





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_     							= {
    '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',
}

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			Any=False      )      ->	Optional[int]:
    UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      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_    (  __UpperCAmelCase:			Optional[Any]      )      ->	Optional[Any]:
    UpperCamelCase__							:				Union[str, Any]          =      {}

    UpperCamelCase__							:				Dict          =      r'''.*sequential.(\d+).*'''
    UpperCamelCase__							:				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:
                UpperCamelCase__							:				Any          =      key.replace(__UpperCAmelCase		,   __UpperCAmelCase      )

        if re.match(__UpperCAmelCase		,   __UpperCAmelCase      ):
            # replace sequential layers with list
            UpperCamelCase__							:				Union[str, Any]          =      re.match(__UpperCAmelCase		,   __UpperCAmelCase      ).group(1      )

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

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

            UpperCamelCase__							:				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
            UpperCamelCase__							:				List[Any]          =      value
            UpperCamelCase__							:				Any          =      mixed_qkv.size(0      ) // 3

            UpperCamelCase__							:				Optional[int]          =      mixed_qkv[:qkv_dim]
            UpperCamelCase__							:				Optional[Any]          =      mixed_qkv[qkv_dim : qkv_dim * 2]
            UpperCamelCase__							:				List[Any]          =      mixed_qkv[qkv_dim * 2 :]

            UpperCamelCase__							:				List[Any]          =      query_layer
            UpperCamelCase__							:				Union[str, Any]          =      key_layer
            UpperCamelCase__							:				List[str]          =      value_layer
        else:
            UpperCamelCase__							:				List[str]          =      value

    return model_state_dict




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			List[Any]=False      )      ->	Optional[Any]:
    UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      init_clap(__UpperCAmelCase		,   enable_fusion=__UpperCAmelCase      )

    clap_model.eval()
    UpperCamelCase__							:				Optional[Any]          =      clap_model.state_dict()
    UpperCamelCase__							:				Any          =      rename_state_dict(__UpperCAmelCase      )

    UpperCamelCase__							:				Optional[Any]          =      ClapConfig()
    UpperCamelCase__							:				List[str]          =      enable_fusion
    UpperCamelCase__							:				str          =      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__":
  UpperCAmelCase_     							= 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')
  UpperCAmelCase_     							= 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

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

