






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

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCAmelCase_           = datasets.utils.logging.get_logger(__name__)
@dataclass
class      UpperCamelCase (				datasets.BuilderConfig ):







		"""simple docstring"""



		snake_case    =   None
		snake_case    =   "utf-8"
		snake_case    =   None
		snake_case    =   None
		snake_case    =   True  # deprecated
		snake_case    =   None  # deprecated
		snake_case    =   1_0 << 2_0  # 10MB
		snake_case    =   None


class      UpperCamelCase (				datasets.ArrowBasedBuilder ):







		"""simple docstring"""



		snake_case    =   JsonConfig
		def       A(  self		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if self.config.block_size is not None:
										logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'		)
										A	 =      self.config.block_size
						if self.config.use_threads is not True:
										logger.warning(
										    'The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'		)
						if self.config.newlines_in_values is not None:
										raise ValueError('The JSON loader parameter `newlines_in_values` is no longer supported'		)
						return datasets.DatasetInfo(features=self.config.features		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						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}'		)
						A	 =      dl_manager.download_and_extract(self.config.data_files		)
						if isinstance(UpperCAmelCase_						,(str, list, tuple)		):
										A	 =      data_files
										if isinstance(UpperCAmelCase_						,UpperCAmelCase_		):
														A	 =      [files]
										A	 =      [dl_manager.iter_files(UpperCAmelCase_		) for file in files]
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,gen_kwargs={'files': files}		)]
						A	 =      []
						for split_name, files in data_files.items():
										if isinstance(UpperCAmelCase_						,UpperCAmelCase_		):
														A	 =      [files]
										A	 =      [dl_manager.iter_files(UpperCAmelCase_		) for file in files]
										splits.append(datasets.SplitGenerator(name=UpperCAmelCase_						,gen_kwargs={'files': files}		)		)
						return splits
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Union[str, Any]:


						'''simple docstring'''
						if self.config.features is not None:
										# adding missing columns
										for column_name in set(self.config.features		) - set(pa_table.column_names		):
														A	 =      self.config.features.arrow_schema.field(UpperCAmelCase_		).type
														A	 =      pa_table.append_column(UpperCAmelCase_						,pa.array([None] * len(UpperCAmelCase_		)						,type=UpperCAmelCase_		)		)
										# more expensive cast to support nested structures with keys in a different order
										# allows str <-> int/float or str to Audio for example
										A	 =      table_cast(UpperCAmelCase_						,self.config.features.arrow_schema		)
						return pa_table







		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_		)		):
										# If the file is one json object and if we need to look at the list of items in one specific field
										if self.config.field is not None:
														with open(UpperCAmelCase_						,encoding=self.config.encoding						,errors=self.config.encoding_errors		) as f:
																		A	 =      json.load(UpperCAmelCase_		)

														# We keep only the field we are interested in
														A	 =      dataset[self.config.field]

														# We accept two format: a list of dicts or a dict of lists
														if isinstance(UpperCAmelCase_						,(list, tuple)		):
																		A	 =      set().union(*[row.keys() for row in dataset]		)
																		A	 =      {col: [row.get(UpperCAmelCase_		) for row in dataset] for col in keys}
														else:
																		A	 =      dataset
														A	 =      pa.Table.from_pydict(UpperCAmelCase_		)
														yield file_idx, self._cast_table(UpperCAmelCase_		)

										# If the file has one json object per line
										else:
														with open(UpperCAmelCase_						,'rb'		) as f:
																		A	 =      0
																		# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																		# Set a default minimum value of 16kB if the chunk size is really small
																		A	 =      max(self.config.chunksize // 3_2						,1_6 << 1_0		)
																		A	 =      (
																		    self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
																		)
																		while True:
																						A	 =      f.read(self.config.chunksize		)
																						if not batch:
																										break
																						# Finish current line
																						try:
																										batch += f.readline()
																						except (AttributeError, io.UnsupportedOperation):
																										batch += readline(UpperCAmelCase_		)
																						# PyArrow only accepts utf-8 encoded bytes
																						if self.config.encoding != "utf-8":
																										A	 =      batch.decode(self.config.encoding						,errors=UpperCAmelCase_		).encode('utf-8'		)
																						try:
																										while True:
																														try:
																																		A	 =      paj.read_json(
																																		    io.BytesIO(UpperCAmelCase_		)						,read_options=paj.ReadOptions(block_size=UpperCAmelCase_		)		)
																																		break
																														except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																		if (
																																		    isinstance(UpperCAmelCase_						,pa.ArrowInvalid		)
																																		    and "straddling" not in str(UpperCAmelCase_		)
																																		    or block_size > len(UpperCAmelCase_		)
																																		):
																																						raise
																																		else:
																																						# Increase the block size in case it was too small.
																																						# The block size will be reset for the next file.
																																						logger.debug(
																																						    f'Batch of {len(UpperCAmelCase_		)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'		)
																																						block_size *= 2
																						except pa.ArrowInvalid as e:
																										try:
																														with open(
																														    UpperCAmelCase_						,encoding=self.config.encoding						,errors=self.config.encoding_errors		) as f:
																																		A	 =      json.load(UpperCAmelCase_		)
																										except json.JSONDecodeError:
																														logger.error(f'Failed to read file \'{file}\' with error {type(UpperCAmelCase_		)}: {e}'		)
																														raise e
																										# If possible, parse the file as a list of json objects and exit the loop
																										if isinstance(UpperCAmelCase_						,UpperCAmelCase_		):  # list is the only sequence type supported in JSON
																														try:
																																		A	 =      set().union(*[row.keys() for row in dataset]		)
																																		A	 =      {col: [row.get(UpperCAmelCase_		) for row in dataset] for col in keys}
																																		A	 =      pa.Table.from_pydict(UpperCAmelCase_		)
																														except (pa.ArrowInvalid, AttributeError) as e:
																																		logger.error(f'Failed to read file \'{file}\' with error {type(UpperCAmelCase_		)}: {e}'		)
																																		raise ValueError(f'Not able to read records in the JSON file at {file}.'		) from None
																														yield file_idx, self._cast_table(UpperCAmelCase_		)
																														break
																										else:
																														logger.error(f'Failed to read file \'{file}\' with error {type(UpperCAmelCase_		)}: {e}'		)
																														raise ValueError(
																														    f'Not able to read records in the JSON file at {file}. '
																														    f'You should probably indicate the field of the JSON file containing your records. '
																														    f'This JSON file contain the following fields: {str(list(dataset.keys()		)		)}. '
																														    f'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '		) from None
                        # 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













from math import factorial
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      real
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      [1] * rank
						else:
										A	 =      rank
		def __repr__(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						return (
						    f'{self.real}+'
						    f'{"+".join(str(_SCREAMING_SNAKE_CASE		)+"E"+str(n+1		)for n,dual in enumerate(self.duals		)		)}'
						)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.duals.copy()
						while cur[-1] == 0:
										cur.pop(-1		)
						return Dual(self.real						,_SCREAMING_SNAKE_CASE		)
		def __add__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										return Dual(self.real + other						,self.duals		)
						A	 =      self.duals.copy()
						A	 =      other.duals.copy()
						if len(_SCREAMING_SNAKE_CASE		) > len(_SCREAMING_SNAKE_CASE		):
										o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						elif len(_SCREAMING_SNAKE_CASE		) < len(_SCREAMING_SNAKE_CASE		):
										s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						A	 =      []
						for i in range(len(_SCREAMING_SNAKE_CASE		)		):
										new_duals.append(s_dual[i] + o_dual[i]		)
						return Dual(self.real + other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __add__
		def __sub__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						return self + other * -1
		def __mul__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i * other		)
										return Dual(self.real * other						,_SCREAMING_SNAKE_CASE		)
						A	 =      [0] * (len(self.duals		) + len(other.duals		) + 1)
						for i, item in enumerate(self.duals		):
										for j, jtem in enumerate(other.duals		):
														new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals		)		):
										new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals		)		):
										new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __mul__
		def __truediv__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i / other		)
										return Dual(self.real / other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError
		def __floordiv__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   Dict:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i // other		)
										return Dual(self.real // other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError







		def __pow__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						if n < 0 or isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										raise ValueError('power must be a positive integer'		)
						if n == 0:
										return 1
						if n == 1:
										return self
						A	 =      self
						for _ in range(n - 1		):
										x *= self
						return x







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int   ):
				if not callable(UpperCAmelCase   ):
								raise ValueError('differentiate() requires a function as input for func'   )
				if not isinstance(UpperCAmelCase,						(float, int)   ):
								raise ValueError('differentiate() requires a float as input for position'   )
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								raise ValueError('differentiate() requires an int as input for order'   )
				A	 =      Dual(UpperCAmelCase,						1   )
				A	 =      func(UpperCAmelCase   )
				if order == 0:
								return result.real
				return result.duals[order - 1] * factorial(UpperCAmelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def      snake_case		(							UpperCAmelCase	:       int   ):
									return y**2 * y**4



					print(differentiate(f, 9, 2))





