






"""simple docstring"""
import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def        lowerCamelCase_  (						_lowerCamelCase			,   **_lowerCamelCase				):
 return RunBeamCommand(
     args.dataset			,   args.name			,   args.cache_dir			,   args.beam_pipeline_options			,   args.data_dir			,   args.all_configs			,   args.save_info or args.save_infos			,   args.ignore_verifications			,   args.force_redownload			,   **_lowerCamelCase			,   )





class 		a_      (	__SCREAMING_SNAKE_CASE ):







      '''simple docstring'''

      @staticmethod
      def 			a__							(lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						parser.add_parser('run_beam',     help='Run a Beam dataset processing pipeline'					)
       run_beam_parser.add_argument('dataset',     type=lowerCamelCase_,     help='Name of the dataset to download'					)
       run_beam_parser.add_argument('--name',     type=lowerCamelCase_,     default=lowerCamelCase_,     help='Dataset config name'					)
       run_beam_parser.add_argument(
           '--cache_dir',     type=lowerCamelCase_,     default=lowerCamelCase_,     help='Cache directory where the datasets are stored',     )
       run_beam_parser.add_argument(
           '--beam_pipeline_options',     type=lowerCamelCase_,     default='',     help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`',     )
       run_beam_parser.add_argument(
           '--data_dir',     type=lowerCamelCase_,     default=lowerCamelCase_,     help='Can be used to specify a manual directory to get the files from',     )
       run_beam_parser.add_argument('--all_configs',     action='store_true',     help='Test all dataset configurations'					)
       run_beam_parser.add_argument('--save_info',     action='store_true',     help='Save the dataset infos file'					)
       run_beam_parser.add_argument(
           '--ignore_verifications',     action='store_true',     help='Run the test without checksums and splits checks'					)
       run_beam_parser.add_argument('--force_redownload',     action='store_true',     help='Force dataset redownload'					)
       # aliases
       run_beam_parser.add_argument('--save_infos',     action='store_true',     help='alias for save_info'					)
       run_beam_parser.set_defaults(func=lowerCamelCase_					)




      def __init__(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     **lowerCamelCase_,     ):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						dataset
       lowerCamelCase__     :			str     				=						name
       lowerCamelCase__     :			Any     				=						cache_dir
       lowerCamelCase__     :			int     				=						beam_pipeline_options
       lowerCamelCase__     :			List[str]     				=						data_dir
       lowerCamelCase__     :			List[Any]     				=						all_configs
       lowerCamelCase__     :			List[str]     				=						save_infos
       lowerCamelCase__     :			str     				=						ignore_verifications
       lowerCamelCase__     :			Tuple     				=						force_redownload
       lowerCamelCase__     :			Tuple     				=						config_kwargs





      def 			a__							(self					):

       '''simple docstring'''



       import apache_beam as beam

       if self._name is not None and self._all_configs:
        print('Both parameters `name` and `all_configs` can\'t be used at once.'					)
        exit(1					)
       lowerCamelCase__     :			Optional[Any]     				=						self._dataset, self._name
       lowerCamelCase__     :			Tuple     				=						dataset_module_factory(lowerCamelCase_					)
       lowerCamelCase__     :			Tuple     				=						import_main_class(dataset_module.module_path					)
       lowerCamelCase__     :			List[DatasetBuilder]     				=						[]
       if self._beam_pipeline_options:
        lowerCamelCase__     :			List[Any]     				=						beam.options.pipeline_options.PipelineOptions(
            flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','					) if opt]					)
       else:
        lowerCamelCase__     :			Optional[int]     				=						None
       if self._all_configs and len(builder_cls.BUILDER_CONFIGS					) > 0:
        for builder_config in builder_cls.BUILDER_CONFIGS:
         builders.append(
             builder_cls(
                 config_name=builder_config.name,     data_dir=self._data_dir,     hash=dataset_module.hash,     beam_options=lowerCamelCase_,     cache_dir=self._cache_dir,     base_path=dataset_module.builder_kwargs.get('base_path'					),     )					)
       else:
        builders.append(
            builder_cls(
                config_name=lowerCamelCase_,     data_dir=self._data_dir,     beam_options=lowerCamelCase_,     cache_dir=self._cache_dir,     base_path=dataset_module.builder_kwargs.get('base_path'					),     **self._config_kwargs,     )					)

       for builder in builders:
        builder.download_and_prepare(
            download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
            if not self._force_redownload
            else DownloadMode.FORCE_REDOWNLOAD,     download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH					),     verification_mode=VerificationMode.NO_CHECKS
            if self._ignore_verifications
            else VerificationMode.ALL_CHECKS,     try_from_hf_gcs=lowerCamelCase_,     )
        if self._save_infos:
         builder._save_infos()

       print('Apache beam run successful.'					)

       # If save_infos=True, the dataset infos file is created next to the loaded module file.
       # Let's move it to the original directory of the dataset script, to allow the user to
       # upload them on S3 at the same time afterwards.
       if self._save_infos:
        lowerCamelCase__     :			Union[str, Any]     				=						os.path.join(builder_cls.get_imported_module_dir(),     config.DATASETDICT_INFOS_FILENAME					)

        lowerCamelCase__     :			Any     				=						Path(lowerCamelCase_					).name + '''.py'''

        lowerCamelCase__     :			List[Any]     				=						os.path.join(lowerCamelCase_,     lowerCamelCase_					)
        if os.path.isfile(lowerCamelCase_					):
         lowerCamelCase__     :			List[str]     				=						os.path.dirname(lowerCamelCase_					)
        elif os.path.isfile(lowerCamelCase_					):
         lowerCamelCase__     :			int     				=						path
        else:  # in case of a remote dataset
         print(f'''Dataset Infos file saved at {dataset_infos_path}'''					)
         exit(1					)

        # Move datasetinfo back to the user
        lowerCamelCase__     :			Optional[int]     				=						os.path.join(lowerCamelCase_,     config.DATASETDICT_INFOS_FILENAME					)
        copyfile(lowerCamelCase_,     lowerCamelCase_					)
        print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''					)







from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					seed(1       )
					return [gauss(UpperCAmelCase,	UpperCAmelCase       ) for _ in range(UpperCAmelCase       )]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					return [k for k in range(UpperCAmelCase       ) for _ in range(instance_count[k]       )]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return sum(UpperCAmelCase       ) / instance_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return instance_count / total_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Any		   =							[]  # An empty list to store all squared differences
					# iterate over number of elements in items
					for i in range(len(UpperCAmelCase       )       ):
										# for loop iterates over number of elements in inner layer of items
										for j in range(len(items[i]       )       ):
															# appending squared differences to 'squared_diff' list
															squared_diff.append((items[i][j] - means[i]) ** 2       )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
					__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					__magic_name__					:							int		   =							[]
					# for loop iterates over number of elements in list
					for i in range(len(UpperCAmelCase       )       ):
										# for loop iterates over number of inner items of each element
										for j in range(len(x_items[i]       )       ):
															__magic_name__					:							Dict		   =							[]  # to store all discriminant values of each item as a list
															# for loop iterates over number of classes we have in our dataset
															for k in range(len(UpperCAmelCase       )       ):
																				# appending values of discriminants for each class to 'temp' list
																				temp.append(
																				    x_items[i][j] * (means[k] / variance)
																				    - (means[k] ** 2 / (2 * variance))
																				    + log(probabilities[k]       )       )
															# appending discriminant values of each item to 'results' list
															results.append(UpperCAmelCase       )

					return [result.index(max(UpperCAmelCase       )       ) for result in results]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							sum(1 for i, j in zip(UpperCAmelCase,	UpperCAmelCase       ) if i == j       )
					# percentage of accuracy equals to number of correct predictions divided by number
					# of all data and multiplied by 100
					return (correct / len(UpperCAmelCase       )) * 100


lowercase_		=							TypeVar('''num''')







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase = lambda UpperCAmelCase       : True,	UpperCAmelCase = None,	)  ->num:






					"""simple docstring"""






					while True:
										try:
															__magic_name__					:							List[Any]		   =							input_type(input(UpperCAmelCase       ).strip() or default       )
															if condition(UpperCAmelCase       ):
																				return user_input
															else:
																				print(F'''{user_input}: {err_msg}'''       )
																				continue
										except ValueError:
															print(
															    F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''       )







def  lowerCAmelCase  (	)  ->str:






					"""simple docstring"""






					while True:
										print(''' Linear Discriminant Analysis '''.center(50,	'''*'''       )       )
										print('''*''' * 50,	'''\n'''       )
										print('''First of all we should specify the number of classes that'''       )
										print('''we want to generate as training dataset'''       )
										# Trying to get number of classes
										__magic_name__					:							List[Any]		   =							valid_input(
										    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x > 0,	input_msg='''Enter the number of classes (Data Groupings): ''',	err_msg='''Number of classes should be positive!''',	)

										print('''-''' * 100       )

										# Trying to get the value of standard deviation
										__magic_name__					:							Any		   =							valid_input(
										    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x >= 0,	input_msg=(
										        '''Enter the value of standard deviation'''
										        '''(Default value is 1.0 for all classes): '''
										    ),	err_msg='''Standard deviation should not be negative!''',	default='''1.0''',	)

										print('''-''' * 100       )

										# Trying to get number of instances in classes and theirs means to generate
										# dataset
										__magic_name__					:							Optional[Any]		   =							[]  # An empty list to store instance counts of classes in dataset
										for i in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							valid_input(
															    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x > 0,	input_msg=(F'''Enter The number of instances for class_{i+1}: '''),	err_msg='''Number of instances should be positive!''',	)
															counts.append(UpperCAmelCase       )
										print('''-''' * 100       )

										# An empty list to store values of user-entered means of classes
										__magic_name__					:							Dict		   =							[]
										for a in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							valid_input(
															    input_type=UpperCAmelCase,	input_msg=(F'''Enter the value of mean for class_{a+1}: '''),	err_msg='''This is an invalid value.''',	)
															user_means.append(UpperCAmelCase       )
										print('''-''' * 100       )

										print('''Standard deviation: ''',	UpperCAmelCase       )
										# print out the number of instances in classes in separated line
										for i, count in enumerate(UpperCAmelCase,	1       ):
															print(F'''Number of instances in class_{i} is: {count}'''       )
										print('''-''' * 100       )

										# print out mean values of classes separated line
										for i, user_mean in enumerate(UpperCAmelCase,	1       ):
															print(F'''Mean of class_{i} is: {user_mean}'''       )
										print('''-''' * 100       )

										# Generating training dataset drawn from gaussian distribution
										__magic_name__					:							Optional[int]		   =							[
										    gaussian_distribution(user_means[j],	UpperCAmelCase,	counts[j]       )
										    for j in range(UpperCAmelCase       )
										]
										print('''Generated Normal Distribution: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Generating Ys to detecting corresponding classes
										__magic_name__					:							List[str]		   =							y_generator(UpperCAmelCase,	UpperCAmelCase       )
										print('''Generated Corresponding Ys: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating the value of actual mean for each class
										__magic_name__					:							Dict		   =							[calculate_mean(counts[k],	x[k]       ) for k in range(UpperCAmelCase       )]
										# for loop iterates over number of elements in 'actual_means' list and print
										# out them in separated line
										for i, actual_mean in enumerate(UpperCAmelCase,	1       ):
															print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''       )
										print('''-''' * 100       )

										# Calculating the value of probabilities for each class
										__magic_name__					:							Union[str, Any]		   =							[
										    calculate_probabilities(counts[i],	sum(UpperCAmelCase       )       ) for i in range(UpperCAmelCase       )
										]

										# for loop iterates over number of elements in 'probabilities' list and print
										# out them in separated line
										for i, probability in enumerate(UpperCAmelCase,	1       ):
															print(F'''Probability of class_{i} is: {probability}'''       )
										print('''-''' * 100       )

										# Calculating the values of variance for each class
										__magic_name__					:							List[Any]		   =							calculate_variance(UpperCAmelCase,	UpperCAmelCase,	sum(UpperCAmelCase       )       )
										print('''Variance: ''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										__magic_name__					:							List[Any]		   =							predict_y_values(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating Accuracy of the model
										print(F'''Accuracy: {accuracy(UpperCAmelCase,	UpperCAmelCase       )}'''       )
										print('''-''' * 100       )
										print(''' DONE '''.center(100,	'''+'''       )       )

										if input('''Press any key to restart or \'q\' for quit: '''       ).strip().lower() == "q":
															print('''\n''' + '''GoodBye!'''.center(100,	'''-'''       ) + '''\n'''       )
															break
										system('''cls''' if name == '''nt''' else '''clear'''       )  # noqa: S605


if __name__ == "__main__":
				main()



