






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:     Tuple   					=					datasets.utils.logging.get_logger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE__	(  datasets.BuilderConfig     ):

  '''simple docstring'''







  _lowerCamelCase    =  None
  _lowerCamelCase    =  "utf-8"
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  True  # deprecated
  _lowerCamelCase    =  None  # deprecated
  _lowerCamelCase    =  10 << 20  # 10MB
  _lowerCamelCase    =  None



class 		SCREAMING_SNAKE_CASE__	(  datasets.ArrowBasedBuilder     ):

  '''simple docstring'''







  _lowerCamelCase    =  JsonConfig


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         if self.config.block_size is not None:
                logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""							)
                _UpperCAmelCase							=    self.config.block_size
         if self.config.use_threads is not True:
                logger.warning(
                    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""							)
         if self.config.newlines_in_values is not None:
                raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""							)
         return datasets.DatasetInfo(features=self.config.features							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         if not self.config.data_files:
                raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
         _UpperCAmelCase							=    dl_manager.download_and_extract(self.config.data_files							)
         if isinstance(lowerCamelCase       ,				(str, list, tuple)							):
                _UpperCAmelCase							=    data_files
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,				gen_kwargs={"""files""": files}							)]
         _UpperCAmelCase							=    []
         for split_name, files in data_files.items():
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                splits.append(datasets.SplitGenerator(name=lowerCamelCase       ,				gen_kwargs={"""files""": files}							)							)
         return splits


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			pa.Table							)						->	pa.Table:


         """simple docstring"""
         if self.config.features is not None:
                # adding missing columns
                for column_name in set(self.config.features							) - set(pa_table.column_names							):
                       _UpperCAmelCase							=    self.config.features.arrow_schema.field(lowerCamelCase							).type
                       _UpperCAmelCase							=    pa_table.append_column(lowerCamelCase       ,				pa.array([None] * len(lowerCamelCase							)       ,				type=lowerCamelCase							)							)
                # more expensive cast to support nested structures with keys in a different order
                # allows str <-> int/float or str to Audio for example
                _UpperCAmelCase							=    table_cast(lowerCamelCase       ,				self.config.features.arrow_schema							)
         return pa_table





  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]							)						->	str:


         """simple docstring"""
         for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase							)							):
                # If the file is one json object and if we need to look at the list of items in one specific field
                if self.config.field is not None:
                       with open(lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                              _UpperCAmelCase							=    json.load(lowerCamelCase							)

                       # We keep only the field we are interested in
                       _UpperCAmelCase							=    dataset[self.config.field]

                       # We accept two format: a list of dicts or a dict of lists
                       if isinstance(lowerCamelCase       ,				(list, tuple)							):
                              _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                              _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                       else:
                              _UpperCAmelCase							=    dataset
                       _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                       yield file_idx, self._cast_table(lowerCamelCase							)

                # If the file has one json object per line
                else:
                       with open(lowerCamelCase       ,				"""rb"""							) as f:
                              _UpperCAmelCase							=    0
                              # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                              # Set a default minimum value of 16kB if the chunk size is really small
                              _UpperCAmelCase							=    max(self.config.chunksize // 32       ,				16 << 10							)
                              _UpperCAmelCase							=    (
                                  self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                              )
                              while True:
                                     _UpperCAmelCase							=    f.read(self.config.chunksize							)
                                     if not batch:
                                            break
                                     # Finish current line
                                     try:
                                            batch += f.readline()
                                     except (AttributeError, io.UnsupportedOperation):
                                            batch += readline(lowerCamelCase							)
                                     # PyArrow only accepts utf-8 encoded bytes
                                     if self.config.encoding != "utf-8":
                                            _UpperCAmelCase							=    batch.decode(self.config.encoding       ,				errors=lowerCamelCase							).encode("""utf-8"""							)
                                     try:
                                            while True:
                                                   try:
                                                          _UpperCAmelCase							=    paj.read_json(
                                                              io.BytesIO(lowerCamelCase							)       ,				read_options=paj.ReadOptions(block_size=lowerCamelCase							)							)
                                                          break
                                                   except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                          if (
                                                              isinstance(lowerCamelCase       ,				pa.ArrowInvalid							)
                                                              and "straddling" not in str(lowerCamelCase							)
                                                              or block_size > len(lowerCamelCase							)
                                                          ):
                                                                 raise
                                                          else:
                                                                 # Increase the block size in case it was too small.
                                                                 # The block size will be reset for the next file.
                                                                 logger.debug(
                                                                     f"""Batch of {len(lowerCamelCase							)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""							)
                                                                 block_size *= 2
                                     except pa.ArrowInvalid as e:
                                            try:
                                                   with open(
                                                       lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                                                          _UpperCAmelCase							=    json.load(lowerCamelCase							)
                                            except json.JSONDecodeError:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise e
                                            # If possible, parse the file as a list of json objects and exit the loop
                                            if isinstance(lowerCamelCase       ,				lowerCamelCase							):  # list is the only sequence type supported in JSON
                                                   try:
                                                          _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                                                          _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                                                          _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                                                   except (pa.ArrowInvalid, AttributeError) as e:
                                                          logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                          raise ValueError(f"""Not able to read records in the JSON file at {file}."""							) from None
                                                   yield file_idx, self._cast_table(lowerCamelCase							)
                                                   break
                                            else:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise ValueError(
                                                       f"""Not able to read records in the JSON file at {file}. """
                                                       f"""You should probably indicate the field of the JSON file containing your records. """
                                                       f"""This JSON file contain the following fields: {str(list(dataset.keys()							)							)}. """
                                                       f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """							) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                     yield (file_idx, batch_idx), self._cast_table(lowerCamelCase							)
                                     batch_idx += 1

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"
