import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

lowerCAmelCase__       = ["names", "prefix"]
lowerCAmelCase__       = ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
lowerCAmelCase__       = ["encoding_errors", "on_bad_lines"]
lowerCAmelCase__       = ["date_format"]






@dataclass
class 						__magic_name__    (       datasets.BuilderConfig    ):
 UpperCAmelCase							    =						""","""
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						"""infer"""
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						"""."""
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						"""\""""
 UpperCAmelCase							    =						0
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						0
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						10_000
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						"""strict"""
 UpperCAmelCase							    =						"""error"""
 UpperCAmelCase							    =						None


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Tuple:
   if self.delimiter is not None:
     UpperCAmelCase					      =  self.delimiter
   if self.column_names is not None:
     UpperCAmelCase					      =  self.column_names


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      List[Any]:
   UpperCAmelCase					      =  {
       "sep": self.sep,
       "header": self.header,
       "names": self.names,
       "index_col": self.index_col,
       "usecols": self.usecols,
       "prefix": self.prefix,
       "mangle_dupe_cols": self.mangle_dupe_cols,
       "engine": self.engine,
       "converters": self.converters,
       "true_values": self.true_values,
       "false_values": self.false_values,
       "skipinitialspace": self.skipinitialspace,
       "skiprows": self.skiprows,
       "nrows": self.nrows,
       "na_values": self.na_values,
       "keep_default_na": self.keep_default_na,
       "na_filter": self.na_filter,
       "verbose": self.verbose,
       "skip_blank_lines": self.skip_blank_lines,
       "thousands": self.thousands,
       "decimal": self.decimal,
       "lineterminator": self.lineterminator,
       "quotechar": self.quotechar,
       "quoting": self.quoting,
       "escapechar": self.escapechar,
       "comment": self.comment,
       "encoding": self.encoding,
       "dialect": self.dialect,
       "error_bad_lines": self.error_bad_lines,
       "warn_bad_lines": self.warn_bad_lines,
       "skipfooter": self.skipfooter,
       "doublequote": self.doublequote,
       "memory_map": self.memory_map,
       "float_precision": self.float_precision,
       "chunksize": self.chunksize,
       "encoding_errors": self.encoding_errors,
       "on_bad_lines": self.on_bad_lines,
       "date_format": self.date_format,
   }

   # some kwargs must not be passed if they don't have a default value
   # some others are deprecated and we can also not pass them if they are the default value
   for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
     if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,						lowerCAmelCase__    ):
       del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
   if not (datasets.config.PANDAS_VERSION.major >= 2):
     for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
       del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
   if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
     for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
       del pd_read_csv_kwargs[pd_read_csv_parameter]

   return pd_read_csv_kwargs






class 						__magic_name__    (       datasets.ArrowBasedBuilder    ):
 UpperCAmelCase							    =						CsvConfig


 def _UpperCamelCase	(						self		: Dict    )    ->      Tuple:
   return datasets.DatasetInfo(features=self.config.features    )


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


 def _UpperCamelCase	(						self		: Tuple			,						lowerCAmelCase__		: pa.Table    )    ->      pa.Table:
   if self.config.features is not None:
     UpperCAmelCase					      =  self.config.features.arrow_schema
     if all(not require_storage_cast(lowerCAmelCase__    ) for feature in self.config.features.values()    ):
       # cheaper cast
       UpperCAmelCase					      =  pa.Table.from_arrays([pa_table[field.name] for field in schema]			,						schema=lowerCAmelCase__    )
     else:
       # more expensive cast; allows str <-> int/float or str to Audio for example
       UpperCAmelCase					      =  table_cast(lowerCAmelCase__			,						lowerCAmelCase__    )
   return pa_table


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: Union[str, Any]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  self.config.features.arrow_schema if self.config.features else None
   # dtype allows reading an int column as str
   UpperCAmelCase					      =  (
       {
           name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__    ) else object
           for name, dtype, feature in zip(schema.names			,						schema.types			,						self.config.features.values()    )
       }
       if schema is not None
       else None
   )
   for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  pd.read_csv(lowerCAmelCase__			,						iterator=lowerCAmelCase__			,						dtype=lowerCAmelCase__			,						**self.config.pd_read_csv_kwargs    )
     try:
       for batch_idx, df in enumerate(lowerCAmelCase__    ):
         UpperCAmelCase					      =  pa.Table.from_pandas(lowerCAmelCase__    )
         # 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(lowerCAmelCase__    )
     except ValueError as e:
       logger.error(f"Failed to read file '{file}' with error {type(lowerCAmelCase__    )}: {e}"    )
       raise






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for i in range(len(__A			) - 1			):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  0
  for i in range(__A			):
    if index == -1:
      summation_value += _error(__A			)
    else:
      summation_value += _error(__A			) * train_data[i][0][index]
  return summation_value
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  for i in range(len(__A			)			):
    print(("Actual output value:", output(__A   ,       "test"			))			)
    print(("Hypothesis output:", calculate_hypothesis_value(__A   ,       "test"			))			)


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



