


'''simple docstring'''


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__					=   logging.get_logger(__name__)






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Tuple				      =   ["pixel_values"]




			def __init__(						self     :   Optional[int]		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Union[int, float] = 1 / 2_55		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   int = 8		,     **lowerCamelCase__     :   Optional[Any]		,     )      ->None:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__       )

					_UpperCAmelCase						:				str	    =					do_rescale
					_UpperCAmelCase						:				Union[str, Any]	    =					rescale_factor
					_UpperCAmelCase						:				List[Any]	    =					do_pad
					_UpperCAmelCase						:				Optional[Any]	    =					pad_size




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   float		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Union[str, Any]       )      ->np.ndarray:



					'''simple docstring'''







					return rescale(lowerCamelCase__		,     scale=lowerCamelCase__		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Any	    =					get_image_size(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					(old_height // size + 1) * size - old_height
					_UpperCAmelCase						:				Dict	    =					(old_width // size + 1) * size - old_width

					return pad(lowerCamelCase__		,     ((0, pad_height), (0, pad_width))		,     mode="symmetric"		,     data_format=lowerCamelCase__       )






			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   ImageInput		,     lowerCamelCase__     :   Optional[bool] = None		,     lowerCamelCase__     :   Optional[float] = None		,     lowerCamelCase__     :   Optional[bool] = None		,     lowerCamelCase__     :   Optional[int] = None		,     lowerCamelCase__     :   Optional[Union[str, TensorType]] = None		,     lowerCamelCase__     :   Union[str, ChannelDimension] = ChannelDimension.FIRST		,     **lowerCamelCase__     :   Any		,     )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					do_rescale if do_rescale is not None else self.do_rescale
					_UpperCAmelCase						:				str	    =					rescale_factor if rescale_factor is not None else self.rescale_factor
					_UpperCAmelCase						:				str	    =					do_pad if do_pad is not None else self.do_pad
					_UpperCAmelCase						:				Optional[int]	    =					pad_size if pad_size is not None else self.pad_size

					_UpperCAmelCase						:				Optional[Any]	    =					make_list_of_images(lowerCamelCase__       )

					if not valid_images(lowerCamelCase__       ):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."       )

					if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True."       )

					# All transformations expect numpy arrays.
					_UpperCAmelCase						:				Optional[int]	    =					[to_numpy_array(lowerCamelCase__       ) for image in images]

					if do_rescale:
							_UpperCAmelCase						:				Tuple	    =					[self.rescale(image=lowerCamelCase__		,     scale=lowerCamelCase__       ) for image in images]

					if do_pad:
							_UpperCAmelCase						:				List[str]	    =					[self.pad(lowerCamelCase__		,     size=lowerCamelCase__       ) for image in images]

					_UpperCAmelCase						:				int	    =					[to_channel_dimension_format(lowerCamelCase__		,     lowerCamelCase__       ) for image in images]

					_UpperCAmelCase						:				List[str]	    =					{"pixel_values": images}
					return BatchFeature(data=lowerCamelCase__		,     tensor_type=lowerCamelCase__       )







'''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      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[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      ):
		_UpperCAmelCase						:				Dict	    =					{}

		_UpperCAmelCase						:				str	    =					R".*sequential.(\d+).*"
		_UpperCAmelCase						:				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:
								_UpperCAmelCase						:				Union[str, Any]	    =					key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

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

						_UpperCAmelCase						:				Optional[int]	    =					key.replace(F"""sequential.{sequential_layer}."""					,       F"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
				elif re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					int(re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )      )

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

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

						_UpperCAmelCase						:				Union[str, Any]	    =					mixed_qkv[:qkv_dim]
						_UpperCAmelCase						:				int	    =					mixed_qkv[qkv_dim : qkv_dim * 2]
						_UpperCAmelCase						:				Optional[int]	    =					mixed_qkv[qkv_dim * 2 :]

						_UpperCAmelCase						:				List[Any]	    =					query_layer
						_UpperCAmelCase						:				int	    =					key_layer
						_UpperCAmelCase						:				Any	    =					value_layer
				else:
						_UpperCAmelCase						:				Dict	    =					value

		return model_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					init_clap(__lowerCAmelCase					,       enable_fusion=__lowerCAmelCase      )

		clap_model.eval()
		_UpperCAmelCase						:				List[str]	    =					clap_model.state_dict()
		_UpperCAmelCase						:				str	    =					rename_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					ClapConfig()
		_UpperCAmelCase						:				str	    =					enable_fusion
		_UpperCAmelCase						:				Union[str, 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__":
						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)



