





"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A			      =	{
    '''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''',
}

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

def      __A   (      a_    :Any ,							a_    :Union[str, Any]=False)		->		Any:
				__a							,    __a			:    str      							=	create_model(
				    '''HTSAT-tiny''' ,							'''roberta''' ,							a_ ,							precision='''fp32''' ,							device='''cuda:0''' if torch.cuda.is_available() else '''cpu''' ,							enable_fusion=a_ ,							fusion_type='''aff_2d''' if enable_fusion else None ,							)
				return model, model_cfg

def      __A   (      a_    :Union[str, Any])		->		List[str]:
				__a			:    str      							=	{}

				__a			:    str      							=	R'''.*sequential.(\d+).*'''
				__a			:    Optional[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			:    List[Any]      							=	key.replace(a_ ,							a_)

								if re.match(a_ ,							a_):
												# replace sequential layers with list
												__a			:    str      							=	re.match(a_ ,							a_).group(1)

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

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

												__a			:    Optional[Any]      							=	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			:    Optional[Any]      							=	value
												__a			:    Tuple      							=	mixed_qkv.size(0) // 3

												__a			:    List[str]      							=	mixed_qkv[:qkv_dim]
												__a			:    Optional[int]      							=	mixed_qkv[qkv_dim : qkv_dim * 2]
												__a			:    Tuple      							=	mixed_qkv[qkv_dim * 2 :]

												__a			:    Union[str, Any]      							=	query_layer
												__a			:    int      							=	key_layer
												__a			:    Tuple      							=	value_layer
								else:
												__a			:    str      							=	value

				return model_state_dict

def      __A   (      a_    :str ,							a_    :Optional[int] ,							a_    :Optional[int] ,							a_    :Optional[Any]=False)		->		List[Any]:
				__a							,    __a			:    List[str]      							=	init_clap(a_ ,							enable_fusion=a_)

				clap_model.eval()
				__a			:    Optional[Any]      							=	clap_model.state_dict()
				__a			:    List[str]      							=	rename_state_dict(a_)

				__a			:    Optional[int]      							=	ClapConfig()
				__a			:    int      							=	enable_fusion
				__a			:    Optional[int]      							=	ClapModel(a_)

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

				model.save_pretrained(a_)
				transformers_config.save_pretrained(a_)


if __name__ == "__main__":
		A			      =	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''')
		A			      =	parser.parse_args()

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



import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowercase							:   Union[str, Any]						=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

				def __getitem__(    self					, a__ )						->					Optional[int]:
					A             =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					, add_special_tokens=a__ ) )
					A       ,      A       ,      A             =					sentence[0], sentence[1:-1], sentence[-1]
					A             =					sentence[: self.max_seq_length]

					A             =					torch.zeros(self.n_classes )
					A             =					1

					A             =					Image.open(os.path.join(self.data_dir					, self.data[index]["""img"""] ) ).convert("""RGB""" )
					A             =					self.transforms(a__ )

					return {
					    "image_start_token": start_token,
					    "image_end_token": end_token,
					    "sentence": sentence,
					    "image": image,
					    "label": label,
					}

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

	for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__				,       UpperCamelCase__    )    ):
		A             =					input_row["""sentence"""]
		A             =					1

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					torch.stack([row["""image_end_token"""] for row in batch]    )

	return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





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





	"""simple docstring"""







	return [
	    "Crime",
	    "Drama",
	    "Thriller",
	    "Action",
	    "Comedy",
	    "Romance",
	    "Documentary",
	    "Short",
	    "Mystery",
	    "History",
	    "Family",
	    "Adventure",
	    "Fantasy",
	    "Sci-Fi",
	    "Western",
	    "Horror",
	    "Sport",
	    "War",
	    "Music",
	    "Musical",
	    "Animation",
	    "Biography",
	    "Film-Noir",
	]





def 	_lowerCAmelCase					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
	    ]    )



