



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 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       EfficientNetConfig()
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""hidden_dim"""]
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""width_coef"""]
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""depth_coef"""]
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""image_size"""]
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""dropout_rate"""]
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""dw_padding"""]

						UpperCAmelCase_=       """huggingface/label-files"""
						UpperCAmelCase_=       """imagenet-1k-id2label.json"""
						UpperCAmelCase_=       10_00
						UpperCAmelCase_=       json.load(open(hf_hub_download(_A      ,_A      ,repo_type="""dataset"""    )      ,"""r"""    )    )
						UpperCAmelCase_=       {int(_A    ): v for k, v in idalabel.items()}

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



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_=       Image.open(requests.get(_A      ,stream=_A    ).raw    )
						return im



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       CONFIG_MAP[model_name]["""image_size"""]
						UpperCAmelCase_=       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 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			str:






						'''simple docstring'''



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

						UpperCAmelCase_=       []
						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:
												UpperCAmelCase_=       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""")    )

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

						UpperCAmelCase_=       """classifier.weight"""
						UpperCAmelCase_=       """classifier.bias"""
						return key_mapping



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : str    )				->			Optional[Any]:






						'''simple docstring'''



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

												UpperCAmelCase_=       key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		UpperCAmelCase_=       torch.from_numpy(_A    ).permute(3      ,2      ,0      ,1    )
												elif "depthwise_kernel" in key:
																		UpperCAmelCase_=       torch.from_numpy(_A    ).permute(2      ,3      ,0      ,1    )
												elif "kernel" in key:
																		UpperCAmelCase_=       torch.from_numpy(np.transpose(_A    )    )
												else:
																		UpperCAmelCase_=       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 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Union[str, Any]    )				->			Any:






						'''simple docstring'''



						UpperCAmelCase_=       model_classes[model_name](
						    include_top=_A      ,weights="""imagenet"""      ,input_tensor=_A      ,input_shape=_A      ,pooling=_A      ,classes=10_00      ,classifier_activation="""softmax"""      ,)

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

						# Load HuggingFace model
						UpperCAmelCase_=       get_efficientnet_config(_A    )
						UpperCAmelCase_=       EfficientNetForImageClassification(_A    ).eval()
						UpperCAmelCase_=       hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("""Converting parameters..."""    )
						UpperCAmelCase_=       rename_keys(_A    )
						replace_params(_A      ,_A      ,_A    )

						# Initialize preprocessor and preprocess input image
						UpperCAmelCase_=       convert_image_processor(_A    )
						UpperCAmelCase_=       preprocessor(images=prepare_img()      ,return_tensors="""pt"""    )

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

						# Original model inference
						UpperCAmelCase_=       False
						UpperCAmelCase_=       CONFIG_MAP[model_name]["""image_size"""]
						UpperCAmelCase_=       prepare_img().resize((image_size, image_size)      ,resample=PIL.Image.NEAREST    )
						UpperCAmelCase_=       image.img_to_array(_A    )
						UpperCAmelCase_=       np.expand_dims(_A      ,axis=0    )
						UpperCAmelCase_=       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..."""    )
												UpperCAmelCase_=       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)




from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




