






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)
  __a:     Optional[Any]   					=					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')

  __a:     Optional[int]   					=					tf.keras.preprocessing.image.ImageDataGenerator(
      rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
  )

  __a:     str   					=					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

  __a:     int   					=					train_datagen.flow_from_directory(
      '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
  )

  __a:     Optional[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

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




"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


a_							=    logging.get_logger(__name__)

a_							=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a_							=    {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

a_							=    {
    """squeezebert/squeezebert-uncased""": 5_12,
    """squeezebert/squeezebert-mnli""": 5_12,
    """squeezebert/squeezebert-mnli-headless""": 5_12,
}


a_							=    {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			VOCAB_FILES_NAMES
			a_       :			List[str]												=			PRETRAINED_VOCAB_FILES_MAP
			a_       :			Optional[Any]												=			PRETRAINED_INIT_CONFIGURATION
			a_       :			Tuple												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_       :			Optional[int]												=			SqueezeBertTokenizer
			def __init__(	self				,   A=None				,   A=None				,   A=True				,   A="[UNK]"				,   A="[SEP]"				,   A="[PAD]"				,   A="[CLS]"				,   A="[MASK]"				,   A=True				,   A=None				,   **A				,   ):
						super().__init__(
						    A				,   tokenizer_file=A				,   do_lower_case=A				,   unk_token=A				,   sep_token=A				,   pad_token=A				,   cls_token=A				,   mask_token=A				,   tokenize_chinese_chars=A				,   strip_accents=A				,   **A				,   )

						_lowerCamelCase					:					Union[str, Any]	       =    json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
						if (
						    normalizer_state.get('lowercase'				,   A							) != do_lower_case
						    or normalizer_state.get('strip_accents'				,   A							) != strip_accents
						    or normalizer_state.get('handle_chinese_chars'				,   A							) != tokenize_chinese_chars
						):
									_lowerCamelCase					:					List[Any]	       =    getattr(A				,   normalizer_state.pop('type'							)							)
									_lowerCamelCase					:					Optional[Any]	       =    do_lower_case
									_lowerCamelCase					:					str	       =    strip_accents
									_lowerCamelCase					:					Any	       =    tokenize_chinese_chars
									_lowerCamelCase					:					int	       =    normalizer_class(**A							)

						_lowerCamelCase					:					Dict	       =    do_lower_case
			def    _lowerCAmelCase		(	self				,   A				,   A=None							):
						_lowerCamelCase					:					Dict	       =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output
			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					Union[str, Any]	       =    [self.sep_token_id]
						_lowerCamelCase					:					Optional[Any]	       =    [self.cls_token_id]
						if token_ids_a is None:
									return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]

			def    _lowerCAmelCase		(	self				,   A				,   A = None							):
						_lowerCamelCase					:					str	       =    self._tokenizer.model.save(A				,   name=A							)
						return tuple(A							)





