
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__SCREAMING_SNAKE_CASE      : Dict       =  datasets.utils.logging.get_logger(__name__)


@dataclass
class 		__magic_name__ (    datasets.BuilderConfig     ):
		_lowerCAmelCase		=						10_000
		_lowerCAmelCase		=						None
		_lowerCAmelCase		=						None



class 		__magic_name__ (    datasets.ArrowBasedBuilder     ):
		_lowerCAmelCase		=						ParquetConfig





		def    _A				(				self     :						List[str]					):
							return datasets.DatasetInfo(features=self.config.features					)





		def    _A				(				self     :						Tuple    ,	lowerCamelCase__     :						Optional[Any]					):
							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}'''					)
							lowerCAmelCase		:       int							=  dl_manager.download_and_extract(self.config.data_files					)
							if isinstance(lowerCamelCase__    ,	(str, list, tuple)					):
												lowerCAmelCase		:       str							=  data_files
												if isinstance(lowerCamelCase__    ,	lowerCamelCase__					):
																	lowerCAmelCase		:       Union[str, Any]							=  [files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												lowerCAmelCase		:       Tuple							=  [dl_manager.iter_files(lowerCamelCase__					) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,	gen_kwargs={'''files''': files}					)]
							lowerCAmelCase		:       Optional[int]							=  []
							for split_name, files in data_files.items():
												if isinstance(lowerCamelCase__    ,	lowerCamelCase__					):
																	lowerCAmelCase		:       Tuple							=  [files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												lowerCAmelCase		:       List[Any]							=  [dl_manager.iter_files(lowerCamelCase__					) for file in files]
												# Infer features is they are stoed in the arrow schema
												if self.info.features is None:
																	for file in itertools.chain.from_iterable(lowerCamelCase__					):
																						with open(lowerCamelCase__    ,	'''rb'''					) as f:
																											lowerCAmelCase		:       Tuple							=  datasets.Features.from_arrow_schema(pq.read_schema(lowerCamelCase__					)					)
																						break
												splits.append(datasets.SplitGenerator(name=lowerCamelCase__    ,	gen_kwargs={'''files''': files}					)					)
							return splits





		def    _A				(				self     :						Optional[Any]    ,	lowerCamelCase__     :						Optional[Any]					):
							if self.info.features is not None:
												# more expensive cast to support nested features with keys in a different order
												# allows str <-> int/float or str to Audio for example
												lowerCAmelCase		:       str							=  table_cast(lowerCamelCase__    ,	self.info.features.arrow_schema					)
							return pa_table





		def    _A				(				self     :						Optional[Any]    ,	lowerCamelCase__     :						Tuple					):
							lowerCAmelCase		:       Dict							=  self.info.features.arrow_schema if self.info.features is not None else None
							if self.info.features is not None and self.config.columns is not None:
												if sorted(field.name for field in schema					) != sorted(self.config.columns					):
																	raise ValueError(
																	    f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''					)
							for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__					)					):
												with open(lowerCamelCase__    ,	'''rb'''					) as f:
																	lowerCAmelCase		:       Optional[Any]							=  pq.ParquetFile(lowerCamelCase__					)
																	try:
																						for batch_idx, record_batch in enumerate(
																						    parquet_file.iter_batches(batch_size=self.config.batch_size    ,	columns=self.config.columns					)					):
																											lowerCAmelCase		:       Any							=  pa.Table.from_batches([record_batch]					)
																											# 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 f'''{file_idx}_{batch_idx}''', self._cast_table(lowerCamelCase__					)
																	except ValueError as e:
																						logger.error(f'''Failed to read file \'{file}\' with error {type(lowerCamelCase__					)}: {e}'''					)
																						raise








'''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)
 __UpperCamelCase   :							Union[str, 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')

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

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

 __UpperCamelCase   :							str         =   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

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