
'''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 :     str				):



      """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 :     Optional[Any]					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: str		    = {}

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

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

            _lowerCamelCase					: Dict		    = value.float()

      for key, value in codebook_state_dict.items():
            _lowerCamelCase					: Union[str, Any]		    = value

      return upgrade







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     Any=None				):



      """simple docstring"""




      if config_path is not None:
            _lowerCamelCase					: Optional[Any]		    = FlavaConfig.from_pretrained(_lowerCAmelCase				)
      else:
            _lowerCamelCase					: List[Any]		    = FlavaConfig()

      _lowerCamelCase					: Union[str, Any]		    = FlavaForPreTraining(_lowerCAmelCase				).eval()

      _lowerCamelCase					: List[Any]		    = convert_dalle_checkpoint(_lowerCAmelCase					,						_lowerCAmelCase					,						save_checkpoint=_lowerCAmelCase				)

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

      _lowerCamelCase					: Union[str, Any]		    = upgrade_state_dict(_lowerCAmelCase					,						_lowerCAmelCase				)
      hf_model.load_state_dict(_lowerCAmelCase				)
      _lowerCamelCase					: Optional[Any]		    = hf_model.state_dict()
      _lowerCamelCase					: Dict		    = count_parameters(_lowerCAmelCase				)
      _lowerCamelCase					: Tuple		    = count_parameters(_lowerCAmelCase				) + count_parameters(_lowerCAmelCase				)

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

      hf_model.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
 UpperCAmelCase_							: Optional[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')
 UpperCAmelCase_							: Tuple =							parser.parse_args()

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





def    UpperCAmelCase_				( )   ->					list[list[int]]:
      return [list(range(10_00 - i     ,     -10_00 - i     ,     -1				)				) for i in range(10_00				)]


lowerCamelCase__					:  List[Any]      							=				generate_large_matrix()
lowerCamelCase__					:  List[Any]      							=				(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					None:
      assert all(row == sorted(__UpperCAmelCase     ,     reverse=__UpperCAmelCase				) for row in grid				)
      assert all(list(__UpperCAmelCase				) == sorted(__UpperCAmelCase     ,     reverse=__UpperCAmelCase				) for col in zip(*__UpperCAmelCase				)				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[int]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     len(__UpperCAmelCase				) - 1

      # Edge cases such as no values or all numbers are negative.
      if not array or array[0] < 0:
            return 0

      while right + 1 > left:
            SCREAMING_SNAKE_CASE_      		=     (left + right) // 2
            SCREAMING_SNAKE_CASE_      		=     array[mid]

            # Num must be negative and the index must be greater than or equal to 0.
            if num < 0 and array[mid - 1] >= 0:
                  return mid

            if num >= 0:
                  SCREAMING_SNAKE_CASE_      		=     mid + 1
            else:
                  SCREAMING_SNAKE_CASE_      		=     mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
      return len(__UpperCAmelCase				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     len(grid[0]				)

      for i in range(len(__UpperCAmelCase				)				):
            SCREAMING_SNAKE_CASE_      		=     find_negative_index(grid[i][:bound]				)
            total += bound
      return (len(__UpperCAmelCase				) * len(grid[0]				)) - total

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      return len([number for row in grid for number in row if number < 0]				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      for row in grid:
            for i, number in enumerate(__UpperCAmelCase				):
                  if number < 0:
                        total += len(__UpperCAmelCase				) - i
                        break
      return total

def    UpperCAmelCase_				( )   ->					None:
      from timeit import timeit

      print('Running benchmarks'				)
      SCREAMING_SNAKE_CASE_      		=     (
          'from __main__ import count_negatives_binary_search, '
          'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
      )
      for func in (
          "count_negatives_binary_search",  # took 0.7727 seconds
          "count_negatives_brute_force_with_break",  # took 4.6505 seconds
          "count_negatives_brute_force",  # took 12.8160 seconds
      ):
            SCREAMING_SNAKE_CASE_      		=     timeit(f"{func}(grid=grid)"     ,     setup=__UpperCAmelCase     ,     number=5_00				)
            print(f"{func}() took {time:0.4f} seconds"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()