
"""simple docstring"""




from typing import List

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


UpperCAmelCase__      :    Any    							= logging.get_logger(__name__)

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



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						List[str]					       =   '''efficientformer'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__ = [3, 2, 6, 4]   ,       SCREAMING_SNAKE_CASE__ = [48, 96, 2_24, 4_48]   ,       SCREAMING_SNAKE_CASE__ = [True, True, True, True]   ,       SCREAMING_SNAKE_CASE__ = 4_48   ,       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__ = 0.02   ,       SCREAMING_SNAKE_CASE__ = 1E-12   ,       SCREAMING_SNAKE_CASE__ = 2_24   ,       SCREAMING_SNAKE_CASE__ = 1E-05   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		None:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

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




