




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 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 UpperCAmelCase__							 =						{}

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

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

  UpperCAmelCase__							 =						value.float()

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

 return upgrade



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   int=None	):




 """simple docstring"""







 if config_path is not None:
  UpperCAmelCase__							 =						FlavaConfig.from_pretrained(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						FlavaConfig()

 UpperCAmelCase__							 =						FlavaForPreTraining(_lowerCAmelCase	).eval()

 UpperCAmelCase__							 =						convert_dalle_checkpoint(_lowerCAmelCase						,							_lowerCAmelCase						,							save_checkpoint=_lowerCAmelCase	)

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

 UpperCAmelCase__							 =						upgrade_state_dict(_lowerCAmelCase						,							_lowerCAmelCase	)
 hf_model.load_state_dict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						hf_model.state_dict()
 UpperCAmelCase__							 =						count_parameters(_lowerCAmelCase	)
 UpperCAmelCase__							 =						count_parameters(_lowerCAmelCase	) + count_parameters(_lowerCAmelCase	)

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

 hf_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   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)











import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowerCAmelCase						:   Union[str, Any]		=   (7_2_0, 1_2_8_0)  # Height, Width
_lowerCAmelCase						:   Any		=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCAmelCase						:   List[str]		=   1 / 1_0_0
_lowerCAmelCase						:   Union[str, Any]		=   ""
_lowerCAmelCase						:   Any		=   ""
_lowerCAmelCase						:   List[str]		=   ""
_lowerCAmelCase						:   Union[str, Any]		=   2_5_0



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__ , UpperCAmelCase__							 =						get_dataset(_lowerCAmelCase						,							_lowerCAmelCase	)
 for index in range(_lowerCAmelCase	):
  UpperCAmelCase__							 =						random.sample(range(len(_lowerCAmelCase	)	)						,							4	)
  UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						update_image_and_anno(
      _lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							filter_scale=_lowerCAmelCase						,							)

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  UpperCAmelCase__							 =						random_chars(32	)
  UpperCAmelCase__							 =						path.split(os.sep	)[-1].rsplit("."						,							1	)[0]
  UpperCAmelCase__							 =						F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
  cva.imwrite(F'''{file_root}.jpg'''						,							_lowerCAmelCase						,							[cva.IMWRITE_JPEG_QUALITY, 85]	)
  print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''	)
  UpperCAmelCase__							 =						[]
  for anno in new_annos:
   UpperCAmelCase__							 =						anno[3] - anno[1]
   UpperCAmelCase__							 =						anno[4] - anno[2]
   UpperCAmelCase__							 =						anno[1] + width / 2
   UpperCAmelCase__							 =						anno[2] + height / 2
   UpperCAmelCase__							 =						F'''{anno[0]} {x_center} {y_center} {width} {height}'''
   annos_list.append(_lowerCAmelCase	)
  with open(F'''{file_root}.txt'''						,							"w"	) as outfile:
   outfile.write("\n".join(line for line in annos_list	)	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						[]
 for label_file in glob.glob(os.path.join(_lowerCAmelCase						,							"*.txt"	)	):
  UpperCAmelCase__							 =						label_file.split(os.sep	)[-1].rsplit("."						,							1	)[0]
  with open(_lowerCAmelCase	) as in_file:
   UpperCAmelCase__							 =						in_file.readlines()
  UpperCAmelCase__							 =						os.path.join(_lowerCAmelCase						,							F'''{label_name}.jpg'''	)

  UpperCAmelCase__							 =						[]
  for obj_list in obj_lists:
   UpperCAmelCase__							 =						obj_list.rstrip("\n"	).split(" "	)
   UpperCAmelCase__							 =						float(obj[1]	) - float(obj[3]	) / 2
   UpperCAmelCase__							 =						float(obj[2]	) - float(obj[4]	) / 2
   UpperCAmelCase__							 =						float(obj[1]	) + float(obj[3]	) / 2
   UpperCAmelCase__							 =						float(obj[2]	) + float(obj[4]	) / 2

   boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
  if not boxes:
   continue
  img_paths.append(_lowerCAmelCase	)
  labels.append(_lowerCAmelCase	)
 return img_paths, labels



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list[int]						,							_lowerCAmelCase							:   tuple[int, int]						,							_lowerCAmelCase							:   tuple[float, float]						,							_lowerCAmelCase							:   float = 0.0						,							):




 """simple docstring"""







 UpperCAmelCase__							 =						np.zeros([output_size[0], output_size[1], 3]						,							dtype=np.uinta	)
 UpperCAmelCase__							 =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCAmelCase__							 =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 UpperCAmelCase__							 =						int(scale_x * output_size[1]	)
 UpperCAmelCase__							 =						int(scale_y * output_size[0]	)

 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						[]
 for i, index in enumerate(_lowerCAmelCase	):
  UpperCAmelCase__							 =						all_img_list[index]
  path_list.append(_lowerCAmelCase	)
  UpperCAmelCase__							 =						all_annos[index]
  UpperCAmelCase__							 =						cva.imread(_lowerCAmelCase	)
  if i == 0:  # top-left
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(divid_point_x, divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						bbox[1] * scale_x
    UpperCAmelCase__							 =						bbox[2] * scale_y
    UpperCAmelCase__							 =						bbox[3] * scale_x
    UpperCAmelCase__							 =						bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
  elif i == 1:  # top-right
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(output_size[1] - divid_point_x, divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						scale_x + bbox[1] * (1 - scale_x)
    UpperCAmelCase__							 =						bbox[2] * scale_y
    UpperCAmelCase__							 =						scale_x + bbox[3] * (1 - scale_x)
    UpperCAmelCase__							 =						bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
  elif i == 2:  # bottom-left
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(divid_point_x, output_size[0] - divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						bbox[1] * scale_x
    UpperCAmelCase__							 =						scale_y + bbox[2] * (1 - scale_y)
    UpperCAmelCase__							 =						bbox[3] * scale_x
    UpperCAmelCase__							 =						scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
  else:  # bottom-right
   UpperCAmelCase__							 =						cva.resize(
       _lowerCAmelCase						,							(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
   UpperCAmelCase__							 =						img
   for bbox in img_annos:
    UpperCAmelCase__							 =						scale_x + bbox[1] * (1 - scale_x)
    UpperCAmelCase__							 =						scale_y + bbox[2] * (1 - scale_y)
    UpperCAmelCase__							 =						scale_x + bbox[3] * (1 - scale_x)
    UpperCAmelCase__							 =						scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  UpperCAmelCase__							 =						[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	):




 """simple docstring"""







 assert number_char > 1, "The number of character should greater than 1"
 UpperCAmelCase__							 =						ascii_lowercase + digits
 return "".join(random.choice(_lowerCAmelCase	) for _ in range(_lowerCAmelCase	)	)


if __name__ == "__main__":
  main()
  print("DONE ✅")





