





'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase_   :   Tuple       =					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

lowerCAmelCase_   :   Any       =					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCAmelCase_   :   List[Any]       =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class       UpperCamelCase__  :
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default="cifar10"	,metadata={"help": "Name of a dataset from the datasets package"}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}	,)
	lowerCAmelCase__					:			Optional[str]								=						field(default=__lowerCAmelCase	,metadata={"help": "A folder containing the training data."}     )
	lowerCAmelCase__					:			Optional[str]								=						field(default=__lowerCAmelCase	,metadata={"help": "A folder containing the validation data."}     )
	lowerCAmelCase__					:			Optional[float]								=						field(
	    default=0.15	,metadata={"help": "Percent to split off of train for validation."}     )
	lowerCAmelCase__					:			int								=						field(default=32	,metadata={"help": "The size of the square patches to use for masking."}     )
	lowerCAmelCase__					:			float								=						field(
	    default=0.6	,metadata={"help": "Percentage of patches to mask."}	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }	,)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = {}
				if self.train_dir is not None:
							a__     = self.train_dir
				if self.validation_dir is not None:
							a__     = self.validation_dir
				a__     = data_files if data_files else None






@dataclass
class       UpperCamelCase__  :
	lowerCAmelCase__					:			str								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
	            "checkpoint identifier on the hub. "
	            "Don't set if you want to train a model from scratch."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowerCAmelCase     )}	,)
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "Override some existing default config settings when a model is trained from scratch. Example: "
	            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[str]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}	,)
	lowerCAmelCase__					:			str								=						field(
	    default="main"	,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}	,)
	lowerCAmelCase__					:			str								=						field(default=__lowerCAmelCase	,metadata={"help": "Name or path of preprocessor config."}     )
	lowerCAmelCase__					:			bool								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=__lowerCAmelCase	,metadata={
	        "help": (
	            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
	        )
	    }	,)
	lowerCAmelCase__					:			Optional[int]								=						field(
	    default=__lowerCAmelCase	,metadata={"help": "Stride to use for the encoder."}	,)






class       UpperCamelCase__  :







	def __init__(				self			:   Any , lowerCamelCase			:   Dict=1_9_2 , lowerCamelCase			:   int=3_2 , lowerCamelCase			:   Dict=4 , lowerCamelCase			:   Dict=0.6		):




				'''simple docstring'''
				a__     = input_size
				a__     = mask_patch_size
				a__     = model_patch_size
				a__     = mask_ratio

				if self.input_size % self.mask_patch_size != 0:
							raise ValueError("Input size must be divisible by mask patch size"		)
				if self.mask_patch_size % self.model_patch_size != 0:
							raise ValueError("Mask patch size must be divisible by model patch size"		)

				a__     = self.input_size // self.mask_patch_size
				a__     = self.mask_patch_size // self.model_patch_size

				a__     = self.rand_size**2
				a__     = int(np.ceil(self.token_count * self.mask_ratio		)		)







	def __call__(				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = np.random.permutation(self.token_count		)[: self.mask_count]
				a__     = np.zeros(self.token_count , dtype=lowerCamelCase		)
				a__     = 1

				a__     = mask.reshape((self.rand_size, self.rand_size)		)
				a__     = mask.repeat(self.scale , axis=0		).repeat(self.scale , axis=1		)

				return torch.tensor(mask.flatten()		)







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Union[str, Any]:
			a__     = torch.stack([example["pixel_values"] for example in examples]     )
			a__     = torch.stack([example["mask"] for example in examples]     )
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def    _lowerCamelCase  ()   ->    Optional[Any]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			a__     = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
			if len(sys.argv     ) == 2 and sys.argv[1].endswith(".json"     ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						a__   ,				a__   ,				a__     = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						a__   ,				a__   ,				a__     = parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("run_mim"					,  __lowerCamelCase					,  __lowerCamelCase     )

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,  datefmt="%m/%d/%Y %H:%M:%S"					,  handlers=[logging.StreamHandler(sys.stdout     )]					,  )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			a__     = training_args.get_process_log_level()
			logger.setLevel(__lowerCamelCase     )
			transformers.utils.logging.set_verbosity(__lowerCamelCase     )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + f'''distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'''     )
			logger.info(f'''Training/evaluation parameters {training_args}'''     )

			# Detecting last checkpoint.
			a__     = None
			if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
						a__     = get_last_checkpoint(training_args.output_dir     )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
									raise ValueError(
									    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    "Use --overwrite_output_dir to overcome."     )
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."     )

    # Initialize our dataset.
			a__     = load_dataset(
			    data_args.dataset_name					,  data_args.dataset_config_name					,  data_files=data_args.data_files					,  cache_dir=model_args.cache_dir					,  use_auth_token=True if model_args.use_auth_token else None					,  )

			# If we don't have a validation split, split off a percentage of train as validation.
			a__     = None if "validation" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split					,  __lowerCamelCase     ) and data_args.train_val_split > 0.0:
						a__     = ds["train"].train_test_split(data_args.train_val_split     )
						a__     = split["train"]
						a__     = split["test"]

			# Create config
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			a__     = {
			    "cache_dir": model_args.cache_dir,
			    "revision": model_args.model_revision,
			    "use_auth_token": True if model_args.use_auth_token else None,
			}
			if model_args.config_name_or_path:
						a__     = AutoConfig.from_pretrained(model_args.config_name_or_path					,  **__lowerCamelCase     )
			elif model_args.model_name_or_path:
						a__     = AutoConfig.from_pretrained(model_args.model_name_or_path					,  **__lowerCamelCase     )
			else:
						a__     = CONFIG_MAPPING[model_args.model_type]()
						logger.warning("You are instantiating a new config instance from scratch."     )
						if model_args.config_overrides is not None:
									logger.info(f'''Overriding config: {model_args.config_overrides}'''     )
									config.update_from_string(model_args.config_overrides     )
									logger.info(f'''New config: {config}'''     )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
			if hasattr(__lowerCamelCase					,  "decoder_type"     ):
						a__     = "simmim"

			# adapt config
			a__     = model_args.image_size if model_args.image_size is not None else config.image_size
			a__     = model_args.patch_size if model_args.patch_size is not None else config.patch_size
			a__     = (
			    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
			)

			config.update(
			    {
			        "image_size": model_args.image_size,
			        "patch_size": model_args.patch_size,
			        "encoder_stride": model_args.encoder_stride,
			    }     )

			# create image processor
			if model_args.image_processor_name:
						a__     = AutoImageProcessor.from_pretrained(model_args.image_processor_name					,  **__lowerCamelCase     )
			elif model_args.model_name_or_path:
						a__     = AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,  **__lowerCamelCase     )
			else:
						a__     = {
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						a__     = IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						a__     = AutoModelForMaskedImageModeling.from_pretrained(
						    model_args.model_name_or_path					,  from_tf=bool(".ckpt" in model_args.model_name_or_path     )					,  config=__lowerCamelCase					,  cache_dir=model_args.cache_dir					,  revision=model_args.model_revision					,  use_auth_token=True if model_args.use_auth_token else None					,  )
			else:
						logger.info("Training new model from scratch"     )
						a__     = AutoModelForMaskedImageModeling.from_config(__lowerCamelCase     )

			if training_args.do_train:
						a__     = ds["train"].column_names
			else:
						a__     = ds["validation"].column_names

			if data_args.image_column_name is not None:
						a__     = data_args.image_column_name
			elif "image" in column_names:
						a__     = "image"
			elif "img" in column_names:
						a__     = "img"
			else:
						a__     = column_names[0]

			# transformations as done in original SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			a__     = Compose(
			    [
			        Lambda(lambda __lowerCamelCase     : img.convert("RGB"     ) if img.mode != "RGB" else img     ),
			        RandomResizedCrop(model_args.image_size					,  scale=(0.67, 1.0)					,  ratio=(3.0 / 4.0, 4.0 / 3.0)     ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean					,  std=image_processor.image_std     ),
			    ]     )

			# create mask generator
			a__     = MaskGenerator(
			    input_size=model_args.image_size					,  mask_patch_size=data_args.mask_patch_size					,  model_patch_size=model_args.patch_size					,  mask_ratio=data_args.mask_ratio					,  )

			def preprocess_images(__lowerCamelCase  :     str     ):
						a__     = [transforms(__lowerCamelCase     ) for image in examples[image_column_name]]
						a__     = [mask_generator() for i in range(len(examples[image_column_name]     )     )]

						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError("--do_train requires a train dataset"     )
						if data_args.max_train_samples is not None:
									a__     = ds["train"].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
						# Set the training transforms
						ds["train"].set_transform(__lowerCamelCase     )

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError("--do_eval requires a validation dataset"     )
						if data_args.max_eval_samples is not None:
									a__     = (
									    ds["validation"].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
									)
						# Set the validation transforms
						ds["validation"].set_transform(__lowerCamelCase     )

			# Initialize our trainer
			a__     = Trainer(
			    model=__lowerCamelCase					,  args=__lowerCamelCase					,  train_dataset=ds["train"] if training_args.do_train else None					,  eval_dataset=ds["validation"] if training_args.do_eval else None					,  tokenizer=__lowerCamelCase					,  data_collator=__lowerCamelCase					,  )

			# Training
			if training_args.do_train:
						a__     = None
						if training_args.resume_from_checkpoint is not None:
									a__     = training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									a__     = last_checkpoint
						a__     = trainer.train(resume_from_checkpoint=__lowerCamelCase     )
						trainer.save_model()
						trainer.log_metrics("train"					,  train_result.metrics     )
						trainer.save_metrics("train"					,  train_result.metrics     )
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						a__     = trainer.evaluate()
						trainer.log_metrics("eval"					,  __lowerCamelCase     )
						trainer.save_metrics("eval"					,  __lowerCamelCase     )

			# Write model card and (optionally) push to hub
			a__     = {
			    "finetuned_from": model_args.model_name_or_path,
			    "tasks": "masked-image-modeling",
			    "dataset": data_args.dataset_name,
			    "tags": ["masked-image-modeling"],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**__lowerCamelCase     )
			else:
						trainer.create_model_card(**__lowerCamelCase     )


if __name__ == "__main__":
		main()










'''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)
		lowerCAmelCase_   :   str       =					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(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
		)

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

		# Adding a second convolutional layer
		classifier.add(layers.ConvaD(32, (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=128, 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')

		lowerCAmelCase_   :   Any       =					tf.keras.preprocessing.image.ImageDataGenerator(
		    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
		)

		lowerCAmelCase_   :   List[Any]       =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

		lowerCAmelCase_   :   Tuple       =					train_datagen.flow_from_directory(
		    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
		)

		lowerCAmelCase_   :   Any       =					test_datagen.flow_from_directory(
		    "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
		)

		classifier.fit_generator(
		    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
		)

		classifier.save("cnn.h5")

		# Part 3 - Making new predictions

		lowerCAmelCase_   :   Dict       =					tf.keras.preprocessing.image.load_img(
		    "dataset/single_prediction/image.png", target_size=(64, 64)
		)
		lowerCAmelCase_   :   Optional[Any]       =					tf.keras.preprocessing.image.img_to_array(test_image)
		lowerCAmelCase_   :   Optional[Any]       =					np.expand_dims(test_image, axis=0)
		lowerCAmelCase_   :   Union[str, Any]       =					classifier.predict(test_image)
		# training_set.class_indices
		if result[0][0] == 0:
				lowerCAmelCase_   :   Optional[int]       =					"Normal"
		if result[0][0] == 1:
				lowerCAmelCase_   :   Union[str, Any]       =					"Abnormality detected"



