






import copy
import os
from typing import Union

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


A        =			logging.get_logger(__name__)

A        =			{
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Tuple				    =						"""bridgetower_vision_model"""


		def __init__(						self ,    UpperCamelCase__=768 ,    UpperCamelCase__=12 ,    UpperCamelCase__=3 ,    UpperCamelCase__=16 ,    UpperCamelCase__=288 ,    UpperCamelCase__=1 ,    UpperCamelCase__=1E-05 ,    UpperCamelCase__=False ,    UpperCamelCase__=True ,    UpperCamelCase__=False ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	List[str]													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	int													=   num_channels
						SCREAMING_SNAKE_CASE_   :	List[str]													=   patch_size
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_size
						SCREAMING_SNAKE_CASE_   :	Dict													=   initializer_factor
						SCREAMING_SNAKE_CASE_   :	Tuple													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	Any													=   stop_gradient
						SCREAMING_SNAKE_CASE_   :	int													=   share_layernorm
						SCREAMING_SNAKE_CASE_   :	str													=   remove_last_layer






		@classmethod
		def   __snake_case			(						cls ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   cls.get_config_dict(UpperCamelCase__ ,    **UpperCamelCase__       )

						if config_dict.get('model_type'       ) == "bridgetower":
										SCREAMING_SNAKE_CASE_   :	Dict													=   config_dict['text_config']

						if "model_type" in config_dict and hasattr(cls ,    'model_type'       ) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
										    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

						return cls.from_dict(UpperCamelCase__ ,    **UpperCamelCase__       )


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Any]				    =						"""bridgetower_text_model"""


		def __init__(						self ,    UpperCamelCase__=50265 ,    UpperCamelCase__=768 ,    UpperCamelCase__=12 ,    UpperCamelCase__=12 ,    UpperCamelCase__=1 ,    UpperCamelCase__=3072 ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=514 ,    UpperCamelCase__=1 ,    UpperCamelCase__=1E-05 ,    UpperCamelCase__=1 ,    UpperCamelCase__=0 ,    UpperCamelCase__=2 ,    UpperCamelCase__="absolute" ,    UpperCamelCase__=True ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   vocab_size
						SCREAMING_SNAKE_CASE_   :	str													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	str													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	Dict													=   num_attention_heads
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	int													=   initializer_factor
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   intermediate_size
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE_   :	List[str]													=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_   :	str													=   max_position_embeddings
						SCREAMING_SNAKE_CASE_   :	int													=   type_vocab_size
						SCREAMING_SNAKE_CASE_   :	int													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   position_embedding_type
						SCREAMING_SNAKE_CASE_   :	Tuple													=   use_cache
						SCREAMING_SNAKE_CASE_   :	Tuple													=   pad_token_id
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   bos_token_id
						SCREAMING_SNAKE_CASE_   :	str													=   eos_token_id






		@classmethod
		def   __snake_case			(						cls ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   cls.get_config_dict(UpperCamelCase__ ,    **UpperCamelCase__       )

						if config_dict.get('model_type'       ) == "bridgetower":
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   config_dict['text_config']

						if "model_type" in config_dict and hasattr(cls ,    'model_type'       ) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
										    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

						return cls.from_dict(UpperCamelCase__ ,    **UpperCamelCase__       )


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						"""bridgetower"""


		def __init__(						self ,    UpperCamelCase__=True ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=768 ,    UpperCamelCase__=1 ,    UpperCamelCase__=1E-05 ,    UpperCamelCase__=False ,    UpperCamelCase__="add" ,    UpperCamelCase__=12 ,    UpperCamelCase__=6 ,    UpperCamelCase__=False ,    UpperCamelCase__=False ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__ ,    ):
						# TODO: remove this once the Hub files are updated.
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   kwargs.pop('text_config_dict' ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   kwargs.pop('vision_config_dict' ,    UpperCamelCase__       )

						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   share_cross_modal_transformer_layers
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	Any													=   initializer_factor
						SCREAMING_SNAKE_CASE_   :	Dict													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	int													=   share_link_tower_layers
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   link_tower_type
						SCREAMING_SNAKE_CASE_   :	Dict													=   num_attention_heads
						SCREAMING_SNAKE_CASE_   :	List[str]													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	str													=   tie_word_embeddings
						SCREAMING_SNAKE_CASE_   :	int													=   init_layernorm_from_vision_encoder

						if text_config is None:
										SCREAMING_SNAKE_CASE_   :	int													=   {}
										logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'       )

						if vision_config is None:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   {}
										logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   BridgeTowerTextConfig(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   BridgeTowerVisionConfig(**UpperCamelCase__       )


		@classmethod
		def   __snake_case			(						cls ,    UpperCamelCase__ ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
						return cls(text_config=text_config.to_dict() ,    vision_config=vision_config.to_dict() ,    **UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   copy.deepcopy(self.__dict__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.text_config.to_dict()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.vision_config.to_dict()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.__class__.model_type
						return output







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if "cls_token" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   name.replace('cls_token'    ,    'vit.embeddings.cls_token'      )
				if "mask_token" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   name.replace('mask_token'    ,    'decoder.mask_token'      )
				if "decoder_pos_embed" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('decoder_pos_embed'    ,    'decoder.decoder_pos_embed'      )
				if "pos_embed" in name and "decoder" not in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('pos_embed'    ,    'vit.embeddings.position_embeddings'      )
				if "patch_embed.proj" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('patch_embed.proj'    ,    'vit.embeddings.patch_embeddings.projection'      )
				if "patch_embed.norm" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('patch_embed.norm'    ,    'vit.embeddings.norm'      )
				if "decoder_blocks" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('decoder_blocks'    ,    'decoder.decoder_layers'      )
				if "blocks" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('blocks'    ,    'vit.encoder.layer'      )
				if "attn.proj" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('attn.proj'    ,    'attention.output.dense'      )
				if "attn" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('attn'    ,    'attention.self'      )
				if "norm1" in name:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   name.replace('norm1'    ,    'layernorm_before'      )
				if "norm2" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('norm2'    ,    'layernorm_after'      )
				if "mlp.fc1" in name:
								SCREAMING_SNAKE_CASE_   :	Dict													=   name.replace('mlp.fc1'    ,    'intermediate.dense'      )
				if "mlp.fc2" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('mlp.fc2'    ,    'output.dense'      )
				if "decoder_embed" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('decoder_embed'    ,    'decoder.decoder_embed'      )
				if "decoder_norm" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('decoder_norm'    ,    'decoder.decoder_norm'      )
				if "decoder_pred" in name:
								SCREAMING_SNAKE_CASE_   :	str													=   name.replace('decoder_pred'    ,    'decoder.decoder_pred'      )
				if "norm.weight" in name and "decoder" not in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('norm.weight'    ,    'vit.layernorm.weight'      )
				if "norm.bias" in name and "decoder" not in name:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   name.replace('norm.bias'    ,    'vit.layernorm.bias'      )

				return name



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE_   :	Tuple													=   orig_state_dict.pop(lowerCAmelCase__      )

								if "qkv" in key:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   key.split('.'      )
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   int(key_split[1]      )
												if "decoder_blocks" in key:
																SCREAMING_SNAKE_CASE_   :	Any													=   config.decoder_hidden_size
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'decoder.decoder_layers.'
																if "weight" in key:
																				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   val[:dim, :]
																				SCREAMING_SNAKE_CASE_   :	List[str]													=   val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE_   :	Any													=   val[-dim:, :]
																elif "bias" in key:
																				SCREAMING_SNAKE_CASE_   :	str													=   val[:dim]
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[-dim:]
												else:
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   config.hidden_size
																SCREAMING_SNAKE_CASE_   :	str													=   'vit.encoder.layer.'
																if "weight" in key:
																				SCREAMING_SNAKE_CASE_   :	Any													=   val[:dim, :]
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE_   :	int													=   val[-dim:, :]
																elif "bias" in key:
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[:dim]
																				SCREAMING_SNAKE_CASE_   :	Dict													=   val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE_   :	str													=   val[-dim:]

								else:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   val

				return orig_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   ViTMAEConfig()
				if "large" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Any													=   1024
								SCREAMING_SNAKE_CASE_   :	Any													=   4096
								SCREAMING_SNAKE_CASE_   :	List[str]													=   24
								SCREAMING_SNAKE_CASE_   :	List[str]													=   16
				elif "huge" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	str													=   14
								SCREAMING_SNAKE_CASE_   :	List[str]													=   1280
								SCREAMING_SNAKE_CASE_   :	int													=   5120
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   32
								SCREAMING_SNAKE_CASE_   :	List[str]													=   16

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ViTMAEForPreTraining(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.hub.load_state_dict_from_url(lowerCAmelCase__    ,    map_location='cpu'      )['model']

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ViTMAEImageProcessor(size=config.image_size      )

				SCREAMING_SNAKE_CASE_   :	int													=   convert_state_dict(lowerCAmelCase__    ,    lowerCAmelCase__      )

				model.load_state_dict(lowerCAmelCase__      )
				model.eval()

				SCREAMING_SNAKE_CASE_   :	Any													=   'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

				SCREAMING_SNAKE_CASE_   :	Dict													=   Image.open(requests.get(lowerCAmelCase__    ,    stream=lowerCAmelCase__      ).raw      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ViTMAEImageProcessor(size=config.image_size      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_processor(images=lowerCAmelCase__    ,    return_tensors='pt'      )

				# forward pass
				torch.manual_seed(2      )
				SCREAMING_SNAKE_CASE_   :	Any													=   model(**lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   outputs.logits

				if "large" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor(
								    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]      )
				elif "huge" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor(
								    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]      )
				else:
								SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor(
								    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]      )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]    ,    lowerCAmelCase__    ,    atol=1E-4      )

				print(F'''Saving model to {pytorch_dump_folder_path}'''      )
				model.save_pretrained(lowerCAmelCase__      )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
				image_processor.save_pretrained(lowerCAmelCase__      )


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
				    type=str,
				    help='URL of the checkpoint you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				A        =			parser.parse_args()
				convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)