



'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
				# 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       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= {}

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

								SCREAMING_SNAKE_CASE  :						Any      					= key.replace('''heads.cmd.mim_head.cls.predictions'''    ,	'''mmm_image_head'''       )
								SCREAMING_SNAKE_CASE  :						str      					= key.replace('''heads.cmd.mlm_head.cls.predictions'''    ,	'''mmm_text_head'''       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= key.replace('''heads.cmd.itm_head.cls'''    ,	'''itm_head'''       )
								SCREAMING_SNAKE_CASE  :						Dict      					= key.replace('''heads.cmd.itm_head.pooler'''    ,	'''itm_head.pooler'''       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= key.replace('''heads.cmd.clip_head.logit_scale'''    ,	'''flava.logit_scale'''       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= key.replace('''heads.fairseq_mlm.cls.predictions'''    ,	'''mlm_head'''       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= key.replace('''heads.imagenet.mim_head.cls.predictions'''    ,	'''mim_head'''       )
								SCREAMING_SNAKE_CASE  :						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  :						Dict      					= key.replace('''text_encoder.module'''    ,	'''flava.text_model'''       )
								SCREAMING_SNAKE_CASE  :						Union[str, 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  :						Dict      					= key.replace('''text_projection'''    ,	'''flava.text_projection'''       )
								SCREAMING_SNAKE_CASE  :						Optional[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  :						List[Any]      					= value

				return upgrade






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :List[Any]=None       ):
				if config_path is not None:
								SCREAMING_SNAKE_CASE  :						List[str]      					= FlavaConfig.from_pretrained(snake_case_       )
				else:
								SCREAMING_SNAKE_CASE  :						List[str]      					= FlavaConfig()

				SCREAMING_SNAKE_CASE  :						Dict      					= FlavaForPreTraining(snake_case_       ).eval()

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= convert_dalle_checkpoint(snake_case_    ,	snake_case_    ,	save_checkpoint=snake_case_       )

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

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= upgrade_state_dict(snake_case_    ,	snake_case_       )
				hf_model.load_state_dict(snake_case_       )
				SCREAMING_SNAKE_CASE  :						Dict      					= hf_model.state_dict()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= count_parameters(snake_case_       )
				SCREAMING_SNAKE_CASE  :						str      					= 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__":
					snake_case_			=     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""")
					snake_case_			=     parser.parse_args()

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









'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class 	a__     (				_lowercase	):
	__magic_name__		:    List[Any]	    =     "markuplm"


	def __init__(self : List[Any],					__UpperCAmelCase : int=30522,					__UpperCAmelCase : Tuple=768,					__UpperCAmelCase : str=12,					__UpperCAmelCase : List[Any]=12,					__UpperCAmelCase : Tuple=3072,					__UpperCAmelCase : Union[str, Any]="gelu",					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Union[str, Any]=512,					__UpperCAmelCase : Dict=2,					__UpperCAmelCase : int=0.02,					__UpperCAmelCase : Optional[Any]=1e-12,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : Optional[Any]=0,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : int=256,					__UpperCAmelCase : Tuple=1024,					__UpperCAmelCase : Optional[Any]=216,					__UpperCAmelCase : str=1001,					__UpperCAmelCase : Optional[Any]=32,					__UpperCAmelCase : Dict=50,					__UpperCAmelCase : Union[str, Any]="absolute",					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : List[str]=None,					**__UpperCAmelCase : Union[str, Any],					)     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						Dict      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Tuple      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= type_vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						int      					= position_embedding_type
					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_cache
					SCREAMING_SNAKE_CASE  :						int      					= classifier_dropout
					# additional properties
					SCREAMING_SNAKE_CASE  :						List[Any]      					= max_depth
					SCREAMING_SNAKE_CASE  :						Any      					= max_xpath_tag_unit_embeddings
					SCREAMING_SNAKE_CASE  :						int      					= max_xpath_subs_unit_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= tag_pad_id
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= subs_pad_id
					SCREAMING_SNAKE_CASE  :						int      					= xpath_unit_hidden_size




