




"""simple docstring"""
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_				:    List[Any]										=     logging.get_logger(__name__)

a_				:    Any										=     {
    '''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_				:    List[str]										=     {
    '''b0''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_24,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_40,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 14_08,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_60,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 15_36,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_00,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 17_92,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_80,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 20_48,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_56,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 23_04,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_28,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 25_60,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_00,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def 						UpperCAmelCase				(			A__:    Dict     )  ->     Optional[Any]:
			__lowerCamelCase					:	List[Any]	       =				EfficientNetConfig()
			__lowerCamelCase					:	str	       =				CONFIG_MAP[model_name]['hidden_dim']
			__lowerCamelCase					:	int	       =				CONFIG_MAP[model_name]['width_coef']
			__lowerCamelCase					:	List[Any]	       =				CONFIG_MAP[model_name]['depth_coef']
			__lowerCamelCase					:	Optional[int]	       =				CONFIG_MAP[model_name]['image_size']
			__lowerCamelCase					:	Tuple	       =				CONFIG_MAP[model_name]['dropout_rate']
			__lowerCamelCase					:	int	       =				CONFIG_MAP[model_name]['dw_padding']

			__lowerCamelCase					:	Tuple	       =				'huggingface/label-files'
			__lowerCamelCase					:	Dict	       =				'imagenet-1k-id2label.json'
			__lowerCamelCase					:	int	       =				1000
			__lowerCamelCase					:	Any	       =				json.load(open(hf_hub_download(A__							,				A__							,				repo_type='dataset'     )							,				'r'     )     )
			__lowerCamelCase					:	List[str]	       =				{int(A__     ): v for k, v in idalabel.items()}

			__lowerCamelCase					:	Optional[int]	       =				idalabel
			__lowerCamelCase					:	List[str]	       =				{v: k for k, v in idalabel.items()}
			return config





def 						UpperCAmelCase				(			)  ->     Optional[Any]:
			__lowerCamelCase					:	Any	       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
			__lowerCamelCase					:	List[str]	       =				Image.open(requests.get(A__							,				stream=A__     ).raw     )
			return im





def 						UpperCAmelCase				(			A__:    List[Any]     )  ->     str:
			__lowerCamelCase					:	Any	       =				CONFIG_MAP[model_name]['image_size']
			__lowerCamelCase					:	List[str]	       =				EfficientNetImageProcessor(
			    size={'height': size, 'width': size}							,				image_mean=[0.485, 0.456, 0.406]							,				image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]							,				do_center_crop=A__							,				)
			return preprocessor





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Tuple:
			__lowerCamelCase					:	Any	       =				[v.split('_'     )[0].split('block'     )[1] for v in original_param_names if v.startswith('block'     )]
			__lowerCamelCase					:	Dict	       =				sorted(set(A__     )     )
			__lowerCamelCase					:	Dict	       =				len(A__     )
			__lowerCamelCase					:	Optional[Any]	       =				{b: str(A__     ) for b, i in zip(A__							,				range(A__     )     )}

			__lowerCamelCase					:	Any	       =				[]
			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:
						__lowerCamelCase					:	Dict	       =				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')     )

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

			__lowerCamelCase					:	str	       =				'classifier.weight'
			__lowerCamelCase					:	Any	       =				'classifier.bias'
			return key_mapping





def 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    Any							,				A__:    Optional[int]     )  ->     List[Any]:
			for key, value in tf_params.items():
						if "normalization" in key:
									continue

						__lowerCamelCase					:	Tuple	       =				key_mapping[key]
						if "_conv" in key and "kernel" in key:
									__lowerCamelCase					:	Optional[int]	       =				torch.from_numpy(A__     ).permute(3							,				2							,				0							,				1     )
						elif "depthwise_kernel" in key:
									__lowerCamelCase					:	List[Any]	       =				torch.from_numpy(A__     ).permute(2							,				3							,				0							,				1     )
						elif "kernel" in key:
									__lowerCamelCase					:	List[str]	       =				torch.from_numpy(np.transpose(A__     )     )
						else:
									__lowerCamelCase					:	int	       =				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 						UpperCAmelCase				(			A__:    List[str]							,				A__:    List[Any]							,				A__:    Dict							,				A__:    Dict     )  ->     List[str]:
			__lowerCamelCase					:	List[str]	       =				model_classes[model_name](
			    include_top=A__							,				weights='imagenet'							,				input_tensor=A__							,				input_shape=A__							,				pooling=A__							,				classes=1000							,				classifier_activation='softmax'							,				)

			__lowerCamelCase					:	Optional[Any]	       =				original_model.trainable_variables
			__lowerCamelCase					:	Union[str, Any]	       =				original_model.non_trainable_variables
			__lowerCamelCase					:	int	       =				{param.name: param.numpy() for param in tf_params}
			for param in tf_non_train_params:
						__lowerCamelCase					:	str	       =				param.numpy()
			__lowerCamelCase					:	int	       =				list(tf_params.keys()     )

			# Load HuggingFace model
			__lowerCamelCase					:	Optional[Any]	       =				get_efficientnet_config(A__     )
			__lowerCamelCase					:	Optional[Any]	       =				EfficientNetForImageClassification(A__     ).eval()
			__lowerCamelCase					:	List[str]	       =				hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print('Converting parameters...'     )
			__lowerCamelCase					:	int	       =				rename_keys(A__     )
			replace_params(A__							,				A__							,				A__     )

			# Initialize preprocessor and preprocess input image
			__lowerCamelCase					:	List[Any]	       =				convert_image_processor(A__     )
			__lowerCamelCase					:	Optional[Any]	       =				preprocessor(images=prepare_img()							,				return_tensors='pt'     )

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						__lowerCamelCase					:	Dict	       =				hf_model(**A__     )
			__lowerCamelCase					:	Optional[int]	       =				outputs.logits.detach().numpy()

			# Original model inference
			__lowerCamelCase					:	str	       =				False
			__lowerCamelCase					:	Tuple	       =				CONFIG_MAP[model_name]['image_size']
			__lowerCamelCase					:	str	       =				prepare_img().resize((image_size, image_size)							,				resample=PIL.Image.NEAREST     )
			__lowerCamelCase					:	Optional[int]	       =				image.img_to_array(A__     )
			__lowerCamelCase					:	Dict	       =				np.expand_dims(A__							,				axis=0     )
			__lowerCamelCase					:	Dict	       =				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...'''     )
						__lowerCamelCase					:	List[str]	       =				f'''efficientnet-{model_name}'''
						preprocessor.push_to_hub(A__     )
						hf_model.push_to_hub(A__     )


if __name__ == "__main__":
					a_				:    Any										=     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_				:    List[str]										=     parser.parse_args()
					convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



