



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__   ) ->      Tuple:
						# 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__						,       lowercase__   ) ->      List[Any]:
						__lowercase					=				{}

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

												__lowercase					=				key.replace("""heads.cmd.mim_head.cls.predictions"""						,       """mmm_image_head"""   )
												__lowercase					=				key.replace("""heads.cmd.mlm_head.cls.predictions"""						,       """mmm_text_head"""   )
												__lowercase					=				key.replace("""heads.cmd.itm_head.cls"""						,       """itm_head"""   )
												__lowercase					=				key.replace("""heads.cmd.itm_head.pooler"""						,       """itm_head.pooler"""   )
												__lowercase					=				key.replace("""heads.cmd.clip_head.logit_scale"""						,       """flava.logit_scale"""   )
												__lowercase					=				key.replace("""heads.fairseq_mlm.cls.predictions"""						,       """mlm_head"""   )
												__lowercase					=				key.replace("""heads.imagenet.mim_head.cls.predictions"""						,       """mim_head"""   )
												__lowercase					=				key.replace("""mm_text_projection"""						,       """flava.text_to_mm_projection"""   )
												__lowercase					=				key.replace("""mm_image_projection"""						,       """flava.image_to_mm_projection"""   )
												__lowercase					=				key.replace("""image_encoder.module"""						,       """flava.image_model"""   )
												__lowercase					=				key.replace("""text_encoder.module"""						,       """flava.text_model"""   )
												__lowercase					=				key.replace("""mm_encoder.module.encoder.cls_token"""						,       """flava.multimodal_model.cls_token"""   )
												__lowercase					=				key.replace("""mm_encoder.module"""						,       """flava.multimodal_model"""   )
												__lowercase					=				key.replace("""text_projection"""						,       """flava.text_projection"""   )
												__lowercase					=				key.replace("""image_projection"""						,       """flava.image_projection"""   )

												__lowercase					=				value.float()

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

						return upgrade






@torch.no_grad()
def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__=None   ) ->      Optional[Any]:
						if config_path is not None:
												__lowercase					=				FlavaConfig.from_pretrained(lowercase__   )
						else:
												__lowercase					=				FlavaConfig()

						__lowercase					=				FlavaForPreTraining(lowercase__   ).eval()

						__lowercase					=				convert_dalle_checkpoint(lowercase__						,       lowercase__						,       save_checkpoint=lowercase__   )

						if os.path.exists(lowercase__   ):
												__lowercase					=				torch.load(lowercase__						,       map_location="""cpu"""   )
						else:
												__lowercase					=				torch.hub.load_state_dict_from_url(lowercase__						,       map_location="""cpu"""   )

						__lowercase					=				upgrade_state_dict(lowercase__						,       lowercase__   )
						hf_model.load_state_dict(lowercase__   )
						__lowercase					=				hf_model.state_dict()
						__lowercase					=				count_parameters(lowercase__   )
						__lowercase					=				count_parameters(lowercase__   ) + count_parameters(lowercase__   )

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

						hf_model.save_pretrained(lowercase__   )


if __name__ == "__main__":
			UpperCamelCase__ =						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__ =						parser.parse_args()

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










from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__ =						logging.get_logger(__name__)

UpperCamelCase__ =						{
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			"""yolos"""





			def __init__(				self	:       Optional[int]			,     lowercase	:       Any=768			,     lowercase	:       Tuple=12			,     lowercase	:       Tuple=12			,     lowercase	:       str=3_072			,     lowercase	:       Optional[Any]="gelu"			,     lowercase	:       Union[str, Any]=0.0			,     lowercase	:       Dict=0.0			,     lowercase	:       Optional[int]=0.02			,     lowercase	:       Optional[Any]=1E-1_2			,     lowercase	:       Tuple=[512, 864]			,     lowercase	:       Optional[int]=16			,     lowercase	:       Dict=3			,     lowercase	:       Optional[Any]=True			,     lowercase	:       Optional[int]=100			,     lowercase	:       Optional[int]=True			,     lowercase	:       Any=False			,     lowercase	:       Any=1			,     lowercase	:       Any=5			,     lowercase	:       List[str]=2			,     lowercase	:       Union[str, Any]=5			,     lowercase	:       str=2			,     lowercase	:       Tuple=0.1			,     **lowercase	:       str			,     )						->					Optional[Any]:
									"""simple docstring"""







									super().__init__(**lowercase   )

									__lowercase					=				hidden_size
									__lowercase					=				num_hidden_layers
									__lowercase					=				num_attention_heads
									__lowercase					=				intermediate_size
									__lowercase					=				hidden_act
									__lowercase					=				hidden_dropout_prob
									__lowercase					=				attention_probs_dropout_prob
									__lowercase					=				initializer_range
									__lowercase					=				layer_norm_eps
									__lowercase					=				image_size
									__lowercase					=				patch_size
									__lowercase					=				num_channels
									__lowercase					=				qkv_bias
									__lowercase					=				num_detection_tokens
									__lowercase					=				use_mid_position_embeddings
									__lowercase					=				auxiliary_loss
									# Hungarian matcher
									__lowercase					=				class_cost
									__lowercase					=				bbox_cost
									__lowercase					=				giou_cost
									# Loss coefficients
									__lowercase					=				bbox_loss_coefficient
									__lowercase					=				giou_loss_coefficient
									__lowercase					=				eos_coefficient





class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Dict      =			version.parse("""1.11"""			)





			@property
			def 		snake_case__			(				self	:       List[str]   )						->					Mapping[str, Mapping[int, str]]:
									"""simple docstring"""







									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]   )





			@property
			def 		snake_case__			(				self	:       int   )						->					float:
									"""simple docstring"""







									return 1E-4







			@property
			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									return 12





