



from typing import List

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


a	:   Any       			=       logging.get_logger(__name__)

a	:   str       			=       {
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}







class 							_a							(					a__ ):
			A							=     '''efficientformer'''

			def __init__(self,					SCREAMING_SNAKE_CASE_ = [3, 2, 6, 4],					SCREAMING_SNAKE_CASE_ = [48, 96, 224, 448],					SCREAMING_SNAKE_CASE_ = [True, True, True, True],					SCREAMING_SNAKE_CASE_ = 448,					SCREAMING_SNAKE_CASE_ = 32,					SCREAMING_SNAKE_CASE_ = 4,					SCREAMING_SNAKE_CASE_ = 7,					SCREAMING_SNAKE_CASE_ = 5,					SCREAMING_SNAKE_CASE_ = 8,					SCREAMING_SNAKE_CASE_ = 4,					SCREAMING_SNAKE_CASE_ = 0.0,					SCREAMING_SNAKE_CASE_ = 16,					SCREAMING_SNAKE_CASE_ = 3,					SCREAMING_SNAKE_CASE_ = 3,					SCREAMING_SNAKE_CASE_ = 3,					SCREAMING_SNAKE_CASE_ = 2,					SCREAMING_SNAKE_CASE_ = 1,					SCREAMING_SNAKE_CASE_ = 0.0,					SCREAMING_SNAKE_CASE_ = 1,					SCREAMING_SNAKE_CASE_ = True,					SCREAMING_SNAKE_CASE_ = True,					SCREAMING_SNAKE_CASE_ = 1E-5,					SCREAMING_SNAKE_CASE_ = "gelu",					SCREAMING_SNAKE_CASE_ = 0.0_2,					SCREAMING_SNAKE_CASE_ = 1E-12,					SCREAMING_SNAKE_CASE_ = 224,					SCREAMING_SNAKE_CASE_ = 1E-05,					**SCREAMING_SNAKE_CASE_,					)    ->					None:
						super().__init__(**SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_:						Union[str, Any]					=			hidden_act
						UpperCAmelCase_:						Tuple					=			hidden_dropout_prob
						UpperCAmelCase_:						Tuple					=			hidden_sizes
						UpperCAmelCase_:						Tuple					=			num_hidden_layers
						UpperCAmelCase_:						Any					=			num_attention_heads
						UpperCAmelCase_:						Dict					=			initializer_range
						UpperCAmelCase_:						int					=			layer_norm_eps
						UpperCAmelCase_:						Dict					=			patch_size
						UpperCAmelCase_:						Tuple					=			num_channels
						UpperCAmelCase_:						Any					=			depths
						UpperCAmelCase_:						Dict					=			mlp_expansion_ratio
						UpperCAmelCase_:						int					=			downsamples
						UpperCAmelCase_:						Optional[int]					=			dim
						UpperCAmelCase_:						Tuple					=			key_dim
						UpperCAmelCase_:						Tuple					=			attention_ratio
						UpperCAmelCase_:						Union[str, Any]					=			resolution
						UpperCAmelCase_:						List[str]					=			pool_size
						UpperCAmelCase_:						Union[str, Any]					=			downsample_patch_size
						UpperCAmelCase_:						Union[str, Any]					=			downsample_stride
						UpperCAmelCase_:						int					=			downsample_pad
						UpperCAmelCase_:						List[str]					=			drop_path_rate
						UpperCAmelCase_:						Any					=			num_metaad_blocks
						UpperCAmelCase_:						List[Any]					=			distillation
						UpperCAmelCase_:						Tuple					=			use_layer_scale
						UpperCAmelCase_:						List[Any]					=			layer_scale_init_value
						UpperCAmelCase_:						List[Any]					=			image_size
						UpperCAmelCase_:						str					=			batch_norm_eps








"""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_  (   _snake_case						):
							# 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_  (   _snake_case   ,_snake_case						):
							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__						:       Optional[int]           =   key.replace("""heads.cmd.mim_head.cls.predictions"""   ,"""mmm_image_head"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""heads.cmd.mlm_head.cls.predictions"""   ,"""mmm_text_head"""						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   key.replace("""heads.cmd.itm_head.cls"""   ,"""itm_head"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""heads.cmd.itm_head.pooler"""   ,"""itm_head.pooler"""						)
														SCREAMING_SNAKE_CASE__						:       int           =   key.replace("""heads.cmd.clip_head.logit_scale"""   ,"""flava.logit_scale"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""heads.fairseq_mlm.cls.predictions"""   ,"""mlm_head"""						)
														SCREAMING_SNAKE_CASE__						:       str           =   key.replace("""heads.imagenet.mim_head.cls.predictions"""   ,"""mim_head"""						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   key.replace("""mm_text_projection"""   ,"""flava.text_to_mm_projection"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""mm_image_projection"""   ,"""flava.image_to_mm_projection"""						)
														SCREAMING_SNAKE_CASE__						:       str           =   key.replace("""image_encoder.module"""   ,"""flava.image_model"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""text_encoder.module"""   ,"""flava.text_model"""						)
														SCREAMING_SNAKE_CASE__						:       int           =   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__						:       Any           =   key.replace("""text_projection"""   ,"""flava.text_projection"""						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   key.replace("""image_projection"""   ,"""flava.image_projection"""						)

														SCREAMING_SNAKE_CASE__						:       Tuple           =   value.float()

							for key, value in codebook_state_dict.items():
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   value

							return upgrade

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=None						):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   FlavaConfig.from_pretrained(_snake_case						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   FlavaConfig()

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FlavaForPreTraining(_snake_case						).eval()

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   convert_dalle_checkpoint(_snake_case   ,_snake_case   ,save_checkpoint=_snake_case						)

							if os.path.exists(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.load(_snake_case   ,map_location="""cpu"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.hub.load_state_dict_from_url(_snake_case   ,map_location="""cpu"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   upgrade_state_dict(_snake_case   ,_snake_case						)
							hf_model.load_state_dict(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Any           =   hf_model.state_dict()
							SCREAMING_SNAKE_CASE__						:       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__":
					UpperCAmelCase__      :    List[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__      :    Optional[int]    							= parser.parse_args()

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




