
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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",
}

snake_case :		List[Any]							      =  AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")






def   lowerCAmelCase_						(					_snake_case      :  Tuple      ,   _snake_case      :  Tuple=False   )				->		Optional[Any]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     {}

	__magic_name__		:		Dict	   =     R".*sequential.(\d+).*"
	__magic_name__		:		List[str]	   =     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:
				__magic_name__		:		Any	   =     key.replace(_snake_case      ,   _snake_case   )

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

			__magic_name__		:		Union[str, Any]	   =     key.replace(F'''sequential.{sequential_layer}.'''      ,   F'''layers.{int(_snake_case   )//3}.linear.'''   )
		elif re.match(_snake_case      ,   _snake_case   ):
			__magic_name__		:		Tuple	   =     int(re.match(_snake_case      ,   _snake_case   ).group(1   )   )

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

			__magic_name__		:		int	   =     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
			__magic_name__		:		Union[str, Any]	   =     value
			__magic_name__		:		List[str]	   =     mixed_qkv.size(0   ) // 3

			__magic_name__		:		int	   =     mixed_qkv[:qkv_dim]
			__magic_name__		:		str	   =     mixed_qkv[qkv_dim : qkv_dim * 2]
			__magic_name__		:		List[str]	   =     mixed_qkv[qkv_dim * 2 :]

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

	return model_state_dict






def   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any      ,   _snake_case      :  Any=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__   ,    __magic_name__		:		Any	   =     init_clap(_snake_case      ,   enable_fusion=_snake_case   )

	clap_model.eval()
	__magic_name__		:		Union[str, Any]	   =     clap_model.state_dict()
	__magic_name__		:		Tuple	   =     rename_state_dict(_snake_case   )

	__magic_name__		:		List[str]	   =     ClapConfig()
	__magic_name__		:		Tuple	   =     enable_fusion
	__magic_name__		:		Any	   =     ClapModel(_snake_case   )

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

	model.save_pretrained(_snake_case   )
	transformers_config.save_pretrained(_snake_case   )


if __name__ == "__main__":
				snake_case :		List[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")
				snake_case :		List[str]							      =  parser.parse_args()

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


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
