"""simple docstring"""







import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 		UpperCamelCase      (   lowercase				,     lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				str   =		AutoencoderKL
				UpperCAmelCase    :				str   =		"""sample"""
				UpperCAmelCase    :				List[str]   =		1E-2



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Optional[Any]:
					__snake_case      :							Optional[Any]						=			4
					__snake_case      :							Any						=			3
					__snake_case      :							Optional[int]						=			(32, 32)

					__snake_case      :							List[str]						=			floats_tensor((batch_size, num_channels) + sizes).to(_A)

					return {"sample": image}



				@property
				def  _lowercase					(self    :				int)   ->     Dict:
					return (3, 32, 32)



				@property
				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					return (3, 32, 32)



				def  _lowercase					(self    :				Any)   ->     str:
					__snake_case      :							Optional[int]						=			{
					    'block_out_channels': [32, 64],
					    'in_channels': 3,
					    'out_channels': 3,
					    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
					    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
					    'latent_channels': 4,
					}
					__snake_case      :							int						=			self.dummy_input
					return init_dict, inputs_dict



				def  _lowercase					(self    :				Optional[int])   ->     Optional[Any]:
					pass



				def  _lowercase					(self    :				Tuple)   ->     Any:
					pass



				@unittest.skipIf(torch_device == 'mps'     ,  'Gradient checkpointing skipped on MPS')
				def  _lowercase					(self    :				Optional[Any])   ->     List[Any]:
					# enable deterministic behavior for gradient checkpointing
					__snake_case		,    __snake_case      :							List[Any]						=			self.prepare_init_args_and_inputs_for_common()
					__snake_case      :							int						=			self.model_class(**_A)
					model.to(_A)

					assert not model.is_gradient_checkpointing and model.training

					__snake_case      :							List[Any]						=			model(**_A).sample
					# run the backwards pass on the model. For backwards pass, for simplicity purpose,
					# we won't calculate the loss and rather backprop on out.sum()
					model.zero_grad()

					__snake_case      :							str						=			torch.randn_like(_A)
					__snake_case      :							List[Any]						=			(out - labels).mean()
					loss.backward()

					# re-instantiate the model now enabling gradient checkpointing
					__snake_case      :							Optional[Any]						=			self.model_class(**_A)
					# clone model
					model_a.load_state_dict(model.state_dict())
					model_a.to(_A)
					model_a.enable_gradient_checkpointing()

					assert model_a.is_gradient_checkpointing and model_a.training

					__snake_case      :							List[Any]						=			model_a(**_A).sample
					# run the backwards pass on the model. For backwards pass, for simplicity purpose,
					# we won't calculate the loss and rather backprop on out.sum()
					model_a.zero_grad()
					__snake_case      :							Dict						=			(out_a - labels).mean()
					loss_a.backward()

					# compare the output and parameters gradients
					self.assertTrue((loss - loss_a).abs() < 1E-5)
					__snake_case      :							List[str]						=			dict(model.named_parameters())
					__snake_case      :							Union[str, Any]						=			dict(model_a.named_parameters())
					for name, param in named_params.items():
						self.assertTrue(torch_all_close(param.grad.data     ,  named_params_a[name].grad.data     ,  atol=5E-5))



				def  _lowercase					(self    :				Union[str, Any])   ->     Any:
					__snake_case		,    __snake_case      :							List[Any]						=			AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'     ,  output_loading_info=_A)
					self.assertIsNotNone(_A)
					self.assertEqual(len(loading_info['missing_keys'])     ,  0)

					model.to(_A)
					__snake_case      :							str						=			model(**self.dummy_input)

					assert image is not None, "Make sure output is not None"






				def  _lowercase					(self    :				Optional[Any])   ->     str:
					__snake_case      :							Optional[int]						=			AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy')
					__snake_case      :							List[Any]						=			model.to(_A)
					model.eval()

					if torch_device == "mps":
						__snake_case      :							str						=			torch.manual_seed(0)
					else:
						__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)

					__snake_case      :							Optional[int]						=			torch.randn(
					    1     ,  model.config.in_channels     ,  model.config.sample_size     ,  model.config.sample_size     ,  generator=torch.manual_seed(0)     ,  )
					__snake_case      :							List[Any]						=			image.to(_A)
					with torch.no_grad():
						__snake_case      :							str						=			model(_A     ,  sample_posterior=_A     ,  generator=_A).sample

					__snake_case      :							Tuple						=			output[0, -1, -3:, -3:].flatten().cpu()

					# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
					# the expected output slices are not the same for CPU and GPU.
					if torch_device == "mps":
						__snake_case      :							int						=			torch.tensor(
						    [
						        -4.0_078E-01,
						        -3.8_323E-04,
						        -1.2_681E-01,
						        -1.1_462E-01,
						        2.0_095E-01,
						        1.0_893E-01,
						        -8.8_247E-02,
						        -3.0_361E-01,
						        -9.8_644E-03,
						    ])
					elif torch_device == "cpu":
						__snake_case      :							Any						=			torch.tensor(
						    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026])
					else:
						__snake_case      :							int						=			torch.tensor(
						    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485])

					self.assertTrue(torch_all_close(_A     ,  _A     ,  rtol=1E-2))







@slow
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Any     ,  _A    :				Union[str, Any]     ,  _A    :				Tuple)   ->     List[Any]:
					return f"gaussian_noise_s={seed}_shape={'_'.join([str(_A) for s in shape])}.npy"



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]=0     ,  _A    :				List[str]=(4, 3, 5_12, 5_12)     ,  _A    :				Union[str, Any]=False)   ->     int:
					__snake_case      :							Optional[int]						=			torch.floataa if fpaa else torch.floataa
					__snake_case      :							Optional[Any]						=			torch.from_numpy(load_hf_numpy(self.get_file_format(_A     ,  _A))).to(_A).to(_A)
					return image



				def  _lowercase					(self    :				Dict     ,  _A    :				int="CompVis/stable-diffusion-v1-4"     ,  _A    :				Any=False)   ->     List[Any]:
					__snake_case      :							Dict						=			'fp16' if fpaa else None
					__snake_case      :							Any						=			torch.floataa if fpaa else torch.floataa

					__snake_case      :							Union[str, Any]						=			AutoencoderKL.from_pretrained(
					    _A     ,  subfolder='vae'     ,  torch_dtype=_A     ,  revision=_A     ,  )
					model.to(_A).eval()

					return model



				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				Any=0)   ->     Union[str, Any]:
					if torch_device == "mps":
						return torch.manual_seed(_A)
					return torch.Generator(device=_A).manual_seed(_A)



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
				        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
				        # fmt: on
				    ])
				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				Optional[int]     ,  _A    :				int     ,  _A    :				Optional[Any])   ->     int:
					__snake_case      :							List[Any]						=			self.get_sd_vae_model()
					__snake_case      :							Optional[int]						=			self.get_sd_image(_A)
					__snake_case      :							Any						=			self.get_generator(_A)

					with torch.no_grad():
						__snake_case      :							Optional[Any]						=			model(_A     ,  generator=_A     ,  sample_posterior=_A).sample

					assert sample.shape == image.shape

					__snake_case      :							Dict						=			sample[-1, -2:, -2:, :2].flatten().float().cpu()
					__snake_case      :							Tuple						=			torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice)

					assert torch_all_close(_A     ,  _A     ,  atol=3E-3)



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
				        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
				        # fmt: on
				    ])
				@require_torch_gpu
				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				Tuple     ,  _A    :				str)   ->     str:
					__snake_case      :							int						=			self.get_sd_vae_model(fpaa=_A)
					__snake_case      :							List[Any]						=			self.get_sd_image(_A     ,  fpaa=_A)
					__snake_case      :							Any						=			self.get_generator(_A)

					with torch.no_grad():
						__snake_case      :							Optional[int]						=			model(_A     ,  generator=_A     ,  sample_posterior=_A).sample

					assert sample.shape == image.shape

					__snake_case      :							int						=			sample[-1, -2:, :2, -2:].flatten().float().cpu()
					__snake_case      :							Any						=			torch.tensor(_A)

					assert torch_all_close(_A     ,  _A     ,  atol=1E-2)



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
				        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
				        # fmt: on
				    ])
				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				int     ,  _A    :				Dict     ,  _A    :				Dict)   ->     Any:
					__snake_case      :							Any						=			self.get_sd_vae_model()
					__snake_case      :							Dict						=			self.get_sd_image(_A)

					with torch.no_grad():
						__snake_case      :							Tuple						=			model(_A).sample

					assert sample.shape == image.shape

					__snake_case      :							Tuple						=			sample[-1, -2:, -2:, :2].flatten().float().cpu()
					__snake_case      :							Tuple						=			torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice)

					assert torch_all_close(_A     ,  _A     ,  atol=3E-3)



				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
				        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
				        # fmt: on
				    ])
				@require_torch_gpu
				def  _lowercase					(self    :				Tuple     ,  _A    :				Any     ,  _A    :				Tuple)   ->     str:
					__snake_case      :							Tuple						=			self.get_sd_vae_model()
					__snake_case      :							List[Any]						=			self.get_sd_image(_A     ,  shape=(3, 4, 64, 64))

					with torch.no_grad():
						__snake_case      :							Tuple						=			model.decode(_A).sample

					assert list(sample.shape) == [3, 3, 5_12, 5_12]

					__snake_case      :							Any						=			sample[-1, -2:, :2, -2:].flatten().cpu()
					__snake_case      :							List[str]						=			torch.tensor(_A)

					assert torch_all_close(_A     ,  _A     ,  atol=1E-3)



				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
				        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
				        # fmt: on
				    ])
				@require_torch_gpu
				def  _lowercase					(self    :				Dict     ,  _A    :				Union[str, Any]     ,  _A    :				Dict)   ->     Optional[int]:
					__snake_case      :							Optional[int]						=			self.get_sd_vae_model(fpaa=_A)
					__snake_case      :							List[str]						=			self.get_sd_image(_A     ,  shape=(3, 4, 64, 64)     ,  fpaa=_A)

					with torch.no_grad():
						__snake_case      :							Tuple						=			model.decode(_A).sample

					assert list(sample.shape) == [3, 3, 5_12, 5_12]

					__snake_case      :							Optional[Any]						=			sample[-1, -2:, :2, -2:].flatten().float().cpu()
					__snake_case      :							Optional[Any]						=			torch.tensor(_A)

					assert torch_all_close(_A     ,  _A     ,  atol=5E-3)



				@parameterized.expand([(13,), (16,), (27,)])
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()     ,  reason='xformers is not required when using PyTorch 2.0.')
				def  _lowercase					(self    :				List[Any]     ,  _A    :				Union[str, Any])   ->     List[str]:
					__snake_case      :							Any						=			self.get_sd_vae_model(fpaa=_A)
					__snake_case      :							int						=			self.get_sd_image(_A     ,  shape=(3, 4, 64, 64)     ,  fpaa=_A)

					with torch.no_grad():
						__snake_case      :							Optional[Any]						=			model.decode(_A).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
						__snake_case      :							Tuple						=			model.decode(_A).sample

					assert list(sample.shape) == [3, 3, 5_12, 5_12]

					assert torch_all_close(_A     ,  _A     ,  atol=1E-1)



				@parameterized.expand([(13,), (16,), (37,)])
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()     ,  reason='xformers is not required when using PyTorch 2.0.')
				def  _lowercase					(self    :				Dict     ,  _A    :				Optional[Any])   ->     Tuple:
					__snake_case      :							Union[str, Any]						=			self.get_sd_vae_model()
					__snake_case      :							Tuple						=			self.get_sd_image(_A     ,  shape=(3, 4, 64, 64))

					with torch.no_grad():
						__snake_case      :							List[Any]						=			model.decode(_A).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
						__snake_case      :							Any						=			model.decode(_A).sample

					assert list(sample.shape) == [3, 3, 5_12, 5_12]

					assert torch_all_close(_A     ,  _A     ,  atol=1E-2)






				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
				        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
				        # fmt: on
				    ])
				def  _lowercase					(self    :				Any     ,  _A    :				int     ,  _A    :				Optional[Any])   ->     Dict:
					__snake_case      :							Optional[Any]						=			self.get_sd_vae_model()
					__snake_case      :							Union[str, Any]						=			self.get_sd_image(_A)
					__snake_case      :							Any						=			self.get_generator(_A)

					with torch.no_grad():
						__snake_case      :							Dict						=			model.encode(_A).latent_dist
						__snake_case      :							Dict						=			dist.sample(generator=_A)

					assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

					__snake_case      :							str						=			sample[0, -1, -3:, -3:].flatten().cpu()
					__snake_case      :							List[str]						=			torch.tensor(_A)

					__snake_case      :							Any						=			3E-3 if torch_device != 'mps' else 1E-2
					assert torch_all_close(_A     ,  _A     ,  atol=_A)






"""simple docstring"""







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_a					:					Optional[int]=			logging.get_logger()

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				List[nn.Module]   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				list   =		field(default_factory=lowercase						)



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			len(list(m.modules())) == 1 or isinstance(_A     ,  nn.Convad) or isinstance(_A     ,  nn.BatchNormad)
					if has_not_submodules:
						self.traced.append(_A)



				def __call__(self    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
					self.module(_A)
					[x.remove() for x in self.handles]
					return self






				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda _A: len(list(x.state_dict().keys())) > 0     ,  self.traced))







@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



				def __call__(self    :				List[str]     ,  _A    :				Tensor)   ->     List[Any]:
					__snake_case      :							Any						=			Tracker(self.dest)(_A).parametrized
					__snake_case      :							int						=			Tracker(self.src)(_A).parametrized

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			list(filter(lambda _A: type(_A) not in self.dest_skip     ,  _A))

					if len(_A) != len(_A):
						raise Exception(
						    f"Numbers of operations are different. Source module has {len(_A)} operations while"
						    f" destination module has {len(_A)}.")

					for dest_m, src_m in zip(_A     ,  _A):
						dest_m.load_state_dict(src_m.state_dict())
						if self.verbose == 1:
							print(f"Transfered from={src_m} to={dest_m}")







def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

	assert torch.allclose(from_model(UpperCAmelCase_  )      ,							our_model(UpperCAmelCase_  ).logits  ), "The model logits don't match the original one."

	__snake_case      :							str						=			F"resnet{'-'.join(name.split('resnet'  )  )}"
	print(UpperCAmelCase_  )

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message='Add model'      ,							use_temp_dir=UpperCAmelCase_      ,							)

		# we can use the convnext one
		__snake_case      :							int						=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'  )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message='Add image processor'      ,							use_temp_dir=UpperCAmelCase_      ,							)

		print(F"Pushed {checkpoint_name}"  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							Optional[Any]						=			10_00
	__snake_case      :							Any						=			(1, num_labels)

	__snake_case      :							List[Any]						=			'huggingface/label-files'
	__snake_case      :							Dict						=			num_labels
	__snake_case      :							Any						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
	__snake_case      :							Any						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	}

	if model_name:
		convert_weight_and_push(UpperCAmelCase_      ,							names_to_config[model_name]      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	return config, expected_shape


if __name__ == "__main__":
	_a					:					Optional[Any]=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default=None,
	    type=str,
	    help=(
	        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
	        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
	    ),
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=Path,
	    required=True,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument(
	    "--push_to_hub",
	    default=True,
	    type=bool,
	    required=False,
	    help="If True, push model and image processor to the hub.",
	)

	_a					:					Union[str, Any]=			parser.parse_args()
	_a					:					Path=			args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





