'''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 	lowerCAmelCase_				(					snake_case_       : str			)    ->      str:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : List[str]			)    ->      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_				(					snake_case_       : Union[str, Any]					,    snake_case_       : List[str]					,    snake_case_       : str					,    snake_case_       : str=None			)    ->      Tuple:


     '''simple docstring'''




     if config_path is not None:
          UpperCAmelCase_           =   FlavaConfig.from_pretrained(snake_case_			)
     else:
          UpperCAmelCase_           =   FlavaConfig()

     UpperCAmelCase_           =   FlavaForPreTraining(snake_case_			).eval()

     UpperCAmelCase_           =   convert_dalle_checkpoint(snake_case_					,    snake_case_					,    save_checkpoint=snake_case_			)

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

     UpperCAmelCase_           =   upgrade_state_dict(snake_case_					,    snake_case_			)
     hf_model.load_state_dict(snake_case_			)
     UpperCAmelCase_           =   hf_model.state_dict()
     UpperCAmelCase_           =   count_parameters(snake_case_			)
     UpperCAmelCase_           =   count_parameters(snake_case_			) + count_parameters(snake_case_			)

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

     hf_model.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     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')
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =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]						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if (direction == 1 and array[indexa] > array[indexa]) or (
				    direction == 0 and array[indexa] < array[indexa]
				):
								lowercase_  ,	lowercase_       =					array[indexa], array[indexa]




def 				a						(							snake_case__:					list[int]						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if length > 1:
								lowercase_       =					int(length / 2					)
								for i in range(snake_case__						,   low + middle					):
												comp_and_swap(snake_case__						,   snake_case__						,   i + middle						,   snake_case__					)
								bitonic_merge(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
								bitonic_merge(snake_case__						,   low + middle						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					list[int]						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int					):


				'''simple docstring'''
				if length > 1:
								lowercase_       =					int(length / 2					)
								bitonic_sort(snake_case__						,   snake_case__						,   snake_case__						,   1					)
								bitonic_sort(snake_case__						,   low + middle						,   snake_case__						,   0					)
								bitonic_merge(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)


if __name__ == "__main__":
						__a  			=					input('Enter numbers separated by a comma:\n').strip()
						__a  			=					[int(item.strip()) for item in user_input.split(',')]

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print('\nSorted array in ascending order is: ', end='')
						print(*unsorted, sep=', ')

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print('Sorted array in descending order is: ', end='')
						print(*unsorted, sep=', ')




