




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   :	int       =    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   :	List[Any]       =    {
    "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 				_UpperCAmelCase	(UpperCamelCase_			:			Dict							):
				'''simple docstring'''
				_lowerCAmelCase     :     Optional[int]              =   EfficientNetConfig()
				_lowerCAmelCase     :     Union[str, Any]              =   CONFIG_MAP[model_name]["""hidden_dim"""]
				_lowerCAmelCase     :     Any              =   CONFIG_MAP[model_name]["""width_coef"""]
				_lowerCAmelCase     :     Union[str, Any]              =   CONFIG_MAP[model_name]["""depth_coef"""]
				_lowerCAmelCase     :     Union[str, Any]              =   CONFIG_MAP[model_name]["""image_size"""]
				_lowerCAmelCase     :     Any              =   CONFIG_MAP[model_name]["""dropout_rate"""]
				_lowerCAmelCase     :     List[str]              =   CONFIG_MAP[model_name]["""dw_padding"""]

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

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




def 				_UpperCAmelCase	():
				'''simple docstring'''
				_lowerCAmelCase     :     Optional[int]              =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
				_lowerCAmelCase     :     Any              =   Image.open(requests.get(__lowerCamelCase   ,     stream=__lowerCamelCase							).raw							)
				return im




def 				_UpperCAmelCase	(UpperCamelCase_			:			List[Any]							):
				'''simple docstring'''
				_lowerCAmelCase     :     Optional[int]              =   CONFIG_MAP[model_name]["""image_size"""]
				_lowerCAmelCase     :     List[Any]              =   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=__lowerCamelCase   ,     )
				return preprocessor




def 				_UpperCAmelCase	(UpperCamelCase_			:			Optional[int]							):
				'''simple docstring'''
				_lowerCAmelCase     :     Optional[int]              =   [v.split("""_"""							)[0].split("""block"""							)[1] for v in original_param_names if v.startswith("""block"""							)]
				_lowerCAmelCase     :     List[str]              =   sorted(set(__lowerCamelCase							)							)
				_lowerCAmelCase     :     Any              =   len(__lowerCamelCase							)
				_lowerCAmelCase     :     Optional[int]              =   {b: str(__lowerCamelCase							) for b, i in zip(__lowerCamelCase   ,     range(__lowerCamelCase							)							)}

				_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     :     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""")							)

				_lowerCAmelCase     :     List[Any]              =   {}
				for item in rename_keys:
								if item[0] in original_param_names:
												_lowerCAmelCase     :     int              =   """efficientnet.""" + item[1]

				_lowerCAmelCase     :     Dict              =   """classifier.weight"""
				_lowerCAmelCase     :     str              =   """classifier.bias"""
				return key_mapping




def 				_UpperCAmelCase	(UpperCamelCase_			:			Union[str, Any]   ,     UpperCamelCase_			:			Dict   ,     UpperCamelCase_			:			int							):
				'''simple docstring'''
				for key, value in tf_params.items():
								if "normalization" in key:
												continue

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

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

				# Load HuggingFace model
				_lowerCAmelCase     :     str              =   get_efficientnet_config(__lowerCamelCase							)
				_lowerCAmelCase     :     Union[str, Any]              =   EfficientNetForImageClassification(__lowerCamelCase							).eval()
				_lowerCAmelCase     :     Tuple              =   hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print("""Converting parameters..."""							)
				_lowerCAmelCase     :     Any              =   rename_keys(__lowerCamelCase							)
				replace_params(__lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase							)

				# Initialize preprocessor and preprocess input image
				_lowerCAmelCase     :     List[str]              =   convert_image_processor(__lowerCamelCase							)
				_lowerCAmelCase     :     Dict              =   preprocessor(images=prepare_img()   ,     return_tensors="""pt"""							)

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								_lowerCAmelCase     :     Dict              =   hf_model(**__lowerCamelCase							)
				_lowerCAmelCase     :     Dict              =   outputs.logits.detach().numpy()

				# Original model inference
				_lowerCAmelCase     :     str              =   False
				_lowerCAmelCase     :     int              =   CONFIG_MAP[model_name]["""image_size"""]
				_lowerCAmelCase     :     Union[str, Any]              =   prepare_img().resize((image_size, image_size)   ,     resample=PIL.Image.NEAREST							)
				_lowerCAmelCase     :     Dict              =   image.img_to_array(__lowerCamelCase							)
				_lowerCAmelCase     :     Any              =   np.expand_dims(__lowerCamelCase   ,     axis=0							)
				_lowerCAmelCase     :     str              =   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..."							)
								_lowerCAmelCase     :     str              =   F"efficientnet-{model_name}"
								preprocessor.push_to_hub(__lowerCamelCase							)
								hf_model.push_to_hub(__lowerCamelCase							)


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







import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__UpperCamelCase   :			Optional[int]          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Union[str, Any]						, *__magic_name__		:int						, **__magic_name__		:str							):







								'''simple docstring'''







								warnings.warn(
								    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
								    """ Please use LayoutLMv2ImageProcessor instead."""						, __magic_name__						, )
								super().__init__(*__magic_name__						, **__magic_name__							)

