



'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCAmelCase									=   {
    '''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''',
}

__lowerCAmelCase									=   AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Any:
		_a       ,					_a     :      Union[str, Any]        =							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      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		_a     :      Optional[int]        =							{}

		_a     :      Tuple        =							r'.*sequential.(\d+).*'
		_a     :      int        =							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     :      Union[str, Any]        =							key.replace(lowerCAmelCase_						,			lowerCAmelCase_  )

				if re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						# replace sequential layers with list
						_a     :      List[str]        =							re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )

						_a     :      Optional[Any]        =							key.replace(f"""sequential.{sequential_layer}."""						,			f"""layers.{int(lowerCAmelCase_  )//3}.linear."""  )
				elif re.match(lowerCAmelCase_						,			lowerCAmelCase_  ):
						_a     :      str        =							int(re.match(lowerCAmelCase_						,			lowerCAmelCase_  ).group(1  )  )

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

						_a     :      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
						_a     :      str        =							value
						_a     :      List[str]        =							mixed_qkv.size(0  ) // 3

						_a     :      str        =							mixed_qkv[:qkv_dim]
						_a     :      int        =							mixed_qkv[qkv_dim : qkv_dim * 2]
						_a     :      Any        =							mixed_qkv[qkv_dim * 2 :]

						_a     :      List[Any]        =							query_layer
						_a     :      Union[str, Any]        =							key_layer
						_a     :      Tuple        =							value_layer
				else:
						_a     :      Dict        =							value

		return model_state_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a       ,					_a     :      Optional[Any]        =							init_clap(lowerCAmelCase_						,			enable_fusion=lowerCAmelCase_  )

		clap_model.eval()
		_a     :      Tuple        =							clap_model.state_dict()
		_a     :      Optional[int]        =							rename_state_dict(lowerCAmelCase_  )

		_a     :      List[str]        =							ClapConfig()
		_a     :      Tuple        =							enable_fusion
		_a     :      int        =							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__":
							__lowerCAmelCase									=   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''')
							__lowerCAmelCase									=   parser.parse_args()

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












from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





