	str		 =			{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
										self.fill_with_past_key_values_(a_	, direction="inputs"    )

							return common_inputs
				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	int:



							'''simple docstring'''



							return 13






				@property
				def       UpperCAmelCase      (		self    :			Dict    )	->	float:



							'''simple docstring'''



							return 5E-4






"""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)