
'''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 				snake_case							(						UpperCAmelCase		)->	List[Any]:







      """simple docstring"""
      # 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 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	Optional[Any]:







      """simple docstring"""
      __A						=	{}

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

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

            __A						=	value.float()

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

      return upgrade





@torch.no_grad()
def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase					,     UpperCAmelCase=None		)->	List[Any]:







      """simple docstring"""
      if config_path is not None:
            __A						=	FlavaConfig.from_pretrained(_lowercase		)
      else:
            __A						=	FlavaConfig()

      __A						=	FlavaForPreTraining(_lowercase		).eval()

      __A						=	convert_dalle_checkpoint(_lowercase					,     _lowercase					,     save_checkpoint=_lowercase		)

      if os.path.exists(_lowercase		):
            __A						=	torch.load(_lowercase					,     map_location='cpu'		)
      else:
            __A						=	torch.hub.load_state_dict_from_url(_lowercase					,     map_location='cpu'		)

      __A						=	upgrade_state_dict(_lowercase					,     _lowercase		)
      hf_model.load_state_dict(_lowercase		)
      __A						=	hf_model.state_dict()
      __A						=	count_parameters(_lowercase		)
      __A						=	count_parameters(_lowercase		) + count_parameters(_lowercase		)

      assert torch.allclose(_lowercase					,     _lowercase					,     atol=1e-3		)

      hf_model.save_pretrained(_lowercase		)


if __name__ == "__main__":
 a__						:      Tuple   				=				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__						:      List[Any]   				=				parser.parse_args()

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







import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





