






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__		:							Tuple 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}







class 				UpperCAmelCase_  (		__a		):
     __lowerCamelCase      				=  '''data2vec-vision'''




     def __init__(				self	,						_lowerCAmelCase=768	,						_lowerCAmelCase=12	,						_lowerCAmelCase=12	,						_lowerCAmelCase=3072	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.0	,						_lowerCAmelCase=0.0	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-12	,						_lowerCAmelCase=224	,						_lowerCAmelCase=16	,						_lowerCAmelCase=3	,						_lowerCAmelCase=False	,						_lowerCAmelCase=False	,						_lowerCAmelCase=False	,						_lowerCAmelCase=False	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=True	,						_lowerCAmelCase=[3, 5, 7, 11]	,						_lowerCAmelCase=[1, 2, 3, 6]	,						_lowerCAmelCase=True	,						_lowerCAmelCase=0.4	,						_lowerCAmelCase=256	,						_lowerCAmelCase=1	,						_lowerCAmelCase=False	,						_lowerCAmelCase=255	,						**_lowerCAmelCase	,						):
          super().__init__(**lowerCAmelCase__		)

          UpperCAmelCase__      :  Any				      =			hidden_size
          UpperCAmelCase__      :  Dict				      =			num_hidden_layers
          UpperCAmelCase__      :  List[str]				      =			num_attention_heads
          UpperCAmelCase__      :  Tuple				      =			intermediate_size
          UpperCAmelCase__      :  Dict				      =			hidden_act
          UpperCAmelCase__      :  List[Any]				      =			hidden_dropout_prob
          UpperCAmelCase__      :  List[Any]				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  str				      =			initializer_range
          UpperCAmelCase__      :  Optional[int]				      =			layer_norm_eps

          UpperCAmelCase__      :  int				      =			image_size
          UpperCAmelCase__      :  int				      =			patch_size
          UpperCAmelCase__      :  List[str]				      =			num_channels
          UpperCAmelCase__      :  List[str]				      =			use_mask_token
          UpperCAmelCase__      :  List[Any]				      =			use_absolute_position_embeddings
          UpperCAmelCase__      :  Optional[int]				      =			use_relative_position_bias
          UpperCAmelCase__      :  Dict				      =			use_shared_relative_position_bias
          UpperCAmelCase__      :  Tuple				      =			layer_scale_init_value
          UpperCAmelCase__      :  Optional[Any]				      =			drop_path_rate
          UpperCAmelCase__      :  Optional[Any]				      =			use_mean_pooling
          # decode head attributes (semantic segmentation)
          UpperCAmelCase__      :  Tuple				      =			out_indices
          UpperCAmelCase__      :  int				      =			pool_scales
          # auxiliary head attributes (semantic segmentation)
          UpperCAmelCase__      :  Any				      =			use_auxiliary_head
          UpperCAmelCase__      :  str				      =			auxiliary_loss_weight
          UpperCAmelCase__      :  Union[str, Any]				      =			auxiliary_channels
          UpperCAmelCase__      :  Optional[int]				      =			auxiliary_num_convs
          UpperCAmelCase__      :  Optional[Any]				      =			auxiliary_concat_input
          UpperCAmelCase__      :  int				      =			semantic_loss_ignore_index







class 				UpperCAmelCase_  (		__a		):
     __lowerCamelCase      				=  version.parse('1.11'		)




     @property
     def   __UpperCAmelCase  (				self		):
          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ]		)




     @property
     def   __UpperCAmelCase  (				self		):
          return 1e-4








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      :							Tuple     	=    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=1_28, 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      :							Optional[int]     	=    tf.keras.preprocessing.image.ImageDataGenerator(
						    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
						)

						_snake_case      :							Any     	=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

						_snake_case      :							Dict     	=    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

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