



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

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

UpperCAmelCase      						=       {
    '''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 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		str				)     ->		str:







	"""simple docstring"""
	lowerCAmelCase       =      EfficientNetConfig()
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""hidden_dim"""]
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""width_coef"""]
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""depth_coef"""]
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""image_size"""]
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""dropout_rate"""]
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""dw_padding"""]

	lowerCAmelCase       =      """huggingface/label-files"""
	lowerCAmelCase       =      """imagenet-1k-id2label.json"""
	lowerCAmelCase       =      1_000
	lowerCAmelCase       =      json.load(open(hf_hub_download(a__				,						a__				,						repo_type="""dataset"""				)				,						"""r"""				)				)
	lowerCAmelCase       =      {int(a__				): v for k, v in idalabel.items()}

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






def 						_snake_case						(   )     ->		int:







	"""simple docstring"""
	lowerCAmelCase       =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
	lowerCAmelCase       =      Image.open(requests.get(a__				,						stream=a__				).raw				)
	return im






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		Any				)     ->		Optional[int]:







	"""simple docstring"""
	lowerCAmelCase       =      CONFIG_MAP[model_name]["""image_size"""]
	lowerCAmelCase       =      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 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		List[str]				)     ->		str:







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

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

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






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		List[str]				,						_SCREAMING_SNAKE_CASE	:		str				,						_SCREAMING_SNAKE_CASE	:		Any				)     ->		Optional[int]:







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

		lowerCAmelCase       =      key_mapping[key]
		if "_conv" in key and "kernel" in key:
			lowerCAmelCase       =      torch.from_numpy(a__				).permute(3				,						2				,						0				,						1				)
		elif "depthwise_kernel" in key:
			lowerCAmelCase       =      torch.from_numpy(a__				).permute(2				,						3				,						0				,						1				)
		elif "kernel" in key:
			lowerCAmelCase       =      torch.from_numpy(np.transpose(a__				)				)
		else:
			lowerCAmelCase       =      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 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		Optional[Any]				,						_SCREAMING_SNAKE_CASE	:		Dict				,						_SCREAMING_SNAKE_CASE	:		Any				,						_SCREAMING_SNAKE_CASE	:		Dict				)     ->		Tuple:







	"""simple docstring"""
	lowerCAmelCase       =      model_classes[model_name](
	    include_top=a__				,						weights="""imagenet"""				,						input_tensor=a__				,						input_shape=a__				,						pooling=a__				,						classes=1_000				,						classifier_activation="""softmax"""				,						)

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

	# Load HuggingFace model
	lowerCAmelCase       =      get_efficientnet_config(a__				)
	lowerCAmelCase       =      EfficientNetForImageClassification(a__				).eval()
	lowerCAmelCase       =      hf_model.state_dict()

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

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

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

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


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

				UpperCAmelCase      						=       parser.parse_args()
				convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


from math import sqrt
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 1000000       )					->				int:
	__A :			int		    =			0
	__A :			int		    =			0
	__A :			int

	while num_cuboids <= limit:
		max_cuboid_size += 1
		for sum_shortest_sides in range(2			,2 * max_cuboid_size + 1       ):
			if sqrt(sum_shortest_sides**2 + max_cuboid_size**2       ).is_integer():
				num_cuboids += (
				    min(a__			,sum_shortest_sides // 2       )
				    - max(1			,sum_shortest_sides - max_cuboid_size       )
				    + 1
				)

	return max_cuboid_size


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





