'''simple docstring'''

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_ : Optional[int]						  =      imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCAmelCase_ : Dict						  =      cvtColor(img, COLOR_BGR2GRAY)


def __a (	)	->							Union[str, Any]:
      '''simple docstring'''


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


def __a (	)	->							Optional[Any]:
      '''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(__lowerCamelCase ,		110				)				).startswith(
                "<PIL.Image.Image image mode=RGB size=100x100 at"				)


def __a (	)	->							List[Any]:
      '''simple docstring'''


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


def __a (	)	->							Union[str, Any]:
      '''simple docstring'''


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


def __a (	)	->							Optional[Any]:
      '''simple docstring'''


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


def __a (	)	->							Optional[int]:
      '''simple docstring'''


      lowercase_     =      array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]]				)
      lowercase_     =      conv.img_convolve(__lowerCamelCase ,		__lowerCamelCase				).astype(__lowerCamelCase				)
      assert res.any()


def __a (	)	->							Dict:
      '''simple docstring'''


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


def __a (	)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_ ,							lowercase_     =      sob.sobel_filter(__lowerCamelCase				)
      assert grad.any() and theta.any()


def __a (	)	->							Dict:
      '''simple docstring'''


      lowercase_     =      sp.make_sepia(__lowerCamelCase ,		20				)
      assert sepia.all()


def __a (	__lowerCamelCase			:						str = "digital_image_processing/image_data/lena_small.jpg"				)	->							Any:
      '''simple docstring'''


      lowercase_     =      bs.Burkes(imread(__lowerCamelCase ,		1				) ,		120				)
      burkes.process()
      assert burkes.output_img.any()


def __a (	__lowerCamelCase			:						str = "digital_image_processing/image_data/lena_small.jpg" ,		)	->							Optional[int]:
      '''simple docstring'''


      lowercase_     =      rs.NearestNeighbour(imread(__lowerCamelCase ,		1				) ,		400 ,		200				)
      nn.process()
      assert nn.output.any()






def __a (	)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      "digital_image_processing/image_data/lena.jpg"

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

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

      lowercase_     =      lbp.get_neighbors_pixel(
          __lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)

      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
      lowercase_     =      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]				):
                  lowercase_     =      lbp.local_binary_value(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)

      assert lbp_image.any()







'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


      return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      {}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase_     =      key.replace("heads.cmd.mim_head.cls.predictions" ,		"mmm_image_head"				)
            lowercase_     =      key.replace("heads.cmd.mlm_head.cls.predictions" ,		"mmm_text_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.cls" ,		"itm_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.pooler" ,		"itm_head.pooler"				)
            lowercase_     =      key.replace("heads.cmd.clip_head.logit_scale" ,		"flava.logit_scale"				)
            lowercase_     =      key.replace("heads.fairseq_mlm.cls.predictions" ,		"mlm_head"				)
            lowercase_     =      key.replace("heads.imagenet.mim_head.cls.predictions" ,		"mim_head"				)
            lowercase_     =      key.replace("mm_text_projection" ,		"flava.text_to_mm_projection"				)
            lowercase_     =      key.replace("mm_image_projection" ,		"flava.image_to_mm_projection"				)
            lowercase_     =      key.replace("image_encoder.module" ,		"flava.image_model"				)
            lowercase_     =      key.replace("text_encoder.module" ,		"flava.text_model"				)
            lowercase_     =      key.replace("mm_encoder.module.encoder.cls_token" ,		"flava.multimodal_model.cls_token"				)
            lowercase_     =      key.replace("mm_encoder.module" ,		"flava.multimodal_model"				)
            lowercase_     =      key.replace("text_projection" ,		"flava.text_projection"				)
            lowercase_     =      key.replace("image_projection" ,		"flava.image_projection"				)

            lowercase_     =      value.float()

      for key, value in codebook_state_dict.items():
            lowercase_     =      value

      return upgrade


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

      assert torch.allclose(__lowerCamelCase ,		__lowerCamelCase ,		atol=1e-3				)

      hf_model.save_pretrained(__lowerCamelCase				)


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 flava checkpoint")
       parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






