




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase       :	str									=     {
    "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       :	List[str]									=     AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: int=False  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{}

      lowerCamelCase_	  =					r'.*sequential.(\d+).*'
      lowerCamelCase_	  =					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:
                        lowerCamelCase_	  =					key.replace(lowercase			,							lowercase  )

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

                  lowerCamelCase_	  =					key.replace(f"""sequential.{sequential_layer}."""			,							f"""layers.{int(lowercase  )//3}.linear."""  )
            elif re.match(lowercase			,							lowercase  ):
                  lowerCamelCase_	  =					int(re.match(lowercase			,							lowercase  ).group(1  )  )

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

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

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

                  lowerCamelCase_	  =					query_layer
                  lowerCamelCase_	  =					key_layer
                  lowerCamelCase_	  =					value_layer
            else:
                  lowerCamelCase_	  =					value

      return model_state_dict

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Optional[Any]			,							lowercase			: int			,							lowercase			: List[Any]=False  ):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					init_clap(lowercase			,							enable_fusion=lowercase  )

      clap_model.eval()
      lowerCamelCase_	  =					clap_model.state_dict()
      lowerCamelCase_	  =					rename_state_dict(lowercase  )

      lowerCamelCase_	  =					ClapConfig()
      lowerCamelCase_	  =					enable_fusion
      lowerCamelCase_	  =					ClapModel(lowercase  )

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

      model.save_pretrained(lowercase  )
      transformers_config.save_pretrained(lowercase  )


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

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





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					lbp.get_neighbors_pixel(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase_	  =					np.zeros((image.shape[0], image.shape[1])  )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0			,							image.shape[0]  ):
            for j in range(0			,							image.shape[1]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



