


'''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()
lowerCAmelCase				:List[str]        =      logging.get_logger(__name__)

lowerCAmelCase				:str        =      {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

lowerCAmelCase				:Tuple        =      {
    '''b0''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_2_4,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_4_0,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [1_6],
    },
    '''b2''': {
        '''hidden_dim''': 1_4_0_8,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_6_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 1_6],
    },
    '''b3''': {
        '''hidden_dim''': 1_5_3_6,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_0_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 1_8],
    },
    '''b4''': {
        '''hidden_dim''': 1_7_9_2,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_8_0,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_0_4_8,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_5_6,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [1_3, 2_7],
    },
    '''b6''': {
        '''hidden_dim''': 2_3_0_4,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_2_8,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [3_1],
    },
    '''b7''': {
        '''hidden_dim''': 2_5_6_0,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_0_0,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [1_8],
    },
}





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					EfficientNetConfig()
	__magic_name__	:   int					     =					CONFIG_MAP[model_name]['hidden_dim']
	__magic_name__	:   Dict					     =					CONFIG_MAP[model_name]['width_coef']
	__magic_name__	:   str					     =					CONFIG_MAP[model_name]['depth_coef']
	__magic_name__	:   Union[str, Any]					     =					CONFIG_MAP[model_name]['image_size']
	__magic_name__	:   str					     =					CONFIG_MAP[model_name]['dropout_rate']
	__magic_name__	:   List[str]					     =					CONFIG_MAP[model_name]['dw_padding']

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

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





def lowerCamelCase      (      ):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	__magic_name__	:   int					     =					CONFIG_MAP[model_name]['image_size']
	__magic_name__	:   List[str]					     =					EfficientNetImageProcessor(
	    size={'height': size, 'width': size}		,		image_mean=[0.485, 0.456, 0.406]		,		image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]		,		do_center_crop=lowerCAmelCase		,		)
	return preprocessor





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







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

	__magic_name__	:   Optional[int]					     =					[]
	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__	:   Union[str, Any]					     =					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__	:   Optional[Any]					     =					{}
	for item in rename_keys:
		if item[0] in original_param_names:
			__magic_name__	:   Dict					     =					'efficientnet.' + item[1]

	__magic_name__	:   int					     =					'classifier.weight'
	__magic_name__	:   List[Any]					     =					'classifier.bias'
	return key_mapping





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Any		):





	"""simple docstring"""







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

		__magic_name__	:   Union[str, Any]					     =					key_mapping[key]
		if "_conv" in key and "kernel" in key:
			__magic_name__	:   Dict					     =					torch.from_numpy(lowerCAmelCase		).permute(3		,		2		,		0		,		1		)
		elif "depthwise_kernel" in key:
			__magic_name__	:   List[Any]					     =					torch.from_numpy(lowerCAmelCase		).permute(2		,		3		,		0		,		1		)
		elif "kernel" in key:
			__magic_name__	:   Dict					     =					torch.from_numpy(np.transpose(lowerCAmelCase		)		)
		else:
			__magic_name__	:   Tuple					     =					torch.from_numpy(lowerCAmelCase		)

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





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					model_classes[model_name](
	    include_top=lowerCAmelCase		,		weights='imagenet'		,		input_tensor=lowerCAmelCase		,		input_shape=lowerCAmelCase		,		pooling=lowerCAmelCase		,		classes=1000		,		classifier_activation='softmax'		,		)

	__magic_name__	:   List[Any]					     =					original_model.trainable_variables
	__magic_name__	:   Any					     =					original_model.non_trainable_variables
	__magic_name__	:   Any					     =					{param.name: param.numpy() for param in tf_params}
	for param in tf_non_train_params:
		__magic_name__	:   Tuple					     =					param.numpy()
	__magic_name__	:   Optional[int]					     =					list(tf_params.keys()		)

	# Load HuggingFace model
	__magic_name__	:   int					     =					get_efficientnet_config(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					EfficientNetForImageClassification(lowerCAmelCase		).eval()
	__magic_name__	:   List[str]					     =					hf_model.state_dict()

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

	# Initialize preprocessor and preprocess input image
	__magic_name__	:   Tuple					     =					convert_image_processor(lowerCAmelCase		)
	__magic_name__	:   Dict					     =					preprocessor(images=prepare_img()		,		return_tensors='pt'		)

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		__magic_name__	:   Optional[int]					     =					hf_model(**lowerCAmelCase		)
	__magic_name__	:   Any					     =					outputs.logits.detach().numpy()

	# Original model inference
	__magic_name__	:   Any					     =					False
	__magic_name__	:   int					     =					CONFIG_MAP[model_name]['image_size']
	__magic_name__	:   Optional[int]					     =					prepare_img().resize((image_size, image_size)		,		resample=PIL.Image.NEAREST		)
	__magic_name__	:   Dict					     =					image.img_to_array(lowerCAmelCase		)
	__magic_name__	:   str					     =					np.expand_dims(lowerCAmelCase		,		axis=0		)
	__magic_name__	:   Any					     =					original_model.predict(lowerCAmelCase		)

	# Check whether original and HF model outputs match  -> np.allclose
	assert np.allclose(lowerCAmelCase		,		lowerCAmelCase		,		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(lowerCAmelCase		):
			os.mkdir(lowerCAmelCase		)
		# Save converted model and image processor
		hf_model.save_pretrained(lowerCAmelCase		)
		preprocessor.save_pretrained(lowerCAmelCase		)

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


if __name__ == "__main__":
							lowerCAmelCase				:int        =      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''')

							lowerCAmelCase				:Union[str, Any]        =      parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		lowerCAmelCase		) and isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   str					     =					len(set_a.intersection(lowerCAmelCase		)		)

		if alternative_union:
			__magic_name__	:   List[str]					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
		else:
			__magic_name__	:   Any					     =					len(set_a.union(lowerCAmelCase		)		)

		return intersection / union

	if isinstance(lowerCAmelCase		,		(list, tuple)		) and isinstance(lowerCAmelCase		,		(list, tuple)		):
		__magic_name__	:   str					     =					[element for element in set_a if element in set_b]

		if alternative_union:
			__magic_name__	:   Dict					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
			return len(lowerCAmelCase		) / union
		else:
			__magic_name__	:   Any					     =					set_a + [element for element in set_b if element not in set_a]
			return len(lowerCAmelCase		) / len(lowerCAmelCase		)

		return len(lowerCAmelCase		) / len(lowerCAmelCase		)
	return None


if __name__ == "__main__":
							lowerCAmelCase				:Dict        =      {'''a''', '''b''', '''c''', '''d''', '''e'''}
							lowerCAmelCase				:Tuple        =      {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
							print(jaccard_similarity(set_a, set_b))