

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor

class 				__UpperCamelCase				(					unittest.TestCase			):







	def __init__(   self: List[str]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Dict=7  , __UpperCamelCase: Any=3  , __UpperCamelCase: Optional[Any]=18  , __UpperCamelCase: Optional[Any]=30  , __UpperCamelCase: Dict=4_00  , __UpperCamelCase: Optional[Any]=True  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: List[str]=True  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: Optional[Any]=True  , __UpperCamelCase: Tuple=[0.5, 0.5, 0.5]  , __UpperCamelCase: Tuple=[0.5, 0.5, 0.5]  , __UpperCamelCase: str=False  , ):

						'''simple docstring'''





						__magic_name__							=    size if size is not None else {'height': 20, 'width': 20}
						__magic_name__							=    crop_size if crop_size is not None else {'height': 18, 'width': 18}
						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    num_channels
						__magic_name__							=    image_size
						__magic_name__							=    min_resolution
						__magic_name__							=    max_resolution
						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean
						__magic_name__							=    image_std
						__magic_name__							=    do_reduce_labels







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_reduce_labels": self.do_reduce_labels,
						}




def 					_lowercase     (				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    load_dataset('hf-internal-testing/fixtures_ade20k'					,split='test'				)

					__magic_name__							=    Image.open(dataset[0]['file']				)
					__magic_name__							=    Image.open(dataset[1]['file']				)

					return image, map




def 					_lowercase     (				)				->	Union[str, Any]:




					'''simple docstring'''







					__magic_name__							=    load_dataset('hf-internal-testing/fixtures_ade20k'					,split='test'				)

					__magic_name__							=    Image.open(ds[0]['file']				)
					__magic_name__							=    Image.open(ds[1]['file']				)
					__magic_name__							=    Image.open(ds[2]['file']				)
					__magic_name__							=    Image.open(ds[3]['file']				)

					return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Any       				=  BeitImageProcessor if is_vision_available() else None







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    BeitImageProcessingTester(self      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_resize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'size'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_center_crop'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'center_crop'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_normalize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'image_mean'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'image_std'      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size  , {'height': 20, 'width': 20}      )
						self.assertEqual(image_processor.crop_size  , {'height': 18, 'width': 18}      )
						self.assertEqual(image_processor.do_reduce_labels  , __UpperCamelCase      )

						__magic_name__							=    self.image_processing_class.from_dict(
						    self.image_processor_dict  , size=42  , crop_size=84  , reduce_labels=__UpperCamelCase      )
						self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}      )
						self.assertEqual(image_processor.crop_size  , {'height': 84, 'width': 84}      )
						self.assertEqual(image_processor.do_reduce_labels  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						pass







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , Image.Image      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , numpify=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , np.ndarray      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , torchify=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , torch.Tensor      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , torchify=__UpperCamelCase      )
						__magic_name__							=    []
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , torch.Tensor      )
											maps.append(torch.zeros(image.shape[-2:]      ).long()      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , maps[0]  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        1,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )

						# Test not batched input (PIL images)
						__magic_name__,	__magic_name__							=    prepare_semantic_single_inputs()

						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        1,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )

						# Test batched input (PIL images)
						__magic_name__,	__magic_name__							=    prepare_semantic_batch_inputs()

						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        2,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        2,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )

						# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
						__magic_name__,	__magic_name__							=    prepare_semantic_single_inputs()
						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 1_50      )

						__magic_name__							=    True
						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )







import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

A__						=							{
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

A__						=							{
    "b0": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}




def 					_lowercase     (				a_				:					List[Any]				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    EfficientNetConfig()
					__magic_name__							=    CONFIG_MAP[model_name]['hidden_dim']
					__magic_name__							=    CONFIG_MAP[model_name]['width_coef']
					__magic_name__							=    CONFIG_MAP[model_name]['depth_coef']
					__magic_name__							=    CONFIG_MAP[model_name]['image_size']
					__magic_name__							=    CONFIG_MAP[model_name]['dropout_rate']
					__magic_name__							=    CONFIG_MAP[model_name]['dw_padding']

					__magic_name__							=    'huggingface/label-files'
					__magic_name__							=    'imagenet-1k-id2label.json'
					__magic_name__							=    1_0_0_0
					__magic_name__							=    json.load(open(hf_hub_download(a_					,a_					,repo_type='dataset'				)					,'r'				)				)
					__magic_name__							=    {int(a_				): v for k, v in idalabel.items()}

					__magic_name__							=    idalabel
					__magic_name__							=    {v: k for k, v in idalabel.items()}
					return config




def 					_lowercase     (				)				->	int:




					'''simple docstring'''







					__magic_name__							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
					__magic_name__							=    Image.open(requests.get(a_					,stream=a_				).raw				)
					return im




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




					'''simple docstring'''







					__magic_name__							=    CONFIG_MAP[model_name]['image_size']
					__magic_name__							=    EfficientNetImageProcessor(
					    size={'height': size, 'width': size}					,image_mean=[0.485, 0.456, 0.406]					,image_std=[0.47853944, 0.4732864, 0.47434163]					,do_center_crop=a_					,)
					return preprocessor




def 					_lowercase     (				a_				:					List[str]				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    [v.split('_'				)[0].split('block'				)[1] for v in original_param_names if v.startswith('block'				)]
					__magic_name__							=    sorted(set(a_				)				)
					__magic_name__							=    len(a_				)
					__magic_name__							=    {b: str(a_				) for b, i in zip(a_					,range(a_				)				)}

					__magic_name__							=    []
					rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')				)
					rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')				)
					rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')				)
					rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')				)
					rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')				)

					for b in block_names:
										__magic_name__							=    block_name_mapping[b]
										rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight')				)
										rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight')				)
										rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias')				)
										rename_keys.append(
										    (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')				)
										rename_keys.append(
										    (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')				)
										rename_keys.append(
										    (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')				)
										rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')				)
										rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')				)
										rename_keys.append(
										    (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')				)
										rename_keys.append(
										    (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')				)

										rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')				)
										rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')				)
										rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')				)
										rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')				)
										rename_keys.append(
										    (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight')				)
										rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight')				)
										rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias')				)
										rename_keys.append(
										    (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean')				)
										rename_keys.append(
										    (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var')				)

					rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')				)
					rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')				)
					rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')				)
					rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')				)
					rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')				)

					__magic_name__							=    {}
					for item in rename_keys:
										if item[0] in original_param_names:
															__magic_name__							=    'efficientnet.' + item[1]

					__magic_name__							=    'classifier.weight'
					__magic_name__							=    'classifier.bias'
					return key_mapping




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Optional[Any]					,a_				:					List[Any]				)				->	Any:




					'''simple docstring'''







					for key, value in tf_params.items():
										if "normalization" in key:
															continue

										__magic_name__							=    key_mapping[key]
										if "_conv" in key and "kernel" in key:
															__magic_name__							=    torch.from_numpy(a_				).permute(3					,2					,0					,1				)
										elif "depthwise_kernel" in key:
															__magic_name__							=    torch.from_numpy(a_				).permute(2					,3					,0					,1				)
										elif "kernel" in key:
															__magic_name__							=    torch.from_numpy(np.transpose(a_				)				)
										else:
															__magic_name__							=    torch.from_numpy(a_				)

										# Replace HF parameters with original TF model parameters
										assert hf_params[hf_key].shape == new_hf_value.shape
										hf_params[hf_key].copy_(a_				)




@torch.no_grad()
def 					_lowercase     (				a_				:					Dict					,a_				:					Optional[Any]					,a_				:					Any					,a_				:					Dict				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    model_classes[model_name](
					    include_top=a_					,weights='imagenet'					,input_tensor=a_					,input_shape=a_					,pooling=a_					,classes=1_0_0_0					,classifier_activation='softmax'					,)

					__magic_name__							=    original_model.trainable_variables
					__magic_name__							=    original_model.non_trainable_variables
					__magic_name__							=    {param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										__magic_name__							=    param.numpy()
					__magic_name__							=    list(tf_params.keys()				)

					# Load HuggingFace model
					__magic_name__							=    get_efficientnet_config(a_				)
					__magic_name__							=    EfficientNetForImageClassification(a_				).eval()
					__magic_name__							=    hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print('Converting parameters...'				)
					__magic_name__							=    rename_keys(a_				)
					replace_params(a_					,a_					,a_				)

					# Initialize preprocessor and preprocess input image
					__magic_name__							=    convert_image_processor(a_				)
					__magic_name__							=    preprocessor(images=prepare_img()					,return_tensors='pt'				)

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										__magic_name__							=    hf_model(**a_				)
					__magic_name__							=    outputs.logits.detach().numpy()

					# Original model inference
					__magic_name__							=    False
					__magic_name__							=    CONFIG_MAP[model_name]['image_size']
					__magic_name__							=    prepare_img().resize((image_size, image_size)					,resample=PIL.Image.NEAREST				)
					__magic_name__							=    image.img_to_array(a_				)
					__magic_name__							=    np.expand_dims(a_					,axis=0				)
					__magic_name__							=    original_model.predict(a_				)

					# Check whether original and HF model outputs match  -> np.allclose
					assert np.allclose(a_					,a_					,atol=1e-3				), "The predicted logits are not the same."
					print('Model outputs match!'				)

					if save_model:
										# Create folder to save model
										if not os.path.isdir(a_				):
															os.mkdir(a_				)
										# Save converted model and image processor
										hf_model.save_pretrained(a_				)
										preprocessor.save_pretrained(a_				)

					if push_to_hub:
										# Push model and image processor to hub
										print(F'Pushing converted {model_name} to the hub...'				)
										__magic_name__							=    F'efficientnet-{model_name}'
										preprocessor.push_to_hub(a_				)
										hf_model.push_to_hub(a_				)


if __name__ == "__main__":
						A__						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="b0",
						    type=str,
						    help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="hf_model",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--save_model", action="store_true", help="Save model to local")
						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

						A__						=							parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




