





import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_UpperCamelCase							=      datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
							import pyspark
@dataclass
class 		SCREAMING_SNAKE_CASE_							(    datasets.BuilderConfig   ):
				"""simple docstring"""
				__snake_case   :       Optional[datasets.Features]	 =		None



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							"pyspark.sql.DataFrame"    ,						SCREAMING_SNAKE_CASE       :							List[int]    ,						):


							'''simple docstring'''







							import pyspark

							def generate_fn():
														__lowerCamelCase      :      int         =df.select('''*'''    ,						pyspark.sql.functions.spark_partition_id().alias('''part_id'''   )   )
														for partition_id in partition_order:
																					__lowerCamelCase      :      List[str]         =df_with_partition_id.select('''*'''   ).where(F'part_id = {partition_id}'   ).drop('''part_id'''   )
																					__lowerCamelCase      :      Optional[Any]         =partition_df.collect()
																					__lowerCamelCase      :      Dict         =0
																					for row in rows:
																												yield F'{partition_id}_{row_id}', row.asDict()
																												row_id += 1

							return generate_fn
class 		SCREAMING_SNAKE_CASE_							(    _BaseExamplesIterable   ):
				"""simple docstring"""




				def __init__(     self  :List[Any]						,    __lowercase  :"pyspark.sql.DataFrame"						,    __lowercase  :List[str]=None						,    ):
											__lowerCamelCase      :      Union[str, Any]         =df
											__lowerCamelCase      :      str         =partition_order or range(self.df.rdd.getNumPartitions()			)
											__lowerCamelCase      :      Dict         =_generate_iterable_examples(self.df						,    self.partition_order			)




				def __iter__(     self  :Optional[int]			):
											yield from self.generate_examples_fn()




				def 					__lowercase  (     self  :str						,    __lowercase  :np.random.Generator			):
											__lowerCamelCase      :      Dict         =list(range(self.df.rdd.getNumPartitions()			)			)
											generator.shuffle(__lowercase			)
											return SparkExamplesIterable(self.df						,    partition_order=__lowercase			)




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :int						,    __lowercase  :int			):
											__lowerCamelCase      :      Optional[int]         =self.split_shard_indices_by_worker(__lowercase						,    __lowercase			)
											return SparkExamplesIterable(self.df						,    partition_order=__lowercase			)




				@property
				def 					__lowercase  (     self  :List[str]			):
											return len(self.partition_order			)

class 		SCREAMING_SNAKE_CASE_							(    datasets.DatasetBuilder   ):
				"""simple docstring"""
				__snake_case   :       List[Any]	 =		SparkConfig




				def __init__(     self  :Union[str, Any]						,    __lowercase  :"pyspark.sql.DataFrame"						,    __lowercase  :str = None						,    __lowercase  :str = None						,    **__lowercase  :List[Any]						,    ):
											import pyspark

											__lowerCamelCase      :      str         =pyspark.sql.SparkSession.builder.getOrCreate()
											__lowerCamelCase      :      int         =df
											__lowerCamelCase      :      Any         =working_dir

											super().__init__(
											    cache_dir=__lowercase						,    config_name=str(self.df.semanticHash()			)						,    **__lowercase						,    )




				def 					__lowercase  (     self  :Any			):
											# Returns the path of the created file.
											def create_cache_and_write_probe(__lowercase  :int			):
																		# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
																		# already exist.
																		os.makedirs(self._cache_dir						,    exist_ok=__lowercase			)
																		__lowerCamelCase      :      Dict         =os.path.join(self._cache_dir						,    '''fs_test''' + uuid.uuida().hex			)
																		# Opening the file in append mode will create a new file unless it already exists, in which case it will not
																		# change the file contents.
																		open(__lowercase						,    '''a'''			)
																		return [probe_file]

											if self._spark.conf.get('''spark.master'''						,    ''''''			).startswith('''local'''			):
																		return

											# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
											# accessible to the driver.
											# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
											if self._cache_dir:
																		__lowerCamelCase      :      List[str]         =(
																		    self._spark.sparkContext.parallelize(range(1			)						,    1			).mapPartitions(__lowercase			).collect()
																		)
																		if os.path.isfile(probe[0]			):
																									return

											raise ValueError(
											    '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''			)




				def 					__lowercase  (     self  :Any			):
											return datasets.DatasetInfo(features=self.config.features			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :datasets.download.download_manager.DownloadManager			):
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN			)]




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :List[str]			):
											import pyspark

											def get_arrow_batch_size(__lowercase  :Dict			):
																		for batch in it:
																									yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}			)

											__lowerCamelCase      :      List[str]         =self.df.count()
											__lowerCamelCase      :      str         =df_num_rows if df_num_rows <= 100 else 100
											# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
											__lowerCamelCase      :      Tuple         =(
											    self.df.limit(__lowercase			)
											    .repartition(1			)
											    .mapInArrow(__lowercase						,    '''batch_bytes: long'''			)
											    .agg(pyspark.sql.functions.sum('''batch_bytes'''			).alias('''sample_bytes'''			)			)
											    .collect()[0]
											    .sample_bytes
											    / sample_num_rows
											)
											__lowerCamelCase      :      List[str]         =approx_bytes_per_row * df_num_rows
											if approx_total_size > max_shard_size:
																		# Make sure there is at least one row per partition.
																		__lowerCamelCase      :      str         =min(__lowercase						,    int(approx_total_size / max_shard_size			)			)
																		__lowerCamelCase      :      List[Any]         =self.df.repartition(__lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :str						,    __lowercase  :str						,    __lowercase  :int						,    ):
											import pyspark

											__lowerCamelCase      :      Optional[Any]         =ParquetWriter if file_format == '''parquet''' else ArrowWriter
											__lowerCamelCase      :      int         =os.path.join(self._working_dir						,    os.path.basename(__lowercase			)			) if self._working_dir else fpath
											__lowerCamelCase      :      Any         =file_format == '''parquet'''

											# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
											# pickling the SparkContext.
											__lowerCamelCase      :      int         =self.config.features
											__lowerCamelCase      :      Any         =self._writer_batch_size
											__lowerCamelCase      :      Dict         =self._fs.storage_options

											def write_arrow(__lowercase  :Optional[int]			):
																		# Within the same SparkContext, no two task attempts will share the same attempt ID.
																		__lowerCamelCase      :      int         =pyspark.TaskContext().taskAttemptId()
																		__lowerCamelCase      :      Optional[Any]         =next(__lowercase						,    __lowercase			)
																		if first_batch is None:
																									# Some partitions might not receive any data.
																									return pa.RecordBatch.from_arrays(
																									    [[task_id], [0], [0]]						,    names=['''task_id''', '''num_examples''', '''num_bytes''']						,    )
																		__lowerCamelCase      :      Any         =0
																		__lowerCamelCase      :      int         =writer_class(
																		    features=__lowercase						,    path=working_fpath.replace('''SSSSS'''						,    f'{shard_id:05d}'			).replace('''TTTTT'''						,    f'{task_id:05d}'			)						,    writer_batch_size=__lowercase						,    storage_options=__lowercase						,    embed_local_files=__lowercase						,    )
																		__lowerCamelCase      :      Union[str, Any]         =pa.Table.from_batches([first_batch]			)
																		writer.write_table(__lowercase			)
																		for batch in it:
																									if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																																__lowerCamelCase      :      str         =writer.finalize()
																																writer.close()
																																yield pa.RecordBatch.from_arrays(
																																    [[task_id], [num_examples], [num_bytes]]						,    names=['''task_id''', '''num_examples''', '''num_bytes''']						,    )
																																shard_id += 1
																																__lowerCamelCase      :      Tuple         =writer_class(
																																    features=writer._features						,    path=working_fpath.replace('''SSSSS'''						,    f'{shard_id:05d}'			).replace('''TTTTT'''						,    f'{task_id:05d}'			)						,    writer_batch_size=__lowercase						,    storage_options=__lowercase						,    embed_local_files=__lowercase						,    )
																									__lowerCamelCase      :      List[str]         =pa.Table.from_batches([batch]			)
																									writer.write_table(__lowercase			)

																		if writer._num_bytes > 0:
																									__lowerCamelCase      :      Any         =writer.finalize()
																									writer.close()
																									yield pa.RecordBatch.from_arrays(
																									    [[task_id], [num_examples], [num_bytes]]						,    names=['''task_id''', '''num_examples''', '''num_bytes''']						,    )

																		if working_fpath != fpath:
																									for file in os.listdir(os.path.dirname(__lowercase			)			):
																																__lowerCamelCase      :      List[str]         =os.path.join(os.path.dirname(__lowercase			)						,    os.path.basename(__lowercase			)			)
																																shutil.move(__lowercase						,    __lowercase			)

											__lowerCamelCase      :      Optional[Any]         =(
											    self.df.mapInArrow(__lowercase						,    '''task_id: long, num_examples: long, num_bytes: long'''			)
											    .groupBy('''task_id'''			)
											    .agg(
											        pyspark.sql.functions.sum('''num_examples'''			).alias('''total_num_examples'''			)						,    pyspark.sql.functions.sum('''num_bytes'''			).alias('''total_num_bytes'''			)						,    pyspark.sql.functions.count('''num_bytes'''			).alias('''num_shards'''			)						,    pyspark.sql.functions.collect_list('''num_examples'''			).alias('''shard_lengths'''			)						,    )
											    .collect()
											)
											for row in stats:
																		yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :"datasets.SplitGenerator"						,    __lowercase  :str = "arrow"						,    __lowercase  :Optional[Union[str, int]] = None						,    __lowercase  :Optional[int] = None						,    **__lowercase  :Tuple						,    ):
											self._validate_cache_dir()

											__lowerCamelCase      :      str         =convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE			)
											self._repartition_df_if_needed(__lowercase			)
											__lowerCamelCase      :      Any         =not is_remote_filesystem(self._fs			)
											__lowerCamelCase      :      Tuple         =os.path.join if is_local else posixpath.join

											__lowerCamelCase      :      Optional[Any]         ='''-TTTTT-SSSSS-of-NNNNN'''
											__lowerCamelCase      :      Any         =f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
											__lowerCamelCase      :      Any         =path_join(self._output_dir						,    __lowercase			)

											__lowerCamelCase      :      List[str]         =0
											__lowerCamelCase      :      List[str]         =0
											__lowerCamelCase      :      Optional[Any]         =0
											__lowerCamelCase      :      Union[str, Any]         =[]
											__lowerCamelCase      :      Union[str, Any]         =[]

											for task_id, content in self._prepare_split_single(__lowercase						,    __lowercase						,    __lowercase			):
																		(
																		    __lowerCamelCase						
																		)      :      Union[str, Any]         =content
																		if num_bytes > 0:
																									total_num_examples += num_examples
																									total_num_bytes += num_bytes
																									total_shards += num_shards
																									task_id_and_num_shards.append((task_id, num_shards)			)
																									all_shard_lengths.extend(__lowercase			)

											__lowerCamelCase      :      Optional[int]         =total_num_examples
											__lowerCamelCase      :      str         =total_num_bytes

											# should rename everything at the end
											logger.debug(f'Renaming {total_shards} shards.'			)
											if total_shards > 1:
																		__lowerCamelCase      :      Any         =all_shard_lengths

																		# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
																		# pickling error due to pickling the SparkContext.
																		__lowerCamelCase      :      Dict         =self._fs

																		# use the -SSSSS-of-NNNNN pattern
																		def _rename_shard(
																		    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :int						,    ):
																									rename(
																									    __lowercase						,    fpath.replace('''SSSSS'''						,    f'{shard_id:05d}'			).replace('''TTTTT'''						,    f'{task_id:05d}'			)						,    fpath.replace('''TTTTT-SSSSS'''						,    f'{global_shard_id:05d}'			).replace('''NNNNN'''						,    f'{total_shards:05d}'			)						,    )

																		__lowerCamelCase      :      Tuple         =[]
																		__lowerCamelCase      :      Optional[int]         =0
																		for i in range(len(__lowercase			)			):
																									__lowerCamelCase      :      Tuple         =task_id_and_num_shards[i]
																									for shard_id in range(__lowercase			):
																																args.append([task_id, shard_id, global_shard_id]			)
																																global_shard_id += 1
																		self._spark.sparkContext.parallelize(__lowercase						,    len(__lowercase			)			).map(lambda __lowercase			: _rename_shard(*__lowercase			)			).collect()
											else:
																		# don't use any pattern
																		__lowerCamelCase      :      Tuple         =0
																		__lowerCamelCase      :      Optional[Any]         =task_id_and_num_shards[0][0]
																		self._rename(
																		    fpath.replace('''SSSSS'''						,    f'{shard_id:05d}'			).replace('''TTTTT'''						,    f'{task_id:05d}'			)						,    fpath.replace(__lowercase						,    ''''''			)						,    )




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :"datasets.SplitGenerator"						,    ):
											return SparkExamplesIterable(self.df			)











"""simple docstring"""





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[Any]						,    __lowercase  :Collection[float] | None = None			):
											if components is None:
																		__lowerCamelCase      :      str         =[]
											__lowerCamelCase      :      int         =list(__lowercase			)




				def __len__(     self  :Union[str, Any]			):
											return len(self.__components			)




				def __str__(     self  :List[Any]			):
											return "(" + ",".join(map(__lowercase						,    self.__components			)			) + ")"




				def __add__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											__lowerCamelCase      :      Tuple         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      int         =[self.__components[i] + other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:
																		raise Exception('''must have the same size'''			)




				def __sub__(     self  :int						,    __lowercase  :Vector			):
											__lowerCamelCase      :      int         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] - other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:  # error case
																		raise Exception('''must have the same size'''			)




				@overload
				def __mul__(     self  :List[str]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :List[Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    (float, int)			):
																		__lowerCamelCase      :      Optional[int]         =[c * other for c in self.__components]
																		return Vector(__lowercase			)
											elif isinstance(__lowercase						,    __lowercase			) and len(self			) == len(__lowercase			):
																		__lowerCamelCase      :      Dict         =len(self			)
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] * other.component(__lowercase			) for i in range(__lowercase			)]
																		return sum(__lowercase			)
											else:  # error case
																		raise Exception('''invalid operand!'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											return Vector(self.__components			)




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :int			):
											if isinstance(__lowercase						,    __lowercase			) and -len(self.__components			) <= i < len(self.__components			):
																		return self.__components[i]
											else:
																		raise Exception('''index out of range'''			)




				def 					__lowercase  (     self  :int						,    __lowercase  :int						,    __lowercase  :float			):
											assert -len(self.__components			) <= pos < len(self.__components			)
											__lowerCamelCase      :      int         =value




				def 					__lowercase  (     self  :Union[str, Any]			):
											if len(self.__components			) == 0:
																		raise Exception('''Vector is empty'''			)
											__lowerCamelCase      :      Tuple         =[c**2 for c in self.__components]
											return math.sqrt(sum(__lowercase			)			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :Vector						,    __lowercase  :bool = False			):
											__lowerCamelCase      :      Tuple         =self * other
											__lowerCamelCase      :      str         =self.euclidean_length() * other.euclidean_length()
											if deg:
																		return math.degrees(math.acos(num / den			)			)
											else:
																		return math.acos(num / den			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							return Vector([0] * dimension   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) and (isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ))
							__lowerCamelCase      :      List[Any]         =[0] * dimension
							__lowerCamelCase      :      Union[str, Any]         =1
							return Vector(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							Vector    ,						SCREAMING_SNAKE_CASE       :							Vector   ):


							'''simple docstring'''







							assert (
							    isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and (isinstance(SCREAMING_SNAKE_CASE    ,						(int, float)   ))
							)
							return x * scalar + y



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =[random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )]
							return Vector(SCREAMING_SNAKE_CASE   )
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :list[list[float]]						,    __lowercase  :int						,    __lowercase  :int			):
											__lowerCamelCase      :      Optional[Any]         =matrix
											__lowerCamelCase      :      int         =w
											__lowerCamelCase      :      str         =h




				def __str__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =''''''
											for i in range(self.__height			):
																		ans += "|"
																		for j in range(self.__width			):
																									if j < self.__width - 1:
																																ans += str(self.__matrix[i][j]			) + ","
																									else:
																																ans += str(self.__matrix[i][j]			) + "|\n"
											return ans




				def __add__(     self  :Any						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      Optional[int]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Optional[int]         =[
																									    self.__matrix[i][j] + other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrix must have the same dimension!'''			)




				def __sub__(     self  :Tuple						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      List[str]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Dict         =[
																									    self.__matrix[i][j] - other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrices must have the same dimension!'''			)




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :Union[str, Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    __lowercase			):  # matrix-vector
																		if len(__lowercase			) == self.__width:
																									__lowerCamelCase      :      int         =zero_vector(self.__height			)
																									for i in range(self.__height			):
																																__lowerCamelCase      :      Union[str, Any]         =[
																																    self.__matrix[i][j] * other.component(__lowercase			)
																																    for j in range(self.__width			)
																																]
																																ans.change_component(__lowercase						,    sum(__lowercase			)			)
																									return ans
																		else:
																									raise Exception(
																									    '''vector must have the same size as the '''
																									    '''number of columns of the matrix!'''			)
											elif isinstance(__lowercase						,    (int, float)			):  # matrix-scalar
																		__lowerCamelCase      :      Optional[Any]         =[
																		    [self.__matrix[i][j] * other for j in range(self.__width			)]
																		    for i in range(self.__height			)
																		]
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											return None




				def 					__lowercase  (     self  :Union[str, Any]			):
											return self.__height




				def 					__lowercase  (     self  :Tuple			):
											return self.__width




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :int			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return self.__matrix[x][y]
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :float			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		__lowerCamelCase      :      int         =value
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											__lowerCamelCase      :      Union[str, Any]         =self.__matrix[:x] + self.__matrix[x + 1 :]
											for i in range(len(__lowercase			)			):
																		__lowerCamelCase      :      Optional[int]         =minor[i][:y] + minor[i][y + 1 :]
											return Matrix(__lowercase						,    self.__width - 1						,    self.__height - 1			).determinant()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return (-1) ** (x + y) * self.minor(__lowercase						,    __lowercase			)
											else:
																		raise Exception('''Indices out of bounds'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if self.__height < 1:
																		raise Exception('''Matrix has no element'''			)
											elif self.__height == 1:
																		return self.__matrix[0][0]
											elif self.__height == 2:
																		return (
																		    self.__matrix[0][0] * self.__matrix[1][1]
																		    - self.__matrix[0][1] * self.__matrix[1][0]
																		)
											else:
																		__lowerCamelCase      :      int         =[
																		    self.__matrix[0][y] * self.cofactor(0						,    __lowercase			) for y in range(self.__width			)
																		]
																		return sum(__lowercase			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      list[list[float]]         =[[0] * n for _ in range(SCREAMING_SNAKE_CASE   )]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      list[list[float]]         =[
							    [random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )] for _ in range(SCREAMING_SNAKE_CASE   )
							]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )




