



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)
		snake_case    :						Optional[int]  =						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')

		snake_case    :						List[str]  =						tf.keras.preprocessing.image.ImageDataGenerator(
		    rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
		)

		snake_case    :						Tuple  =						tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

		snake_case    :						List[Any]  =						train_datagen.flow_from_directory(
		    'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
		)

		snake_case    :						Dict  =						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

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




import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							List[str]						    = checkpoint

						A__	:							Dict						    = {}

						A__	:							Union[str, Any]						    = vae_state_dict["encoder.conv_in.weight"]
						A__	:							int						    = vae_state_dict["encoder.conv_in.bias"]
						A__	:							List[Any]						    = vae_state_dict["encoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.conv_out.bias"]
						A__	:							int						    = vae_state_dict["encoder.norm_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.norm_out.bias"]

						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_in.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.conv_in.bias"]
						A__	:							str						    = vae_state_dict["decoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_out.bias"]
						A__	:							Union[str, Any]						    = vae_state_dict["decoder.norm_out.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.norm_out.bias"]

						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.bias"]
						A__	:							Dict						    = vae_state_dict["post_quant_conv.weight"]
						A__	:							Union[str, Any]						    = vae_state_dict["post_quant_conv.bias"]

						# Retrieves the keys for the encoder down blocks only
						A__	:							str						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "encoder.down" in layer}       )
						A__	:							Optional[int]						    = {
						    layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						# Retrieves the keys for the decoder up blocks only
						A__	:							Any						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "decoder.up" in layer}       )
						A__	:							Any						    = {
						    layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						for i in range(__lowercase       ):
												A__	:							Any						    = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

												if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
																		A__	:							Any						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.weight'       )
																		A__	:							Optional[int]						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.bias'       )

												A__	:							Any						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "encoder.mid.block" in key]
						A__	:							Any						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Optional[int]						    = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

												A__	:							Optional[Any]						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							str						    = [key for key in vae_state_dict if "encoder.mid.attn" in key]
						A__	:							List[Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							Optional[int]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )

						for i in range(__lowercase       ):
												A__	:							Dict						    = num_up_blocks - 1 - i
												A__	:							int						    = [
												    key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
												]

												if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
																		A__	:							Tuple						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.weight'
																		]
																		A__	:							List[Any]						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.bias'
																		]

												A__	:							str						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							List[str]						    = {"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "decoder.mid.block" in key]
						A__	:							Optional[int]						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Union[str, Any]						    = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

												A__	:							Dict						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							Optional[Any]						    = [key for key in vae_state_dict if "decoder.mid.attn" in key]
						A__	:							Union[str, Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							List[str]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )
						return new_checkpoint
def 	snake_case__       (					__lowercase							,     __lowercase							,     )		->		Tuple:







						"""simple docstring"""

						A__	:							List[str]						    = requests.get(
						    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"       )
						A__	:							Dict						    = io.BytesIO(r.content       )

						A__	:							str						    = OmegaConf.load(__lowercase       )
						A__	:							Any						    = 5_1_2
						A__	:							Optional[Any]						    = "cuda" if torch.cuda.is_available() else "cpu"
						if checkpoint_path.endswith("safetensors"       ):
												from safetensors import safe_open

												A__	:							str						    = {}
												with safe_open(__lowercase							,     framework="pt"							,     device="cpu"       ) as f:
																		for key in f.keys():
																								A__	:							List[str]						    = f.get_tensor(__lowercase       )
						else:
												A__	:							str						    = torch.load(__lowercase							,     map_location=__lowercase       )["state_dict"]

						# Convert the VAE model.
						A__	:							List[str]						    = create_vae_diffusers_config(__lowercase							,     image_size=__lowercase       )
						A__	:							List[Any]						    = custom_convert_ldm_vae_checkpoint(__lowercase							,     __lowercase       )

						A__	:							Dict						    = AutoencoderKL(**__lowercase       )
						vae.load_state_dict(__lowercase       )
						vae.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						List[Any]  =						argparse.ArgumentParser()

		parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

		snake_case    :						int  =						parser.parse_args()

		vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)