import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase = False     ,   UpperCAmelCase = False     ,   UpperCAmelCase = None     ,   **UpperCAmelCase     ,   ):







														'''simple docstring'''






														super().__init__(
														    UpperCAmelCase     ,   split=UpperCAmelCase     ,   features=UpperCAmelCase     ,   cache_dir=UpperCAmelCase     ,   keep_in_memory=UpperCAmelCase     ,   streaming=UpperCAmelCase     ,   num_proc=UpperCAmelCase     ,   **UpperCAmelCase     ,   )
														_lowercase				=	path_or_paths if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ) else {self.split: path_or_paths}
														_lowercase				=	Csv(
														    cache_dir=UpperCAmelCase     ,   data_files=UpperCAmelCase     ,   features=UpperCAmelCase     ,   **UpperCAmelCase     ,   )



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														if self.streaming:
																					_lowercase				=	self.builder.as_streaming_dataset(split=self.split  )
														# Build regular (map-style) dataset
														else:
																					_lowercase				=	None
																					_lowercase				=	None
																					_lowercase				=	None
																					_lowercase				=	None

																					self.builder.download_and_prepare(
																					    download_config=UpperCAmelCase     ,   download_mode=UpperCAmelCase     ,   verification_mode=UpperCAmelCase     ,   base_path=UpperCAmelCase     ,   num_proc=self.num_proc     ,   )
																					_lowercase				=	self.builder.as_dataset(
																					    split=self.split     ,   verification_mode=UpperCAmelCase     ,   in_memory=self.keep_in_memory  )
														return dataset






class _lowercase					:





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   **UpperCAmelCase     ,   ):







														'''simple docstring'''






														if num_proc is not None and num_proc <= 0:
																					raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''  )

														_lowercase				=	dataset
														_lowercase				=	path_or_buf
														_lowercase				=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
														_lowercase				=	num_proc
														_lowercase				=	"""utf-8"""
														_lowercase				=	to_csv_kwargs



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	self.to_csv_kwargs.pop("""path_or_buf"""     ,   UpperCAmelCase  )
														_lowercase				=	self.to_csv_kwargs.pop("""header"""     ,   UpperCAmelCase  )
														_lowercase				=	self.to_csv_kwargs.pop("""index"""     ,   UpperCAmelCase  )

														if isinstance(self.path_or_buf     ,   (str, bytes, os.PathLike)  ):
																					with open(self.path_or_buf     ,   """wb+"""  ) as buffer:
																												_lowercase				=	self._write(file_obj=UpperCAmelCase     ,   header=UpperCAmelCase     ,   index=UpperCAmelCase     ,   **self.to_csv_kwargs  )
														else:
																					_lowercase				=	self._write(file_obj=self.path_or_buf     ,   header=UpperCAmelCase     ,   index=UpperCAmelCase     ,   **self.to_csv_kwargs  )
														return written



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				,       _lowercase				,       _lowercase				,       _lowercase				=	args

														_lowercase				=	query_table(
														    table=self.dataset.data     ,   key=slice(UpperCAmelCase     ,   offset + self.batch_size  )     ,   indices=self.dataset._indices     ,   )
														_lowercase				=	batch.to_pandas().to_csv(
														    path_or_buf=UpperCAmelCase     ,   header=header if (offset == 0) else False     ,   index=UpperCAmelCase     ,   **UpperCAmelCase  )
														return csv_str.encode(self.encoding  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   **UpperCAmelCase  ):







														'''simple docstring'''






														_lowercase				=	0

														if self.num_proc is None or self.num_proc == 1:
																					for offset in logging.tqdm(
																					    range(0     ,   len(self.dataset  )     ,   self.batch_size  )     ,   unit="""ba"""     ,   disable=not logging.is_progress_bar_enabled()     ,   desc="""Creating CSV from Arrow format"""     ,   ):
																												_lowercase				=	self._batch_csv((offset, header, index, to_csv_kwargs)  )
																												written += file_obj.write(UpperCAmelCase  )

														else:
																					_lowercase				,       _lowercase				=	len(self.dataset  ), self.batch_size
																					with multiprocessing.Pool(self.num_proc  ) as pool:
																												for csv_str in logging.tqdm(
																												    pool.imap(
																												        self._batch_csv     ,   [(offset, header, index, to_csv_kwargs) for offset in range(0     ,   UpperCAmelCase     ,   UpperCAmelCase  )]     ,   )     ,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,   unit="""ba"""     ,   disable=not logging.is_progress_bar_enabled()     ,   desc="""Creating CSV from Arrow format"""     ,   ):
																																			written += file_obj.write(UpperCAmelCase  )

														return written



import random


class _lowercase					:





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):







														'''simple docstring'''






														_lowercase				=	sample
														if len(self.sample  ) == 0:
																					raise ValueError("""Sample data can not be empty"""  )
														_lowercase				=	target
														if len(self.target  ) == 0:
																					raise ValueError("""Target data can not be empty"""  )
														if len(self.sample  ) != len(self.target  ):
																					raise ValueError("""Sample data and Target data do not have matching lengths"""  )
														_lowercase				=	learning_rate
														_lowercase				=	epoch_number
														_lowercase				=	bias
														_lowercase				=	len(UpperCAmelCase  )
														_lowercase				=	len(sample[0]  )  # number of columns in dataset
														_lowercase				=	[]



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														for sample in self.sample:
																					sample.insert(0     ,   self.bias  )

														for _ in range(self.col_sample  ):
																					self.weight.append(random.random()  )

														self.weight.insert(0     ,   self.bias  )

														_lowercase				=	0

														while True:
																					_lowercase				=	False
																					for i in range(self.number_sample  ):
																												_lowercase				=	0
																												for j in range(self.col_sample + 1  ):
																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
																												_lowercase				=	self.sign(UpperCAmelCase  )
																												if y != self.target[i]:
																																			for j in range(self.col_sample + 1  ):
																																										_lowercase				=	(
																																										    self.weight[j]
																																										    + self.learning_rate
																																										    * (self.target[i] - y)
																																										    * self.sample[i][j]
																																										)
																																			_lowercase				=	True
            # print('Epoch: \n',epoch_count)
																					_lowercase				=	epoch_count + 1
																					# if you want control the epoch or just by error
																					if not has_misclassified:
																												print(("""\nEpoch:\n""", epoch_count)  )
																												print("""------------------------\n"""  )
																												# if epoch_count > self.epoch_number or not error:
																												break



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														if len(self.sample  ) == 0:
																					raise ValueError("""Sample data can not be empty"""  )
														sample.insert(0     ,   self.bias  )
														_lowercase				=	0
														for i in range(self.col_sample + 1  ):
																					_lowercase				=	u + self.weight[i] * sample[i]

														_lowercase				=	self.sign(UpperCAmelCase  )

														if y == -1:
																					print(("""Sample: """, sample)  )
																					print("""classification: P1"""  )
														else:
																					print(("""Sample: """, sample)  )
																					print("""classification: P2"""  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														return 1 if u >= 0 else -1






A_:      Optional[int]        =			[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

A_:      Optional[int]        =			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				A_:      str        =			Perceptron(
				    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
				)
				network.training()
				print('Finished training perceptron')
				print('Enter values to predict or q to exit')
				while True:
								A_:      list        =			[]
								for i in range(len(samples[0])):
												A_:      Dict        =			input('value: ').strip()
												if user_input == "q":
																break
												A_:      Union[str, Any]        =			float(user_input)
												sample.insert(i, observation)
								network.sort(sample)


