






import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu


class 							__a							(  nn.Module						):





		'''simple docstring'''



		def __init__(						self       ):
						super().__init__()
						SCREAMING_SNAKE_CASE_   :	int													=   nn.Linear(3 ,    4       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   nn.BatchNormad(4       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   nn.Linear(4 ,    5       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__       )       )       )


class 							__a							(  __A						):





		'''simple docstring'''



		def   __snake_case			(						self ,    UpperCamelCase__ ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						return (args[0] + 1,) + args[1:], kwargs


class 							__a							(  __A						):





		'''simple docstring'''



		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						return output + 1


class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   ModelForTest()
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ModelHook()

						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						self.assertEqual(test_model._hf_hook ,    UpperCamelCase__       )
						self.assertTrue(hasattr(UpperCamelCase__ ,    '_old_forward'       )       )

						# Check adding the hook did not change the name or the signature
						self.assertEqual(test_model.forward.__name__ ,    'forward'       )
						self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       ) ,    ['x']       )

						remove_hook_from_module(UpperCamelCase__       )
						self.assertFalse(hasattr(UpperCamelCase__ ,    '_hf_hook'       )       )
						self.assertFalse(hasattr(UpperCamelCase__ ,    '_old_forward'       )       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ModelForTest()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ModelHook()

						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__ ,    append=UpperCamelCase__       )

						self.assertEqual(isinstance(test_model._hf_hook ,    UpperCamelCase__       ) ,    UpperCamelCase__       )
						self.assertEqual(len(test_model._hf_hook.hooks       ) ,    2       )
						self.assertTrue(hasattr(UpperCamelCase__ ,    '_old_forward'       )       )

						# Check adding the hook did not change the name or the signature
						self.assertEqual(test_model.forward.__name__ ,    'forward'       )
						self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       ) ,    ['x']       )

						remove_hook_from_module(UpperCamelCase__       )
						self.assertFalse(hasattr(UpperCamelCase__ ,    '_hf_hook'       )       )
						self.assertFalse(hasattr(UpperCamelCase__ ,    '_old_forward'       )       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   ModelForTest()
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   test_model(x + 1       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   test_model(x + 2       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   PreForwardHook()
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   test_model(UpperCamelCase__       )
						self.assertTrue(torch.allclose(UpperCamelCase__ ,    UpperCamelCase__ ,    atol=1E-5       )       )

						# Attaching a hook to a model when it already has one replaces, does not chain
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   PreForwardHook()
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   test_model(UpperCamelCase__       )
						self.assertTrue(torch.allclose(UpperCamelCase__ ,    UpperCamelCase__ ,    atol=1E-5       )       )

						# You need to use the sequential hook to chain two or more hooks
						SCREAMING_SNAKE_CASE_   :	Any													=   SequentialHook(PreForwardHook() ,    PreForwardHook()       )
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   test_model(UpperCamelCase__       )
						assert torch.allclose(UpperCamelCase__ ,    UpperCamelCase__ ,    atol=1E-5       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   ModelForTest()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   test_model(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   PostForwardHook()
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   test_model(UpperCamelCase__       )
						self.assertTrue(torch.allclose(UpperCamelCase__ ,    output + 1 ,    atol=1E-5       )       )

						# Attaching a hook to a model when it already has one replaces, does not chain
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   PostForwardHook()
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   test_model(UpperCamelCase__       )
						self.assertTrue(torch.allclose(UpperCamelCase__ ,    output + 1 ,    atol=1E-5       )       )

						# You need to use the sequential hook to chain two or more hooks
						SCREAMING_SNAKE_CASE_   :	Tuple													=   SequentialHook(PostForwardHook() ,    PostForwardHook()       )
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   test_model(UpperCamelCase__       )
						assert torch.allclose(UpperCamelCase__ ,    output + 2 ,    atol=1E-5       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ModelForTest()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   test_model(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Any													=   PostForwardHook()
						add_hook_to_module(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   test_model(UpperCamelCase__       )
						self.assertTrue(torch.allclose(UpperCamelCase__ ,    output + 1       )       )
						self.assertTrue(outputa.requires_grad       )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   True
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   test_model(UpperCamelCase__       )
						self.assertFalse(outputa.requires_grad       )


		@require_multi_gpu
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   ModelForTest()
						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# This will move each submodule on different devices
						add_hook_to_module(model.lineara ,    AlignDevicesHook(execution_device=0       )       )
						add_hook_to_module(model.batchnorm ,    AlignDevicesHook(execution_device=0       )       )
						add_hook_to_module(model.lineara ,    AlignDevicesHook(execution_device=1       )       )

						self.assertEqual(model.lineara.weight.device ,    torch.device(0       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device(0       )       )
						self.assertEqual(model.batchnorm.running_mean.device ,    torch.device(0       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device(1       )       )

						# We can still make a forward pass. The input does not need to be on any particular device
						SCREAMING_SNAKE_CASE_   :	int													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    torch.device(1       )       )

						# We can add a general hook to put back output on same device as input.
						add_hook_to_module(UpperCamelCase__ ,    AlignDevicesHook(io_same_device=UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.randn(2 ,    3       ).to(0       )
						SCREAMING_SNAKE_CASE_   :	int													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    torch.device(0       )       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   ModelForTest()

						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# This will move each submodule on different devices
						SCREAMING_SNAKE_CASE_   :	str													=   {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

						add_hook_to_module(model.lineara ,    AlignDevicesHook(**UpperCamelCase__       )       )
						add_hook_to_module(model.batchnorm ,    AlignDevicesHook(**UpperCamelCase__       )       )
						add_hook_to_module(model.lineara ,    AlignDevicesHook(**UpperCamelCase__       )       )

						# Parameters have been offloaded, so on the meta device
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						# Buffers are not included in the offload by default, so are on the execution device
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.device(hook_kwargs['execution_device']       )
						self.assertEqual(model.batchnorm.running_mean.device ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    UpperCamelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_module(model.lineara       )
						remove_hook_from_module(model.batchnorm       )
						remove_hook_from_module(model.lineara       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# Now test with buffers included in the offload
						SCREAMING_SNAKE_CASE_   :	str													=   {
						    'execution_device': 0 if torch.cuda.is_available() else 'cpu',
						    'offload': True,
						    'offload_buffers': True,
						}

						add_hook_to_module(model.lineara ,    AlignDevicesHook(**UpperCamelCase__       )       )
						add_hook_to_module(model.batchnorm ,    AlignDevicesHook(**UpperCamelCase__       )       )
						add_hook_to_module(model.lineara ,    AlignDevicesHook(**UpperCamelCase__       )       )

						# Parameters have been offloaded, so on the meta device, buffers included
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.running_mean.device ,    torch.device('meta'       )       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    UpperCamelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_module(model.lineara       )
						remove_hook_from_module(model.batchnorm       )
						remove_hook_from_module(model.lineara       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ModelForTest()

						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# This will move each submodule on different devices
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0 if torch.cuda.is_available() else 'cpu'
						attach_align_device_hook(UpperCamelCase__ ,    execution_device=UpperCamelCase__ ,    offload=UpperCamelCase__       )

						# Parameters have been offloaded, so on the meta device
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						# Buffers are not included in the offload by default, so are on the execution device
						SCREAMING_SNAKE_CASE_   :	Any													=   torch.device(UpperCamelCase__       )
						self.assertEqual(model.batchnorm.running_mean.device ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    UpperCamelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCamelCase__       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# Now test with buffers included in the offload
						attach_align_device_hook(UpperCamelCase__ ,    execution_device=UpperCamelCase__ ,    offload=UpperCamelCase__ ,    offload_buffers=UpperCamelCase__       )

						# Parameters have been offloaded, so on the meta device, buffers included
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.running_mean.device ,    torch.device('meta'       )       )

						SCREAMING_SNAKE_CASE_   :	int													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    UpperCamelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCamelCase__       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   ModelForTest()

						# Everything is on CPU
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# This will move each submodule on different devices
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0 if torch.cuda.is_available() else 'cpu'
						attach_align_device_hook(
						    UpperCamelCase__ ,    execution_device=UpperCamelCase__ ,    offload=UpperCamelCase__ ,    weights_map=model.state_dict()       )

						# Parameters have been offloaded, so on the meta device
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						# Buffers are not included in the offload by default, so are on the execution device
						SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.device(UpperCamelCase__       )
						self.assertEqual(model.batchnorm.running_mean.device ,    UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Any													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    UpperCamelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCamelCase__       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )

						# Now test with buffers included in the offload
						attach_align_device_hook(
						    UpperCamelCase__ ,    execution_device=UpperCamelCase__ ,    offload=UpperCamelCase__ ,    weights_map=model.state_dict() ,    offload_buffers=UpperCamelCase__ ,    )

						# Parameters have been offloaded, so on the meta device, buffers included
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('meta'       )       )
						self.assertEqual(model.batchnorm.running_mean.device ,    torch.device('meta'       )       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.randn(2 ,    3       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   model(UpperCamelCase__       )
						self.assertEqual(output.device ,    UpperCamelCase__       )

						# Removing hooks loads back the weights in the model.
						remove_hook_from_submodules(UpperCamelCase__       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.batchnorm.weight.device ,    torch.device('cpu'       )       )
						self.assertEqual(model.lineara.weight.device ,    torch.device('cpu'       )       )







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)