

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 						UpperCamelCase					(lowercase_: Optional[int]					)     ->      List[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 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: Tuple					)     ->      Any:
	A__							:    Any        =		{}

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

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

		A__							:    List[str]        =		value.float()

	for key, value in codebook_state_dict.items():
		A__							:    List[str]        =		value

	return upgrade

@torch.no_grad()
def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: List[Any]    ,      lowercase_: Optional[Any]    ,      lowercase_: Union[str, Any]=None					)     ->      Optional[int]:
	if config_path is not None:
		A__							:    List[str]        =		FlavaConfig.from_pretrained(lowercase_					)
	else:
		A__							:    Union[str, Any]        =		FlavaConfig()

	A__							:    str        =		FlavaForPreTraining(lowercase_					).eval()

	A__							:    List[Any]        =		convert_dalle_checkpoint(lowercase_    ,      lowercase_    ,      save_checkpoint=lowercase_					)

	if os.path.exists(lowercase_					):
		A__							:    List[str]        =		torch.load(lowercase_    ,      map_location="""cpu"""					)
	else:
		A__							:    Optional[Any]        =		torch.hub.load_state_dict_from_url(lowercase_    ,      map_location="""cpu"""					)

	A__							:    Dict        =		upgrade_state_dict(lowercase_    ,      lowercase_					)
	hf_model.load_state_dict(lowercase_					)
	A__							:    Tuple        =		hf_model.state_dict()
	A__							:    int        =		count_parameters(lowercase_					)
	A__							:    Optional[Any]        =		count_parameters(lowercase_					) + count_parameters(lowercase_					)

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

	hf_model.save_pretrained(lowercase_					)


if __name__ == "__main__":
	A_       : int         =			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_       : Union[str, Any]         =			parser.parse_args()

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






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


A_       : int         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   List[Any]  							=       '''mgp-str'''






					def __init__( self    ,       A__=[32, 128]    ,       A__=4    ,       A__=3    ,       A__=27    ,       A__=38    ,       A__=5_0257    ,       A__=3_0522    ,       A__=768    ,       A__=12    ,       A__=12    ,       A__=4.0    ,       A__=True    ,       A__=False    ,       A__=1e-5    ,       A__=0.0    ,       A__=0.0    ,       A__=0.0    ,       A__=False    ,       A__=0.0_2    ,       **A__    ,       ):
						super().__init__(**A__			)

						A__							:    Dict        =		image_size
						A__							:    int        =		patch_size
						A__							:    Dict        =		num_channels
						A__							:    List[Any]        =		max_token_length
						A__							:    str        =		num_character_labels
						A__							:    Tuple        =		num_bpe_labels
						A__							:    Optional[Any]        =		num_wordpiece_labels
						A__							:    Optional[int]        =		hidden_size
						A__							:    Tuple        =		num_hidden_layers
						A__							:    Any        =		num_attention_heads
						A__							:    List[Any]        =		mlp_ratio
						A__							:    Tuple        =		distilled
						A__							:    Union[str, Any]        =		layer_norm_eps
						A__							:    Tuple        =		drop_rate
						A__							:    List[str]        =		qkv_bias
						A__							:    Optional[Any]        =		attn_drop_rate
						A__							:    Union[str, Any]        =		drop_path_rate
						A__							:    Optional[Any]        =		output_aa_attentions
						A__							:    Optional[int]        =		initializer_range



