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	(    _lowercase						):
						# 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       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							{}

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

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							value.float()

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

						return upgrade
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase=None						):
						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							FlavaConfig.from_pretrained(_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    str							=							FlavaConfig()

						SCREAMING_SNAKE_CASE							:    str							=							FlavaForPreTraining(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    Dict							=							convert_dalle_checkpoint(_lowercase		,  _lowercase		,  save_checkpoint=_lowercase						)

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

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

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

						hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					__UpperCamelCase		:       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')
					__UpperCamelCase		:       List[Any]									=     parser.parse_args()

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






def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float(moles / volume						) * nfactor						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (volume)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (pressure)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((pressure * volume) / (0.0821 * moles)						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





