

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__			(       _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 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							{}

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

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

		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							value.float()

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

	return upgrade

@torch.no_grad()
def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a=None):
	if config_path is not None:
		SCREAMING_SNAKE_CASE :   Any					  =							FlavaConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE :   Tuple					  =							FlavaConfig()

	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							FlavaForPreTraining(_a).eval()

	SCREAMING_SNAKE_CASE :   int					  =							convert_dalle_checkpoint(_a				,      _a				,      save_checkpoint=_a)

	if os.path.exists(_a):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.load(_a				,      map_location="cpu")
	else:
		SCREAMING_SNAKE_CASE :   str					  =							torch.hub.load_state_dict_from_url(_a				,      map_location="cpu")

	SCREAMING_SNAKE_CASE :   Tuple					  =							upgrade_state_dict(_a				,      _a)
	hf_model.load_state_dict(_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							hf_model.state_dict()
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							count_parameters(_a)
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							count_parameters(_a) + count_parameters(_a)

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

	hf_model.save_pretrained(_a)


if __name__ == "__main__":
			a_      =     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')
			a_      =     parser.parse_args()

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



from __future__ import annotations




def    a__    (		snake_case__					:							list[int]     ):
  if len(snake_case__     ) == 0:
    return array

  _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     min(snake_case__     ), max(snake_case__     )

  # Compute the variables
  _UpperCAmelCase		:							Tuple							=     _max - _min + 1
  _UpperCAmelCase,_UpperCAmelCase		:							Union[str, Any]							=     [0] * holes_range, [0] * holes_range

  # Make the sorting.
  for i in array:
    _UpperCAmelCase		:							Optional[int]							=     i - _min
    _UpperCAmelCase		:							Any							=     i
    holes_repeat[index] += 1

  # Makes the array back by replacing the numbers.
  _UpperCAmelCase		:							List[Any]							=     0
  for i in range(snake_case__     ):
    while holes_repeat[i] > 0:
      _UpperCAmelCase		:							Optional[Any]							=     holes[i]
      index += 1
      holes_repeat[i] -= 1

    # Returns the sorted array.
  return array


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE__					:     Dict       =     input('Enter numbers separated by comma:\n')
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     [int(x) for x in user_input.split(',')]
 print(pigeon_sort(unsorted))





