




import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


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




@dataclass
class 		UpperCamelCase__     (datasets.BuilderConfig					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[datasets.Features]     					=				None
    lowerCamelCase_				:     str     					=				"utf-8"
    lowerCamelCase_				:     Optional[str]     					=				None
    lowerCamelCase_				:     int     					=				1_0 << 2_0  # 10MB
    lowerCamelCase_				:     bool     					=				False
    lowerCamelCase_				:     str     					=				"line"




class 		UpperCamelCase__     (datasets.ArrowBasedBuilder					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				TextConfig







    def    _lowercase					(   self					)					->				Optional[Any]:
           return datasets.DatasetInfo(features=self.config.features					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Optional[int]:
           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       :				Any      			= dl_manager.download_and_extract(self.config.data_files					)
           if isinstance(UpperCamelCase__					,  (str, list, tuple)					):
                  lowerCamelCase       :				Optional[int]      			= data_files
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Optional[Any]      			= [files]
                  lowerCamelCase       :				List[str]      			= [dl_manager.iter_files(UpperCamelCase__					) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,  gen_kwargs={"files": files}					)]
           lowerCamelCase       :				Any      			= []
           for split_name, files in data_files.items():
                  if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         lowerCamelCase       :				Optional[Any]      			= [files]
                  lowerCamelCase       :				Optional[Any]      			= [dl_manager.iter_files(UpperCamelCase__					) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCamelCase__					,  gen_kwargs={"files": files}					)					)
           return splits







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				pa.Table:
           if self.config.features is not None:
                  lowerCamelCase       :				List[str]      			= self.config.features.arrow_schema
                  if all(not require_storage_cast(UpperCamelCase__					) for feature in self.config.features.values()					):
                         # cheaper cast
                         lowerCamelCase       :				Tuple      			= pa_table.cast(UpperCamelCase__					)
                  else:
                         # more expensive cast; allows str <-> int/float or str to Audio for example
                         lowerCamelCase       :				Any      			= table_cast(UpperCamelCase__					,  UpperCamelCase__					)
                  return pa_table
           else:
                  return pa_table.cast(pa.schema({"text": pa.string()}					)					)







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Tuple:
           lowerCamelCase       :				List[Any]      			= list(self.config.features					) if self.config.features is not None else ["text"]
           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__					)					):
                  # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
                  with open(UpperCamelCase__					,  encoding=self.config.encoding					,  errors=self.config.errors					) as f:
                         if self.config.sample_by == "line":
                                lowerCamelCase       :				Any      			= 0
                                while True:
                                       lowerCamelCase       :				Union[str, Any]      			= f.read(self.config.chunksize					)
                                       if not batch:
                                              break
                                       batch += f.readline()  # finish current line
                                       # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                                       lowerCamelCase       :				Optional[Any]      			= StringIO(UpperCamelCase__					).readlines()
                                       if not self.config.keep_linebreaks:
                                              lowerCamelCase       :				List[Any]      			= [line.rstrip("\n"					) for line in batch]
                                       lowerCamelCase       :				Optional[int]      			= pa.Table.from_arrays([pa.array(UpperCamelCase__					)]					,  names=UpperCamelCase__					)
                                       # 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 (file_idx, batch_idx), self._cast_table(UpperCamelCase__					)
                                       batch_idx += 1
                         elif self.config.sample_by == "paragraph":
                                lowerCamelCase       :				Union[str, Any]      			= 0
                                lowerCamelCase       :				List[str]      			= ""
                                while True:
                                       lowerCamelCase       :				Optional[Any]      			= f.read(self.config.chunksize					)
                                       if not new_batch:
                                              break
                                       batch += new_batch
                                       batch += f.readline()  # finish current line
                                       lowerCamelCase       :				Tuple      			= batch.split("\n\n"					)
                                       lowerCamelCase       :				Tuple      			= pa.Table.from_arrays(
                                           [pa.array([example for example in batch[:-1] if example]					)]					,  names=UpperCamelCase__					)
                                       # 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 (file_idx, batch_idx), self._cast_table(UpperCamelCase__					)
                                       batch_idx += 1
                                       lowerCamelCase       :				Optional[int]      			= batch[-1]
                                if batch:
                                       lowerCamelCase       :				Union[str, Any]      			= pa.Table.from_arrays([pa.array([batch]					)]					,  names=UpperCamelCase__					)
                                       yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__					)
                         elif self.config.sample_by == "document":
                                lowerCamelCase       :				Optional[Any]      			= f.read()
                                lowerCamelCase       :				str      			= pa.Table.from_arrays([pa.array([text]					)]					,  names=UpperCamelCase__					)
                                yield file_idx, self._cast_table(UpperCamelCase__					)












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






