






'''simple docstring'''







import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
							from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 		UpperCamelCase__     :




		'''simple docstring'''





		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase=13 ,_lowerCAmelCase=32 ,_lowerCAmelCase=2 ,_lowerCAmelCase=3 ,_lowerCAmelCase=16 ,_lowerCAmelCase=[1, 2, 1] ,_lowerCAmelCase=[2, 2, 4] ,_lowerCAmelCase=2 ,_lowerCAmelCase=2.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase="gelu" ,_lowerCAmelCase=False ,_lowerCAmelCase=True ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=10 ,_lowerCAmelCase=8 ,):
								lowerCamelCase__								=	parent
								lowerCamelCase__								=	batch_size
								lowerCamelCase__								=	image_size
								lowerCamelCase__								=	patch_size
								lowerCamelCase__								=	num_channels
								lowerCamelCase__								=	embed_dim
								lowerCamelCase__								=	depths
								lowerCamelCase__								=	num_heads
								lowerCamelCase__								=	window_size
								lowerCamelCase__								=	mlp_ratio
								lowerCamelCase__								=	qkv_bias
								lowerCamelCase__								=	hidden_dropout_prob
								lowerCamelCase__								=	attention_probs_dropout_prob
								lowerCamelCase__								=	drop_path_rate
								lowerCamelCase__								=	hidden_act
								lowerCamelCase__								=	use_absolute_embeddings
								lowerCamelCase__								=	patch_norm
								lowerCamelCase__								=	layer_norm_eps
								lowerCamelCase__								=	initializer_range
								lowerCamelCase__								=	is_training
								lowerCamelCase__								=	scope
								lowerCamelCase__								=	use_labels
								lowerCamelCase__								=	type_sequence_label_size
								lowerCamelCase__								=	encoder_stride






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

								lowerCamelCase__								=	None
								if self.use_labels:
														lowerCamelCase__								=	ids_tensor([self.batch_size] ,self.type_sequence_label_size							)

								lowerCamelCase__								=	self.get_config()

								return config, pixel_values, labels






		def       UpperCamelCase_       (   self							):
								return SwinvaConfig(
								    image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,embed_dim=self.embed_dim ,depths=self.depths ,num_heads=self.num_heads ,window_size=self.window_size ,mlp_ratio=self.mlp_ratio ,qkv_bias=self.qkv_bias ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,drop_path_rate=self.drop_path_rate ,hidden_act=self.hidden_act ,use_absolute_embeddings=self.use_absolute_embeddings ,path_norm=self.patch_norm ,layer_norm_eps=self.layer_norm_eps ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	SwinvaModel(config=_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase							)

								lowerCamelCase__								=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
								lowerCamelCase__								=	int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

								self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, expected_seq_len, expected_dim)							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	SwinvaForMaskedImageModeling(config=_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase							)
								self.parent.assertEqual(
								    result.logits.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

								# test greyscale images
								lowerCamelCase__								=	1
								lowerCamelCase__								=	SwinvaForMaskedImageModeling(_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()

								lowerCamelCase__								=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase__								=	model(_lowerCAmelCase							)
								self.parent.assertEqual(result.logits.shape ,(self.batch_size, 1, self.image_size, self.image_size)							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	self.type_sequence_label_size
								lowerCamelCase__								=	SwinvaForImageClassification(_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()
								lowerCamelCase__								=	model(_lowerCAmelCase ,labels=_lowerCAmelCase							)
								self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	config_and_inputs
								lowerCamelCase__								=	{"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class 		UpperCamelCase__     (a ,a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       (
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		_UpperCamelCase						=       (
		    {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_UpperCamelCase						=       False
		_UpperCamelCase						=       False
		_UpperCamelCase						=       False
		_UpperCamelCase						=       False






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	SwinvaModelTester(self							)
								lowerCamelCase__								=	ConfigTester(self ,config_class=_lowerCAmelCase ,embed_dim=37							)






		def       UpperCamelCase_       (   self							):
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCAmelCase							)






		@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""							)
		def       UpperCamelCase_       (   self							):
								pass






		@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""							)
		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__								=	model_class(_lowerCAmelCase							)
														self.assertIsInstance(model.get_input_embeddings() ,(nn.Module)							)
														lowerCamelCase__								=	model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_lowerCAmelCase ,nn.Linear							)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCamelCase__								=	model_class(_lowerCAmelCase							)
														lowerCamelCase__								=	inspect.signature(model.forward							)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowerCamelCase__								=	[*signature.parameters.keys()]

														lowerCamelCase__								=	["""pixel_values"""]
														self.assertListEqual(arg_names[:1] ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__								=	True

								for model_class in self.all_model_classes:
														lowerCamelCase__								=	True
														lowerCamelCase__								=	False
														lowerCamelCase__								=	True
														lowerCamelCase__								=	model_class(_lowerCAmelCase							)
														model.to(_lowerCAmelCase							)
														model.eval()
														with torch.no_grad():
																				lowerCamelCase__								=	model(**self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase							)							)
														lowerCamelCase__								=	outputs.attentions
														lowerCamelCase__								=	len(self.model_tester.depths							)
														self.assertEqual(len(_lowerCAmelCase							) ,_lowerCAmelCase							)

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														lowerCamelCase__								=	True
														lowerCamelCase__								=	config.window_size**2
														lowerCamelCase__								=	model_class(_lowerCAmelCase							)
														model.to(_lowerCAmelCase							)
														model.eval()
														with torch.no_grad():
																				lowerCamelCase__								=	model(**self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase							)							)
														lowerCamelCase__								=	outputs.attentions
														self.assertEqual(len(_lowerCAmelCase							) ,_lowerCAmelCase							)

														self.assertListEqual(
														    list(attentions[0].shape[-3:]							) ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,)
														lowerCamelCase__								=	len(_lowerCAmelCase							)

														# Check attention is always last and order is fine
														lowerCamelCase__								=	True
														lowerCamelCase__								=	True
														lowerCamelCase__								=	model_class(_lowerCAmelCase							)
														model.to(_lowerCAmelCase							)
														model.eval()
														with torch.no_grad():
																				lowerCamelCase__								=	model(**self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase							)							)

														if hasattr(self.model_tester ,"""num_hidden_states_types"""							):
																				lowerCamelCase__								=	self.model_tester.num_hidden_states_types
														else:
																				# also another +1 for reshaped_hidden_states
																				lowerCamelCase__								=	2
														self.assertEqual(out_len + added_hidden_states ,len(_lowerCAmelCase							)							)

														lowerCamelCase__								=	outputs.attentions

														self.assertEqual(len(_lowerCAmelCase							) ,_lowerCAmelCase							)

														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]							) ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	model_class(_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								model.eval()

								with torch.no_grad():
														lowerCamelCase__								=	model(**self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase							)							)

								lowerCamelCase__								=	outputs.hidden_states

								lowerCamelCase__								=	getattr(
								    self.model_tester ,"""expected_num_hidden_layers""" ,len(self.model_tester.depths							) + 1							)
								self.assertEqual(len(_lowerCAmelCase							) ,_lowerCAmelCase							)

								# Swinv2 has a different seq_length
								lowerCamelCase__								=	(
								    config.patch_size
								    if isinstance(config.patch_size ,collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase__								=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]							) ,[num_patches, self.model_tester.embed_dim] ,)

								lowerCamelCase__								=	outputs.reshaped_hidden_states
								self.assertEqual(len(_lowerCAmelCase							) ,_lowerCAmelCase							)

								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	reshaped_hidden_states[0].shape
								lowerCamelCase__								=	(
								    reshaped_hidden_states[0].view(_lowerCAmelCase ,_lowerCAmelCase ,height * width							).permute(0 ,2 ,1							)
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]							) ,[num_patches, self.model_tester.embed_dim] ,)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__								=	(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size ,collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
														lowerCamelCase__								=	True
														self.check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowerCamelCase__								=	True

														self.check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__								=	3

								lowerCamelCase__								=	(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size ,collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								lowerCamelCase__								=	(
								    config.patch_size
								    if isinstance(config.patch_size ,collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase__								=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCamelCase__								=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
														lowerCamelCase__								=	True
														self.check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,(padded_height, padded_width)							)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowerCamelCase__								=	True
														self.check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,(padded_height, padded_width)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase							)






		@slow
		def       UpperCamelCase_       (   self							):
								for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCamelCase__								=	SwinvaModel.from_pretrained(_lowerCAmelCase							)
														self.assertIsNotNone(_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase__								=	_config_zero_init(_lowerCAmelCase							)
								for model_class in self.all_model_classes:
														lowerCamelCase__								=	model_class(config=_lowerCAmelCase							)
														for name, param in model.named_parameters():
																				if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,)




@require_vision
@require_torch
class 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		@cached_property
		def       UpperCamelCase_       (   self							):
								return (
								    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""							)
								    if is_vision_available()
								    else None
								)






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""							).to(
								    _lowerCAmelCase							)
								lowerCamelCase__								=	self.default_image_processor

								lowerCamelCase__								=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
								lowerCamelCase__								=	image_processor(images=_lowerCAmelCase ,return_tensors="""pt"""							).to(_lowerCAmelCase							)

								# forward pass
								with torch.no_grad():
														lowerCamelCase__								=	model(**_lowerCAmelCase							)

								# verify the logits
								lowerCamelCase__								=	torch.Size((1, 10_00)							)
								self.assertEqual(outputs.logits.shape ,_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor([-0.3947, -0.4306, 0.0026]							).to(_lowerCAmelCase							)
								self.assertTrue(torch.allclose(outputs.logits[0, :3] ,_lowerCAmelCase ,atol=1E-4							)							)





'''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 	_A  (       A							,A							)  ->				str:
	lowercase						:		Optional[int]   					=		old_name

	if "patch_embed" in old_name:
		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		old_name.split("."							)

		if layer == "0":
			lowercase						:		int   					=		old_name.replace("0"							,"convolution1"							)
		elif layer == "1":
			lowercase						:		List[str]   					=		old_name.replace("1"							,"batchnorm_before"							)
		elif layer == "3":
			lowercase						:		Dict   					=		old_name.replace("3"							,"convolution2"							)
		else:
			lowercase						:		Union[str, Any]   					=		old_name.replace("4"							,"batchnorm_after"							)

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

			lowercase						:		Dict   					=		"last_stage." + trimmed_name

	elif "network" in old_name and re.search(r".\d."							,A							):
		lowercase						:		Union[str, Any]   					=		old_name.replace("network"							,"intermediate_stages"							)

	if "fc" in new_name:
		lowercase						:		Any   					=		new_name.replace("fc"							,"convolution"							)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		lowercase						:		Optional[Any]   					=		new_name.replace("norm1"							,"batchnorm_before"							)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		lowercase						:		List[str]   					=		new_name.replace("norm2"							,"batchnorm_after"							)
	if "proj" in new_name:
		lowercase						:		Optional[int]   					=		new_name.replace("proj"							,"projection"							)
	if "dist_head" in new_name:
		lowercase						:		Tuple   					=		new_name.replace("dist_head"							,"distillation_classifier"							)
	elif "head" in new_name:
		lowercase						:		Tuple   					=		new_name.replace("head"							,"classifier"							)
	elif "patch_embed" in new_name:
		lowercase						:		Optional[int]   					=		"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		lowercase						:		str   					=		new_name.replace("norm"							,"layernorm"							)
		lowercase						:		List[Any]   					=		"efficientformer." + new_name
	else:
		lowercase						:		Optional[Any]   					=		"efficientformer.encoder." + new_name

	return new_name




def 	_A  (       A							,A							)  ->				Optional[Any]:
	for key in checkpoint.copy().keys():
		lowercase						:		List[str]   					=		checkpoint.pop(A							)
		lowercase						:		int   					=		val

	return checkpoint




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		str   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		Optional[Any]   					=		Image.open(requests.get(A							,stream=A							).raw							)

	return image




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		torch.load(A							,map_location="cpu"							)["model"]
	lowercase						:		int   					=		EfficientFormerConfig.from_json_file(A							)
	lowercase						:		Tuple   					=		EfficientFormerForImageClassificationWithTeacher(A							)
	lowercase						:		int   					=		"_".join(checkpoint_path.split("/"							)[-1].split("."							)[0].split("_"							)[:-1]							)

	lowercase						:		Optional[int]   					=		config.depths[-1] - config.num_metaad_blocks + 1
	lowercase						:		int   					=		convert_torch_checkpoint(A							,A							)

	model.load_state_dict(A							)
	model.eval()

	lowercase						:		List[Any]   					=		{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	lowercase						:		Tuple   					=		prepare_img()
	lowercase						:		Optional[int]   					=		2_5_6
	lowercase						:		str   					=		2_2_4
	lowercase						:		List[str]   					=		EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}							,crop_size={"height": crop_size, "width": crop_size}							,resample=pillow_resamplings["bicubic"]							,)
	lowercase						:		Union[str, Any]   					=		processor(images=A							,return_tensors="pt"							).pixel_values

	# original processing pipeline
	lowercase						:		Tuple   					=		Compose(
	    [
	        Resize(A							,interpolation=pillow_resamplings["bicubic"]							),
	        CenterCrop(A							),
	        ToTensor(),
	        Normalize(A							,A							),
	    ]							)
	lowercase						:		List[Any]   					=		image_transforms(A							).unsqueeze(0							)

	assert torch.allclose(A							,A							)

	lowercase						:		Union[str, Any]   					=		model(A							)
	lowercase						:		Any   					=		outputs.logits

	lowercase						:		List[str]   					=		(1, 1_0_0_0)

	if "l1" in model_name:
		lowercase						:		Any   					=		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, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		lowercase						:		List[Any]   					=		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, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		lowercase						:		Optional[int]   					=		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(A							).mkdir(exist_ok=A							)
	model.save_pretrained(A							)
	print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)
	processor.save_pretrained(A							)
	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=A							,)
		processor.push_to_hub(
		    repo_id=F'''Bearnardd/{pytorch_dump_path}'''							,commit_message="Add image processor"							,use_temp_dir=A							,)


if __name__ == "__main__":
					lowerCAmelCase   : Tuple             =				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)

					lowerCAmelCase   : Optional[int]             =				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,
					)


