




'''simple docstring'''



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      lowerCAmelCase (			nn.Module):


       def __init__( self	)   ->	Optional[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	nn.Linear(3     ,	4	)
            __snake_case            =	nn.BatchNormad(4	)
            __snake_case            =	nn.Linear(4     ,	5	)




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            return self.lineara(self.batchnorm(self.lineara(__SCREAMING_SNAKE_CASE	)	)	)





class      lowerCAmelCase (			__lowerCAmelCase):


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Union[str, Any]:



            '''simple docstring'''




            return (args[0] + 1,) + args[1:], kwargs





class      lowerCAmelCase (			__lowerCAmelCase):


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            return output + 1





class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	ModelForTest()
            __snake_case            =	ModelHook()

            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            self.assertEqual(test_model._hf_hook     ,	__SCREAMING_SNAKE_CASE	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''_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(__SCREAMING_SNAKE_CASE	)
            self.assertFalse(hasattr(__SCREAMING_SNAKE_CASE     ,	'''_hf_hook'''	)	)
            self.assertFalse(hasattr(__SCREAMING_SNAKE_CASE     ,	'''_old_forward'''	)	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	ModelForTest()
            __snake_case            =	ModelHook()

            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	append=__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	ModelForTest()
            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	test_model(x + 1	)
            __snake_case            =	test_model(x + 2	)

            __snake_case            =	PreForwardHook()
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-5	)	)

            # Attaching a hook to a model when it already has one replaces, does not chain
            __snake_case            =	PreForwardHook()
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-5	)	)

            # You need to use the sequential hook to chain two or more hooks
            __snake_case            =	SequentialHook(PreForwardHook()     ,	PreForwardHook()	)
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            assert torch.allclose(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-5	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	ModelForTest()
            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	PostForwardHook()
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	output + 1     ,	atol=1E-5	)	)

            # Attaching a hook to a model when it already has one replaces, does not chain
            __snake_case            =	PostForwardHook()
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	output + 1     ,	atol=1E-5	)	)

            # You need to use the sequential hook to chain two or more hooks
            __snake_case            =	SequentialHook(PostForwardHook()     ,	PostForwardHook()	)
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            assert torch.allclose(__SCREAMING_SNAKE_CASE     ,	output + 2     ,	atol=1E-5	)


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	ModelForTest()
            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	PostForwardHook()
            add_hook_to_module(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	output + 1	)	)
            self.assertTrue(outputa.requires_grad	)

            __snake_case            =	True
            __snake_case            =	test_model(__SCREAMING_SNAKE_CASE	)
            self.assertFalse(outputa.requires_grad	)


       @require_multi_gpu
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	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
            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            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(__SCREAMING_SNAKE_CASE     ,	AlignDevicesHook(io_same_device=__SCREAMING_SNAKE_CASE	)	)
            __snake_case            =	torch.randn(2     ,	3	).to(0	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	torch.device(0	)	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	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
            __snake_case            =	{'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}

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

            # 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
            __snake_case            =	torch.device(hook_kwargs['''execution_device''']	)
            self.assertEqual(model.batchnorm.running_mean.device     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	__SCREAMING_SNAKE_CASE	)

            # 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
            __snake_case            =	{
                '''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
                '''offload''': True,
                '''offload_buffers''': True,
            }

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

            # 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'''	)	)

            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	__SCREAMING_SNAKE_CASE	)

            # 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    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	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
            __snake_case            =	0 if torch.cuda.is_available() else '''cpu'''
            attach_align_device_hook(__SCREAMING_SNAKE_CASE     ,	execution_device=__SCREAMING_SNAKE_CASE     ,	offload=__SCREAMING_SNAKE_CASE	)

            # 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
            __snake_case            =	torch.device(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(model.batchnorm.running_mean.device     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	__SCREAMING_SNAKE_CASE	)

            # Removing hooks loads back the weights in the model.
            remove_hook_from_submodules(__SCREAMING_SNAKE_CASE	)
            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(__SCREAMING_SNAKE_CASE     ,	execution_device=__SCREAMING_SNAKE_CASE     ,	offload=__SCREAMING_SNAKE_CASE     ,	offload_buffers=__SCREAMING_SNAKE_CASE	)

            # 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'''	)	)

            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	__SCREAMING_SNAKE_CASE	)

            # Removing hooks loads back the weights in the model.
            remove_hook_from_submodules(__SCREAMING_SNAKE_CASE	)
            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    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	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
            __snake_case            =	0 if torch.cuda.is_available() else '''cpu'''
            attach_align_device_hook(
                __SCREAMING_SNAKE_CASE     ,	execution_device=__SCREAMING_SNAKE_CASE     ,	offload=__SCREAMING_SNAKE_CASE     ,	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
            __snake_case            =	torch.device(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(model.batchnorm.running_mean.device     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	__SCREAMING_SNAKE_CASE	)

            # Removing hooks loads back the weights in the model.
            remove_hook_from_submodules(__SCREAMING_SNAKE_CASE	)
            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(
                __SCREAMING_SNAKE_CASE     ,	execution_device=__SCREAMING_SNAKE_CASE     ,	offload=__SCREAMING_SNAKE_CASE     ,	weights_map=model.state_dict()     ,	offload_buffers=__SCREAMING_SNAKE_CASE     ,	)

            # 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'''	)	)

            __snake_case            =	torch.randn(2     ,	3	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(output.device     ,	__SCREAMING_SNAKE_CASE	)

            # Removing hooks loads back the weights in the model.
            remove_hook_from_submodules(__SCREAMING_SNAKE_CASE	)
            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'''	)	)












'''simple docstring'''



import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  str						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	old_name

     if "patch_embed" in old_name:
          __snake_case       ,     __snake_case       ,     __snake_case            =	old_name.split('''.'''						)

          if layer == "0":
               __snake_case            =	old_name.replace('''0''' ,						'''convolution1'''						)
          elif layer == "1":
               __snake_case            =	old_name.replace('''1''' ,						'''batchnorm_before'''						)
          elif layer == "3":
               __snake_case            =	old_name.replace('''3''' ,						'''convolution2'''						)
          else:
               __snake_case            =	old_name.replace('''4''' ,						'''batchnorm_after'''						)

     if "network" in old_name and re.search(R'''\d\.\d''' ,						_lowerCamelCase						):
          __snake_case            =	R'''\b\d{2}\b'''
          if bool(re.search(_lowerCamelCase ,						_lowerCamelCase						)						):
               __snake_case            =	re.search(R'''\d\.\d\d.''' ,						_lowerCamelCase						).group()
          else:
               __snake_case            =	re.search(R'''\d\.\d.''' ,						_lowerCamelCase						).group()
          if int(match[0]						) < 6:
               __snake_case            =	old_name.replace(_lowerCamelCase ,						''''''						)
               __snake_case            =	trimmed_name.replace('''network''' ,						match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]						)
               __snake_case            =	'''intermediate_stages.''' + trimmed_name
          else:
               __snake_case            =	old_name.replace(_lowerCamelCase ,						''''''						)
               if int(match[2]						) < num_meta4D_last_stage:
                    __snake_case            =	trimmed_name.replace('''network''' ,						'''meta4D_layers.blocks.''' + match[2]						)
               else:
                    __snake_case            =	str(int(match[2]						) - num_meta4D_last_stage						)
                    __snake_case            =	trimmed_name.replace('''network''' ,						'''meta3D_layers.blocks.''' + layer_index						)
                    if "norm1" in old_name:
                         __snake_case            =	trimmed_name.replace('''norm1''' ,						'''layernorm1'''						)
                    elif "norm2" in old_name:
                         __snake_case            =	trimmed_name.replace('''norm2''' ,						'''layernorm2'''						)
                    elif "fc1" in old_name:
                         __snake_case            =	trimmed_name.replace('''fc1''' ,						'''linear_in'''						)
                    elif "fc2" in old_name:
                         __snake_case            =	trimmed_name.replace('''fc2''' ,						'''linear_out'''						)

               __snake_case            =	'''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(R'''.\d.''' ,						_lowerCamelCase						):
          __snake_case            =	old_name.replace('''network''' ,						'''intermediate_stages'''						)

     if "fc" in new_name:
          __snake_case            =	new_name.replace('''fc''' ,						'''convolution'''						)
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          __snake_case            =	new_name.replace('''norm1''' ,						'''batchnorm_before'''						)
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          __snake_case            =	new_name.replace('''norm2''' ,						'''batchnorm_after'''						)
     if "proj" in new_name:
          __snake_case            =	new_name.replace('''proj''' ,						'''projection'''						)
     if "dist_head" in new_name:
          __snake_case            =	new_name.replace('''dist_head''' ,						'''distillation_classifier'''						)
     elif "head" in new_name:
          __snake_case            =	new_name.replace('''head''' ,						'''classifier'''						)
     elif "patch_embed" in new_name:
          __snake_case            =	'''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          __snake_case            =	new_name.replace('''norm''' ,						'''layernorm'''						)
          __snake_case            =	'''efficientformer.''' + new_name
     else:
          __snake_case            =	'''efficientformer.encoder.''' + new_name

     return new_name







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     for key in checkpoint.copy().keys():
          __snake_case            =	checkpoint.pop(_lowerCamelCase						)
          __snake_case            =	val

     return checkpoint







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __snake_case            =	Image.open(requests.get(_lowerCamelCase ,						stream=_lowerCamelCase						).raw						)

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  bool						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)['''model''']
     __snake_case            =	EfficientFormerConfig.from_json_file(_lowerCamelCase						)
     __snake_case            =	EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase						)
     __snake_case            =	'''_'''.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

     __snake_case            =	config.depths[-1] - config.num_metaad_blocks + 1
     __snake_case            =	convert_torch_checkpoint(_lowerCamelCase ,						_lowerCamelCase						)

     model.load_state_dict(_lowerCamelCase						)
     model.eval()

     __snake_case            =	{
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     __snake_case            =	prepare_img()
     __snake_case            =	2_56
     __snake_case            =	2_24
     __snake_case            =	EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size} ,						crop_size={'''height''': crop_size, '''width''': crop_size} ,						resample=pillow_resamplings['''bicubic'''] ,						)
     __snake_case            =	processor(images=_lowerCamelCase ,						return_tensors='''pt'''						).pixel_values

     # original processing pipeline
     __snake_case            =	Compose(
         [
             Resize(_lowerCamelCase ,						interpolation=pillow_resamplings['''bicubic''']						),
             CenterCrop(_lowerCamelCase						),
             ToTensor(),
             Normalize(_lowerCamelCase ,						_lowerCamelCase						),
         ]						)
     __snake_case            =	image_transforms(_lowerCamelCase						).unsqueeze(0						)

     assert torch.allclose(_lowerCamelCase ,						_lowerCamelCase						)

     __snake_case            =	model(_lowerCamelCase						)
     __snake_case            =	outputs.logits

     __snake_case            =	(1, 10_00)

     if "l1" in model_name:
          __snake_case            =	torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]						)
          assert torch.allclose(logits[0, :10] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          __snake_case            =	torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]						)
          assert torch.allclose(logits[0, :10] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          __snake_case            =	torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]						)
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''						)

     # Save Checkpoints
     Path(_lowerCamelCase						).mkdir(exist_ok=_lowerCamelCase						)
     model.save_pretrained(_lowerCamelCase						)
     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)
     processor.save_pretrained(_lowerCamelCase						)
     print(f'''Processor successfuly saved at {pytorch_dump_path}'''						)

     if push_to_hub:
          print('''Pushing model to the hub...'''						)

          model.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,						commit_message='''Add model''' ,						use_temp_dir=_lowerCamelCase ,						)
          processor.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,						commit_message='''Add image processor''' ,						use_temp_dir=_lowerCamelCase ,						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Optional[int]	     =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to EfficientFormer pytorch checkpoint.''',
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for EfficientFormer model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )

     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
     parser.add_argument(
         '''--no-push_to_hub''',
         dest='''push_to_hub''',
         action='''store_false''',
         help='''Do not push model and image processor to the hub''',
     )
     parser.set_defaults(push_to_hub=True)

     UpperCAmelCase_							:	Union[str, Any]	     =		parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )






