

"""simple docstring"""





import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


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




@dataclass
class  _lowerCAmelCase  (     datasets.BuilderConfig       ):







 """simple docstring"""







 __UpperCAmelCase     : int       		=     1_0_0_0_0
 __UpperCAmelCase     : Optional[List[str]]       		=     None
 __UpperCAmelCase     : Optional[datasets.Features]       		=     None







class  _lowerCAmelCase  (     datasets.ArrowBasedBuilder       ):







 """simple docstring"""







 __UpperCAmelCase     : Any       		=     ParquetConfig


 def  _lowercase			(      self							:      Dict     ):
    return datasets.DatasetInfo(features=self.config.features     )


 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      Optional[Any]     ):
    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}"""     )
    __lowercase								=						dl_manager.download_and_extract(self.config.data_files     )
    if isinstance(UpperCAmelCase__, (str, list, tuple)     ):
       __lowercase								=						data_files
       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase								=						[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__     ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": files}     )]
    __lowercase								=						[]
    for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase								=						[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__     ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
          for file in itertools.chain.from_iterable(UpperCAmelCase__     ):
             with open(UpperCAmelCase__, "rb"     ) as f:
                __lowercase								=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase__     )     )
             break
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase__, gen_kwargs={"files": files}     )     )
    return splits


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      pa.Table     ):
    if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       __lowercase								=						table_cast(UpperCAmelCase__, self.info.features.arrow_schema     )
    return pa_table



 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      List[str]     ):
    __lowercase								=						self.info.features.arrow_schema if self.info.features is not None else None
    if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema     ) != sorted(self.config.columns     ):
          raise ValueError(
              F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""     )
    for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__     )     ):
       with open(UpperCAmelCase__, "rb"     ) as f:
          __lowercase								=						pq.ParquetFile(UpperCAmelCase__     )
          try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size, columns=self.config.columns     )     ):
                __lowercase								=						pa.Table.from_batches([record_batch]     )
                # 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 F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase__     )
          except ValueError as e:
             logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase__     )}: {e}"""     )
             raise







"""simple docstring"""





import numpy

# List of input, output pairs
_a							=							(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a							=							(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_a							=							[2, 4, 1, 5]
_a							=							len(train_data)
_a							=							0.009




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	List[Any]="train")     ->						Optional[Any]:
   '''simple docstring'''


   return calculate_hypothesis_value(UpperCamelCase_,      UpperCamelCase_) - output(
       UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[Any])     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(len(UpperCamelCase_) - 1):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''simple docstring'''


   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[str])     ->						int:
   '''simple docstring'''


   if data_set == "train":
      return _hypothesis_value(train_data[example_no][0])
   elif data_set == "test":
      return _hypothesis_value(test_data[example_no][0])
   return None




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple=m)     ->						int:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(UpperCamelCase_):
      if index == -1:
         summation_value += _error(UpperCamelCase_)
      else:
         summation_value += _error(UpperCamelCase_) * train_data[i][0][index]
   return summation_value




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   __lowercase								=						summation_of_cost_derivative(UpperCamelCase_,      UpperCamelCase_) / m
   return cost_derivative_value




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowercase								=						0.000_002
   __lowercase								=						0
   __lowercase								=						0
   while True:
      j += 1
      __lowercase								=						[0, 0, 0, 0]
      for i in range(0,      len(UpperCamelCase_)):
         __lowercase								=						get_cost_derivative(i - 1)
         __lowercase								=						(
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          UpperCamelCase_,      UpperCamelCase_,      atol=UpperCamelCase_,      rtol=UpperCamelCase_,      ):
         break
      __lowercase								=						temp_parameter_vector
   print(("Number of iterations:", j))




def   _A  (  )     ->						int:
   '''simple docstring'''


   for i in range(len(UpperCamelCase_)):
      print(("Actual output value:", output(UpperCamelCase_,      "test")))
      print(("Hypothesis output:", calculate_hypothesis_value(UpperCamelCase_,      "test")))


if __name__ == "__main__":
   run_gradient_descent()
   print('\nTesting gradient descent for a linear hypothesis function.\n')
   test_gradient_descent()




