


'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

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

						return name

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						for key in orig_state_dict.copy().keys():
												__lowerCAmelCase							       =				orig_state_dict.pop(lowerCamelCase)

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

												else:
																		__lowerCAmelCase							       =				val

						return orig_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				ViTMAEConfig()
						if "large" in checkpoint_url:
												__lowerCAmelCase							       =				1_0_2_4
												__lowerCAmelCase							       =				4_0_9_6
												__lowerCAmelCase							       =				2_4
												__lowerCAmelCase							       =				1_6
						elif "huge" in checkpoint_url:
												__lowerCAmelCase							       =				1_4
												__lowerCAmelCase							       =				1_2_8_0
												__lowerCAmelCase							       =				5_1_2_0
												__lowerCAmelCase							       =				3_2
												__lowerCAmelCase							       =				1_6

						__lowerCAmelCase							       =				ViTMAEForPreTraining(lowerCamelCase)

						__lowerCAmelCase							       =				torch.hub.load_state_dict_from_url(lowerCamelCase,				map_location='''cpu''')['''model''']

						__lowerCAmelCase							       =				ViTMAEImageProcessor(size=config.image_size)

						__lowerCAmelCase							       =				convert_state_dict(lowerCamelCase,				lowerCamelCase)

						model.load_state_dict(lowerCamelCase)
						model.eval()

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

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

						# forward pass
						torch.manual_seed(2)
						__lowerCAmelCase							       =				model(**lowerCamelCase)
						__lowerCAmelCase							       =				outputs.logits

						if "large" in checkpoint_url:
												__lowerCAmelCase							       =				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:
												__lowerCAmelCase							       =				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:
												__lowerCAmelCase							       =				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__":
	_UpperCAmelCase				: Tuple							    =				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."""
	)

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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

