





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

_SCREAMING_SNAKE_CASE     :   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,
}

_SCREAMING_SNAKE_CASE     :   Dict					=    {
    "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_			:  str				):
			__lowercase										=							EfficientNetConfig()
			__lowercase										=							CONFIG_MAP[model_name]['''hidden_dim''']
			__lowercase										=							CONFIG_MAP[model_name]['''width_coef''']
			__lowercase										=							CONFIG_MAP[model_name]['''depth_coef''']
			__lowercase										=							CONFIG_MAP[model_name]['''image_size''']
			__lowercase										=							CONFIG_MAP[model_name]['''dropout_rate''']
			__lowercase										=							CONFIG_MAP[model_name]['''dw_padding''']

			__lowercase										=							'''huggingface/label-files'''
			__lowercase										=							'''imagenet-1k-id2label.json'''
			__lowercase										=							1_0_0_0
			__lowercase										=							json.load(open(hf_hub_download(lowerCamelCase_		,       lowerCamelCase_		,       repo_type='''dataset'''				)		,       '''r'''				)				)
			__lowercase										=							{int(lowerCamelCase_				): v for k, v in idalabel.items()}

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




def    _lowerCAmelCase						(				):
			__lowercase										=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase										=							Image.open(requests.get(lowerCamelCase_		,       stream=lowerCamelCase_				).raw				)
			return im




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							CONFIG_MAP[model_name]['''image_size''']
			__lowercase										=							EfficientNetImageProcessor(
			    size={'''height''': size, '''width''': size}		,       image_mean=[0.4_85, 0.4_56, 0.4_06]		,       image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]		,       do_center_crop=lowerCamelCase_		,       )
			return preprocessor




def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple				):
			__lowercase										=							[v.split('''_'''				)[0].split('''block'''				)[1] for v in original_param_names if v.startswith('''block'''				)]
			__lowercase										=							sorted(set(lowerCamelCase_				)				)
			__lowercase										=							len(lowerCamelCase_				)
			__lowercase										=							{b: str(lowerCamelCase_				) for b, i in zip(lowerCamelCase_		,       range(lowerCamelCase_				)				)}

			__lowercase										=							[]
			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:
						__lowercase										=							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''')				)

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

			__lowercase										=							'''classifier.weight'''
			__lowercase										=							'''classifier.bias'''
			return key_mapping




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Optional[Any]				):



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

						__lowercase										=							key_mapping[key]
						if "_conv" in key and "kernel" in key:
									__lowercase										=							torch.from_numpy(lowerCamelCase_				).permute(3		,       2		,       0		,       1				)
						elif "depthwise_kernel" in key:
									__lowercase										=							torch.from_numpy(lowerCamelCase_				).permute(2		,       3		,       0		,       1				)
						elif "kernel" in key:
									__lowercase										=							torch.from_numpy(np.transpose(lowerCamelCase_				)				)
						else:
									__lowercase										=							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_			:  int		,       lowerCamelCase_			:  Any		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  List[str]				):
			__lowercase										=							model_classes[model_name](
			    include_top=lowerCamelCase_		,       weights='''imagenet'''		,       input_tensor=lowerCamelCase_		,       input_shape=lowerCamelCase_		,       pooling=lowerCamelCase_		,       classes=1_0_0_0		,       classifier_activation='''softmax'''		,       )

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

			# Load HuggingFace model
			__lowercase										=							get_efficientnet_config(lowerCamelCase_				)
			__lowercase										=							EfficientNetForImageClassification(lowerCamelCase_				).eval()
			__lowercase										=							hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print('''Converting parameters...'''				)
			__lowercase										=							rename_keys(lowerCamelCase_				)
			replace_params(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)

			# Initialize preprocessor and preprocess input image
			__lowercase										=							convert_image_processor(lowerCamelCase_				)
			__lowercase										=							preprocessor(images=prepare_img()		,       return_tensors='''pt'''				)

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

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


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE     :   Any					=    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''')

	_SCREAMING_SNAKE_CASE     :   Dict					=    parser.parse_args()
	convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)












'''simple docstring'''



import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
	# Initialising the CNN
	# (Sequential- Building the model layer by layer)
	_SCREAMING_SNAKE_CASE					=    models.Sequential()

	# Step 1 - Convolution
	# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
	# (3,3) is the kernel size (filter matrix)
	classifier.add(
	    layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='''relu''')
	)

	# Step 2 - Pooling
	classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

	# Adding a second convolutional layer
	classifier.add(layers.ConvaD(3_2, (3, 3), activation='''relu'''))
	classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

	# Step 3 - Flattening
	classifier.add(layers.Flatten())

	# Step 4 - Full connection
	classifier.add(layers.Dense(units=1_2_8, activation='''relu'''))
	classifier.add(layers.Dense(units=1, activation='''sigmoid'''))

	# Compiling the CNN
	classifier.compile(
	    optimizer='''adam''', loss='''binary_crossentropy''', metrics=['''accuracy''']
	)

	# Part 2 - Fitting the CNN to the images

	# Load Trained model weights

	# from keras.models import load_model
	# regressor=load_model('cnn.h5')

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(
	    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
	)

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

	_SCREAMING_SNAKE_CASE					=    train_datagen.flow_from_directory(
	    '''dataset/training_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	_SCREAMING_SNAKE_CASE					=    test_datagen.flow_from_directory(
	    '''dataset/test_set''', target_size=(6_4, 6_4), batch_size=3_2, class_mode='''binary'''
	)

	classifier.fit_generator(
	    training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
	)

	classifier.save('''cnn.h5''')

	# Part 3 - Making new predictions

	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.load_img(
	    '''dataset/single_prediction/image.png''', target_size=(6_4, 6_4)
	)
	_SCREAMING_SNAKE_CASE					=    tf.keras.preprocessing.image.img_to_array(test_image)
	_SCREAMING_SNAKE_CASE					=    np.expand_dims(test_image, axis=0)
	_SCREAMING_SNAKE_CASE					=    classifier.predict(test_image)
	# training_set.class_indices
	if result[0][0] == 0:
		_SCREAMING_SNAKE_CASE					=    '''Normal'''
	if result[0][0] == 1:
		_SCREAMING_SNAKE_CASE					=    '''Abnormality detected'''





