


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      UpperCamelCase(	nn.Module ):


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




											'''simple docstring'''

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






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						List[str]:




											'''simple docstring'''

											return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE    )    )    )






class      UpperCamelCase(	__lowercase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     *SCREAMING_SNAKE_CASE						:		Dict  ,     **SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						Tuple:




											'''simple docstring'''

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






class      UpperCamelCase(	__lowercase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						Union[str, Any]:




											'''simple docstring'''

											return output + 1






class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Any:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						List[str]:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Union[str, 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 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						List[str]:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						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 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						List[Any]:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						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				=		{"""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 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						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 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Union[str, Any]:




											'''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 json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

			if "_quant" in model_name:
						raise ValueError("""Quantized models are not supported.""" )

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} 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 push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v1_1.0_224",
	    type=str,
	    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	__UpperCamelCase					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


