
'''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()
lowercase__											=logging.get_logger(__name__)

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

lowercase__											={
    '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__						):
	a_      				=						EfficientNetConfig()
	a_      				=						CONFIG_MAP[model_name]["""hidden_dim"""]
	a_      				=						CONFIG_MAP[model_name]["""width_coef"""]
	a_      				=						CONFIG_MAP[model_name]["""depth_coef"""]
	a_      				=						CONFIG_MAP[model_name]["""image_size"""]
	a_      				=						CONFIG_MAP[model_name]["""dropout_rate"""]
	a_      				=						CONFIG_MAP[model_name]["""dw_padding"""]

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

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






def      UpperCamelCase_				(      ):
	a_      				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a_      				=						Image.open(requests.get(A__  ,      stream=A__						).raw						)
	return im






def      UpperCamelCase_				(      A__						):
	a_      				=						CONFIG_MAP[model_name]["""image_size"""]
	a_      				=						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=A__  ,      )
	return preprocessor






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

	a_      				=						[]
	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:
		a_      				=						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""")						)

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

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






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

		a_      				=						key_mapping[key]
		if "_conv" in key and "kernel" in key:
			a_      				=						torch.from_numpy(A__						).permute(3  ,      2  ,      0  ,      1						)
		elif "depthwise_kernel" in key:
			a_      				=						torch.from_numpy(A__						).permute(2  ,      3  ,      0  ,      1						)
		elif "kernel" in key:
			a_      				=						torch.from_numpy(np.transpose(A__						)						)
		else:
			a_      				=						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__  ,      A__  ,      A__  ,      A__						):
	a_      				=						model_classes[model_name](
	    include_top=A__  ,      weights="""imagenet"""  ,      input_tensor=A__  ,      input_shape=A__  ,      pooling=A__  ,      classes=10_00  ,      classifier_activation="""softmax"""  ,      )

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

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

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

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

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

	# Original model inference
	a_      				=						False
	a_      				=						CONFIG_MAP[model_name]["""image_size"""]
	a_      				=						prepare_img().resize((image_size, image_size)  ,      resample=PIL.Image.NEAREST						)
	a_      				=						image.img_to_array(A__						)
	a_      				=						np.expand_dims(A__  ,      axis=0						)
	a_      				=						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...'''						)
		a_      				=						F'''efficientnet-{model_name}'''
		preprocessor.push_to_hub(A__						)
		hf_model.push_to_hub(A__						)


if __name__ == "__main__":
		lowercase__											=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')

		lowercase__											=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__ = 1_00						):
	a_      				=						sum(i * i for i in range(1  ,      n + 1						)						)
	a_      				=						int(math.pow(sum(range(1  ,      n + 1						)						)  ,      2						)						)
	return square_of_sum - sum_of_squares


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