



"""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 __lowercase    (							_a					):
    # encoder.embeddings are double copied in original FLAVA
    return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()					)




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Any       =			{}

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

        snake_case_				:  str       =			key.replace('''heads.cmd.mim_head.cls.predictions'''	,    '''mmm_image_head'''					)
        snake_case_				:  Optional[int]       =			key.replace('''heads.cmd.mlm_head.cls.predictions'''	,    '''mmm_text_head'''					)
        snake_case_				:  Dict       =			key.replace('''heads.cmd.itm_head.cls'''	,    '''itm_head'''					)
        snake_case_				:  Tuple       =			key.replace('''heads.cmd.itm_head.pooler'''	,    '''itm_head.pooler'''					)
        snake_case_				:  List[Any]       =			key.replace('''heads.cmd.clip_head.logit_scale'''	,    '''flava.logit_scale'''					)
        snake_case_				:  str       =			key.replace('''heads.fairseq_mlm.cls.predictions'''	,    '''mlm_head'''					)
        snake_case_				:  int       =			key.replace('''heads.imagenet.mim_head.cls.predictions'''	,    '''mim_head'''					)
        snake_case_				:  Dict       =			key.replace('''mm_text_projection'''	,    '''flava.text_to_mm_projection'''					)
        snake_case_				:  int       =			key.replace('''mm_image_projection'''	,    '''flava.image_to_mm_projection'''					)
        snake_case_				:  int       =			key.replace('''image_encoder.module'''	,    '''flava.image_model'''					)
        snake_case_				:  List[str]       =			key.replace('''text_encoder.module'''	,    '''flava.text_model'''					)
        snake_case_				:  Dict       =			key.replace('''mm_encoder.module.encoder.cls_token'''	,    '''flava.multimodal_model.cls_token'''					)
        snake_case_				:  List[str]       =			key.replace('''mm_encoder.module'''	,    '''flava.multimodal_model'''					)
        snake_case_				:  Tuple       =			key.replace('''text_projection'''	,    '''flava.text_projection'''					)
        snake_case_				:  Any       =			key.replace('''image_projection'''	,    '''flava.image_projection'''					)

        snake_case_				:  int       =			value.float()

    for key, value in codebook_state_dict.items():
        snake_case_				:  List[Any]       =			value

    return upgrade




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a	,    _a=None					):
    if config_path is not None:
        snake_case_				:  Dict       =			FlavaConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Dict       =			FlavaConfig()

    snake_case_				:  Optional[Any]       =			FlavaForPreTraining(_a					).eval()

    snake_case_				:  Dict       =			convert_dalle_checkpoint(_a	,    _a	,    save_checkpoint=_a					)

    if os.path.exists(_a					):
        snake_case_				:  int       =			torch.load(_a	,    map_location='''cpu'''					)
    else:
        snake_case_				:  Optional[int]       =			torch.hub.load_state_dict_from_url(_a	,    map_location='''cpu'''					)

    snake_case_				:  str       =			upgrade_state_dict(_a	,    _a					)
    hf_model.load_state_dict(_a					)
    snake_case_				:  Any       =			hf_model.state_dict()
    snake_case_				:  Any       =			count_parameters(_a					)
    snake_case_				:  int       =			count_parameters(_a					) + count_parameters(_a					)

    assert torch.allclose(_a	,    _a	,    atol=1E-3					)

    hf_model.save_pretrained(_a					)


if __name__ == "__main__":
      lowercase__					:						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 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''')
      lowercase__					:						Optional[Any]												=			parser.parse_args()

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










"""simple docstring"""

import math
import sys

import cva
import numpy as np




def __lowercase    (							_a	,    _a					):
    # For applying gaussian function for each element in matrix.
    snake_case_				:  Dict       =			math.sqrt(_a					)
    snake_case_				:  Tuple       =			1 / (sigma * math.sqrt(2 * math.pi					))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5					)




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Optional[int]       =			kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def __lowercase    (							_a	,    _a					):
    # Creates a gaussian kernel of given dimension.
    snake_case_				:  Union[str, Any]       =			np.zeros((kernel_size, kernel_size)					)
    for i in range(0	,    _a					):
        for j in range(0	,    _a					):
            snake_case_				:  Union[str, Any]       =			math.sqrt(
                abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
    return vec_gaussian(_a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    ):
    snake_case_				:  int       =			np.zeros(img.shape					)
    snake_case_				:  str       =			get_gauss_kernel(_a	,    _a					)
    snake_case_,			snake_case_				:  Any       =			img.shape
    for i in range(kernel_size // 2	,    size_x - kernel_size // 2					):
        for j in range(kernel_size // 2	,    size_y - kernel_size // 2					):
            snake_case_				:  List[Any]       =			get_slice(_a	,    _a	,    _a	,    _a					)
            snake_case_				:  Union[str, Any]       =			img_s - img_s[kernel_size // 2, kernel_size // 2]
            snake_case_				:  Optional[Any]       =			vec_gaussian(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  List[Any]       =			np.sum(_a					) / np.sum(_a					)
            snake_case_				:  Dict       =			val
    return imga




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			args[1] if args[1:] else '''../image_data/lena.jpg'''
    snake_case_				:  int       =			float(args[2]					) if args[2:] else 1.0
    snake_case_				:  Dict       =			float(args[3]					) if args[3:] else 1.0
    if args[4:]:
        snake_case_				:  Optional[Any]       =			int(args[4]					)
        snake_case_				:  Union[str, Any]       =			kernel_size + abs(kernel_size % 2 - 1					)
    else:
        snake_case_				:  Union[str, Any]       =			5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     lowercase__					,lowercase__					,lowercase__					,lowercase__					:						Tuple												=			parse_args(sys.argv)
     lowercase__					:						str												=			cva.imread(filename, 0)
     cva.imshow('''input image''', img)

     lowercase__					:						Tuple												=			img / 2_55
     lowercase__					:						Optional[int]												=			out.astype('''float32''')
     lowercase__					:						Union[str, Any]												=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowercase__					:						List[str]												=			out * 2_55
     lowercase__					:						List[str]												=			np.uinta(out)
     cva.imshow('''output image''', out)
     cva.waitKey(0)
     cva.destroyAllWindows()





