


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A							:					Any     						=     {
    '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							:					Optional[int]     						=     AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase=False )						->	List[Any]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Any:


						'''simple docstring'''






						__snake_case				=		{}

						__snake_case				=		r".*sequential.(\d+).*"
						__snake_case				=		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:
																								__snake_case				=		key.replace(__UpperCamelCase      ,	__UpperCamelCase )

												if re.match(__UpperCamelCase      ,	__UpperCamelCase ):
																		# replace sequential layers with list
																		__snake_case				=		re.match(__UpperCamelCase      ,	__UpperCamelCase ).group(1 )

																		__snake_case				=		key.replace(F'''sequential.{sequential_layer}.'''      ,	F'''layers.{int(__UpperCamelCase )//3}.linear.''' )
												elif re.match(__UpperCamelCase      ,	__UpperCamelCase ):
																		__snake_case				=		int(re.match(__UpperCamelCase      ,	__UpperCamelCase ).group(1 ) )

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

																		__snake_case				=		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
																		__snake_case				=		value
																		__snake_case				=		mixed_qkv.size(0 ) // 3

																		__snake_case				=		mixed_qkv[:qkv_dim]
																		__snake_case				=		mixed_qkv[qkv_dim : qkv_dim * 2]
																		__snake_case				=		mixed_qkv[qkv_dim * 2 :]

																		__snake_case				=		query_layer
																		__snake_case				=		key_layer
																		__snake_case				=		value_layer
												else:
																		__snake_case				=		value

						return model_state_dict





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=False )						->	str:


						'''simple docstring'''






						__snake_case		,					__snake_case				=		init_clap(__UpperCamelCase      ,	enable_fusion=__UpperCamelCase )

						clap_model.eval()
						__snake_case				=		clap_model.state_dict()
						__snake_case				=		rename_state_dict(__UpperCamelCase )

						__snake_case				=		ClapConfig()
						__snake_case				=		enable_fusion
						__snake_case				=		ClapModel(__UpperCamelCase )

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

						model.save_pretrained(__UpperCamelCase )
						transformers_config.save_pretrained(__UpperCamelCase )


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

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









from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A							:					Union[str, Any]     						=     logging.get_logger(__name__)

A							:					Tuple     						=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""data2vec-vision"""


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_2     ,    SCREAMING_SNAKE_CASE	:		int=1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.02     ,    SCREAMING_SNAKE_CASE	:		List[str]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2_2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=True     ,    SCREAMING_SNAKE_CASE	:		List[str]=[3, 5, 7, 1_1]     ,    SCREAMING_SNAKE_CASE	:		int=[1, 2, 3, 6]     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_5_5     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	List[str]:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps

											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		use_mask_token
											__snake_case				=		use_absolute_position_embeddings
											__snake_case				=		use_relative_position_bias
											__snake_case				=		use_shared_relative_position_bias
											__snake_case				=		layer_scale_init_value
											__snake_case				=		drop_path_rate
											__snake_case				=		use_mean_pooling
											# decode head attributes (semantic segmentation)
											__snake_case				=		out_indices
											__snake_case				=		pool_scales
											# auxiliary head attributes (semantic segmentation)
											__snake_case				=		use_auxiliary_head
											__snake_case				=		auxiliary_loss_weight
											__snake_case				=		auxiliary_channels
											__snake_case				=		auxiliary_num_convs
											__snake_case				=		auxiliary_concat_input
											__snake_case				=		semantic_loss_ignore_index






class     UpperCamelCase(  _a   ):
					snake_case_		:				Any										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	float:




											'''simple docstring'''

											return 1e-4





