import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def 							lowerCAmelCase(     __lowerCamelCase						):
					if "cls_token" in name:
										__a		 =       name.replace('cls_token'				,    'vit.embeddings.cls_token'						)
					if "mask_token" in name:
										__a		 =       name.replace('mask_token'				,    'decoder.mask_token'						)
					if "decoder_pos_embed" in name:
										__a		 =       name.replace('decoder_pos_embed'				,    'decoder.decoder_pos_embed'						)
					if "pos_embed" in name and "decoder" not in name:
										__a		 =       name.replace('pos_embed'				,    'vit.embeddings.position_embeddings'						)
					if "patch_embed.proj" in name:
										__a		 =       name.replace('patch_embed.proj'				,    'vit.embeddings.patch_embeddings.projection'						)
					if "patch_embed.norm" in name:
										__a		 =       name.replace('patch_embed.norm'				,    'vit.embeddings.norm'						)
					if "decoder_blocks" in name:
										__a		 =       name.replace('decoder_blocks'				,    'decoder.decoder_layers'						)
					if "blocks" in name:
										__a		 =       name.replace('blocks'				,    'vit.encoder.layer'						)
					if "attn.proj" in name:
										__a		 =       name.replace('attn.proj'				,    'attention.output.dense'						)
					if "attn" in name:
										__a		 =       name.replace('attn'				,    'attention.self'						)
					if "norm1" in name:
										__a		 =       name.replace('norm1'				,    'layernorm_before'						)
					if "norm2" in name:
										__a		 =       name.replace('norm2'				,    'layernorm_after'						)
					if "mlp.fc1" in name:
										__a		 =       name.replace('mlp.fc1'				,    'intermediate.dense'						)
					if "mlp.fc2" in name:
										__a		 =       name.replace('mlp.fc2'				,    'output.dense'						)
					if "decoder_embed" in name:
										__a		 =       name.replace('decoder_embed'				,    'decoder.decoder_embed'						)
					if "decoder_norm" in name:
										__a		 =       name.replace('decoder_norm'				,    'decoder.decoder_norm'						)
					if "decoder_pred" in name:
										__a		 =       name.replace('decoder_pred'				,    'decoder.decoder_pred'						)
					if "norm.weight" in name and "decoder" not in name:
										__a		 =       name.replace('norm.weight'				,    'vit.layernorm.weight'						)
					if "norm.bias" in name and "decoder" not in name:
										__a		 =       name.replace('norm.bias'				,    'vit.layernorm.bias'						)

					return name
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					for key in orig_state_dict.copy().keys():
										__a		 =       orig_state_dict.pop(__lowerCamelCase						)

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

										else:
															__a		 =       val

					return orig_state_dict
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       ViTMAEConfig()
					if "large" in checkpoint_url:
										__a		 =       1024
										__a		 =       4096
										__a		 =       24
										__a		 =       16
					elif "huge" in checkpoint_url:
										__a		 =       14
										__a		 =       1280
										__a		 =       5120
										__a		 =       32
										__a		 =       16

					__a		 =       ViTMAEForPreTraining(__lowerCamelCase						)

					__a		 =       torch.hub.load_state_dict_from_url(__lowerCamelCase				,    map_location='cpu'						)['model']

					__a		 =       ViTMAEImageProcessor(size=config.image_size						)

					__a		 =       convert_state_dict(__lowerCamelCase				,    __lowerCamelCase						)

					model.load_state_dict(__lowerCamelCase						)
					model.eval()

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

					__a		 =       Image.open(requests.get(__lowerCamelCase				,    stream=__lowerCamelCase						).raw						)
					__a		 =       ViTMAEImageProcessor(size=config.image_size						)
					__a		 =       image_processor(images=__lowerCamelCase				,    return_tensors='pt'						)

					# forward pass
					torch.manual_seed(2						)
					__a		 =       model(**__lowerCamelCase						)
					__a		 =       outputs.logits

					if "large" in checkpoint_url:
										__a		 =       torch.tensor(
										    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]						)
					elif "huge" in checkpoint_url:
										__a		 =       torch.tensor(
										    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]						)
					else:
										__a		 =       torch.tensor(
										    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]						)

					# 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__":
	lowerCamelCase_    :		Any							=	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."""
	)

	lowerCamelCase_    :		Tuple							=	parser.parse_args()
	convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Any:
								warnings.warn(
								    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
								    'instead.'				,						UpperCAmelCase				,						)
								super().__init__(args=UpperCAmelCase				,						**UpperCAmelCase   )




