


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
from ..auto import CONFIG_MAPPING


_snake_case  :	Tuple			      =       logging.get_logger(__name__)

_snake_case  :	List[Any]			      =       {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Tuple  						=						"table-transformer"
							__UpperCAmelCase					:							Optional[int]  						=						["past_key_values"]
							__UpperCAmelCase					:							int  						=						{
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}

							def __init__(							self :						List[Any]					,			lowerCamelCase :						str=True					,			lowerCamelCase :						Tuple=None					,			lowerCamelCase :						Dict=3					,			lowerCamelCase :						Optional[int]=100					,			lowerCamelCase :						List[str]=6					,			lowerCamelCase :						Dict=2048					,			lowerCamelCase :						str=8					,			lowerCamelCase :						Dict=6					,			lowerCamelCase :						List[str]=2048					,			lowerCamelCase :						Optional[int]=8					,			lowerCamelCase :						Tuple=0.0					,			lowerCamelCase :						List[str]=0.0					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Optional[Any]="relu"					,			lowerCamelCase :						int=256					,			lowerCamelCase :						str=0.1					,			lowerCamelCase :						int=0.0					,			lowerCamelCase :						List[Any]=0.0					,			lowerCamelCase :						Any=0.02					,			lowerCamelCase :						List[Any]=1.0					,			lowerCamelCase :						str=False					,			lowerCamelCase :						Optional[Any]="sine"					,			lowerCamelCase :						Optional[Any]="resnet50"					,			lowerCamelCase :						Any=True					,			lowerCamelCase :						int=False					,			lowerCamelCase :						int=1					,			lowerCamelCase :						Union[str, Any]=5					,			lowerCamelCase :						List[Any]=2					,			lowerCamelCase :						str=1					,			lowerCamelCase :						Union[str, Any]=1					,			lowerCamelCase :						Optional[int]=5					,			lowerCamelCase :						Tuple=2					,			lowerCamelCase :						List[Any]=0.1					,			**lowerCamelCase :						Optional[Any]					,			)			->					str:
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."       )

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
																												__snake_case	:    Optional[int]											=     CONFIG_MAPPING["resnet"](out_features=["stage4"]       )
																					elif isinstance(lowerCamelCase					,			lowerCamelCase       ):
																												__snake_case	:    Optional[int]											=     backbone_config.get("model_type"       )
																												__snake_case	:    Union[str, Any]											=     CONFIG_MAPPING[backbone_model_type]
																												__snake_case	:    Union[str, Any]											=     config_class.from_dict(lowerCamelCase       )
																					# set timm attributes to None
																					__snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     None, None, None

														__snake_case	:    Optional[Any]											=     use_timm_backbone
														__snake_case	:    Optional[int]											=     backbone_config
														__snake_case	:    Any											=     num_channels
														__snake_case	:    List[Any]											=     num_queries
														__snake_case	:    Optional[int]											=     d_model
														__snake_case	:    Optional[int]											=     encoder_ffn_dim
														__snake_case	:    Dict											=     encoder_layers
														__snake_case	:    Union[str, Any]											=     encoder_attention_heads
														__snake_case	:    Optional[Any]											=     decoder_ffn_dim
														__snake_case	:    Any											=     decoder_layers
														__snake_case	:    Dict											=     decoder_attention_heads
														__snake_case	:    List[str]											=     dropout
														__snake_case	:    List[Any]											=     attention_dropout
														__snake_case	:    Union[str, Any]											=     activation_dropout
														__snake_case	:    List[str]											=     activation_function
														__snake_case	:    List[str]											=     init_std
														__snake_case	:    int											=     init_xavier_std
														__snake_case	:    Tuple											=     encoder_layerdrop
														__snake_case	:    str											=     decoder_layerdrop
														__snake_case	:    Tuple											=     encoder_layers
														__snake_case	:    Optional[int]											=     auxiliary_loss
														__snake_case	:    Tuple											=     position_embedding_type
														__snake_case	:    str											=     backbone
														__snake_case	:    Union[str, Any]											=     use_pretrained_backbone
														__snake_case	:    Tuple											=     dilation
														# Hungarian matcher
														__snake_case	:    Union[str, Any]											=     class_cost
														__snake_case	:    Dict											=     bbox_cost
														__snake_case	:    List[Any]											=     giou_cost
														# Loss coefficients
														__snake_case	:    Any											=     mask_loss_coefficient
														__snake_case	:    Optional[int]											=     dice_loss_coefficient
														__snake_case	:    str											=     bbox_loss_coefficient
														__snake_case	:    Dict											=     giou_loss_coefficient
														__snake_case	:    List[str]											=     eos_coefficient
														super().__init__(is_encoder_decoder=lowerCamelCase					,			**lowerCamelCase       )

							@property
							def 					__snake_case      (							self :						int       )			->					int:
														return self.encoder_attention_heads






							@property
							def 					__snake_case      (							self :						Optional[int]       )			->					int:
														return self.d_model

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							str  						=						version.parse("1.11"   )

							@property
							def 					__snake_case      (							self :						List[Any]       )			->					Mapping[str, Mapping[int, str]]:
														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														        ("pixel_mask", {0: "batch"}),
														    ]       )

							@property
							def 					__snake_case      (							self :						Optional[int]       )			->					float:
														return 1E-5






							@property
							def 					__snake_case      (							self :						List[Any]       )			->					int:
														return 12





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "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  :	Dict			      =       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    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			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    Any											=     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	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

														if re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					# replace sequential layers with list
																					__snake_case	:    List[Any]											=     re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)

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

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     1 if projecton_layer == 0 else 2

																					__snake_case	:    Tuple											=     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	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

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

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[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__":
			_snake_case  :	Union[str, 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  :	Tuple			      =       parser.parse_args()

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

