






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  int				      =			[]
     for part_id in partition_order:
          UpperCAmelCase__      :  Any				      =			df.where(F"SPARK_PARTITION_ID() = {part_id}"       ).collect()
          for row_idx, row in enumerate(__lowerCamelCase       ):
               expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())       )
     return expected_row_ids_and_row_dicts





@require_not_windows
@require_dill_gt_0_3_2
def  _lowerCamelCase						(						) ->			Tuple:

     '''simple docstring'''
     UpperCAmelCase__      :  Tuple				      =			pyspark.sql.SparkSession.builder.master("""local[*]"""       ).appName("""pyspark"""       ).getOrCreate()
     UpperCAmelCase__      :  int				      =			spark.range(100       ).repartition(1       )
     UpperCAmelCase__      :  Optional[Any]				      =			Spark(__lowerCamelCase       )
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16       )
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50





@require_not_windows
@require_dill_gt_0_3_2
def  _lowerCamelCase						(						) ->			Dict:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			pyspark.sql.SparkSession.builder.master("""local[*]"""       ).appName("""pyspark"""       ).getOrCreate()
     UpperCAmelCase__      :  List[Any]				      =			spark.range(10       ).repartition(2       )
     UpperCAmelCase__      :  int				      =			[1, 0]
     UpperCAmelCase__      :  str				      =			_generate_iterable_examples(__lowerCamelCase ,							__lowerCamelCase       )  # Reverse the partitions.
     UpperCAmelCase__      :  Dict				      =			_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCamelCase ,							__lowerCamelCase       )

     for i, (row_id, row_dict) in enumerate(generate_fn()       ):
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def  _lowerCamelCase						(						) ->			int:

     '''simple docstring'''
     UpperCAmelCase__      :  Any				      =			pyspark.sql.SparkSession.builder.master("""local[*]"""       ).appName("""pyspark"""       ).getOrCreate()
     UpperCAmelCase__      :  List[Any]				      =			spark.range(10       ).repartition(1       )
     UpperCAmelCase__      :  Union[str, Any]				      =			SparkExamplesIterable(__lowerCamelCase       )
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(__lowerCamelCase       ):
          assert row_id == F"0_{i}"
          assert row_dict == {"id": i}





@require_not_windows
@require_dill_gt_0_3_2
def  _lowerCamelCase						(						) ->			Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			pyspark.sql.SparkSession.builder.master("""local[*]"""       ).appName("""pyspark"""       ).getOrCreate()
     UpperCAmelCase__      :  int				      =			spark.range(30       ).repartition(3       )
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("""numpy.random.Generator"""       ) as generator_mock:
          UpperCAmelCase__      :  List[str]				      =			lambda __lowerCamelCase       : x.reverse()
          UpperCAmelCase__      :  List[Any]				      =			_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCamelCase ,							[2, 1, 0]       )

          UpperCAmelCase__      :  List[str]				      =			SparkExamplesIterable(__lowerCamelCase       ).shuffle_data_sources(__lowerCamelCase       )
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(__lowerCamelCase       ):
               UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def  _lowerCamelCase						(						) ->			Optional[int]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			pyspark.sql.SparkSession.builder.master("""local[*]"""       ).appName("""pyspark"""       ).getOrCreate()
     UpperCAmelCase__      :  List[Any]				      =			spark.range(20       ).repartition(4       )

     # Partitions 0 and 2
     UpperCAmelCase__      :  Optional[Any]				      =			SparkExamplesIterable(__lowerCamelCase       ).shard_data_sources(worker_id=0 ,							num_workers=2       )
     assert shard_it_a.n_shards == 2
     UpperCAmelCase__      :  str				      =			_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCamelCase ,							[0, 2]       )
     for i, (row_id, row_dict) in enumerate(__lowerCamelCase       ):
          UpperCAmelCase__      ,		UpperCAmelCase__      :  str				      =			expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     UpperCAmelCase__      :  List[str]				      =			SparkExamplesIterable(__lowerCamelCase       ).shard_data_sources(worker_id=1 ,							num_workers=2       )
     assert shard_it_a.n_shards == 2
     UpperCAmelCase__      :  Optional[int]				      =			_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCamelCase ,							[1, 3]       )
     for i, (row_id, row_dict) in enumerate(__lowerCamelCase       ):
          UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def  _lowerCamelCase						(						) ->			Any:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			pyspark.sql.SparkSession.builder.master("""local[*]"""       ).appName("""pyspark"""       ).getOrCreate()
     UpperCAmelCase__      :  List[Any]				      =			spark.range(100       ).repartition(1       )
     UpperCAmelCase__      :  int				      =			Spark(__lowerCamelCase       )
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1       )
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 100








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"
