
'''simple docstring'''







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


class       lowerCAmelCase			(					TensorFormatter[Mapping, np.ndarray, Mapping]      ):




		def __init__(					self       ,    UpperCamelCase=None       ,    **UpperCamelCase   ):
								super().__init__(features=__A   )
								_SCREAMING_SNAKE_CASE					=			np_array_kwargs




		def      lowercase							(					self       ,    UpperCamelCase   ):
								if isinstance(__A       ,    __A   ):
														if column and all(
														    isinstance(__A       ,    np.ndarray   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
																				return np.stack(__A   )
														else:
																				# don't use np.array(column, dtype=object)
																				# since it fails in certain cases
																				# see https://stackoverflow.com/q/51005699
																				_SCREAMING_SNAKE_CASE					=			np.empty(len(__A   )       ,    dtype=__A   )
																				_SCREAMING_SNAKE_CASE					=			column
																				return out
								return column




		def      lowercase							(					self       ,    UpperCamelCase   ):
								if isinstance(__A       ,    (str, bytes, type(__A   ))   ):
														return value
								elif isinstance(__A       ,    (np.character, np.ndarray)   ) and np.issubdtype(value.dtype       ,    np.character   ):
														return value
								elif isinstance(__A       ,    np.number   ):
														return value

								_SCREAMING_SNAKE_CASE					=			{}

								if isinstance(__A       ,    np.ndarray   ) and np.issubdtype(value.dtype       ,    np.integer   ):
														_SCREAMING_SNAKE_CASE					=			{"""dtype""": np.intaa}
								elif isinstance(__A       ,    np.ndarray   ) and np.issubdtype(value.dtype       ,    np.floating   ):
														_SCREAMING_SNAKE_CASE					=			{"""dtype""": np.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
														import PIL.Image

														if isinstance(__A       ,    PIL.Image.Image   ):
																				return np.asarray(__A       ,    **self.np_array_kwargs   )

								return np.asarray(__A       ,    **{**default_dtype, **self.np_array_kwargs}   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(__A       ,    torch.Tensor   ):
																				return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
								if hasattr(__A       ,    "__array__"   ) and not isinstance(__A       ,    (np.ndarray, np.character, np.number)   ):
														_SCREAMING_SNAKE_CASE					=			data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(__A       ,    np.ndarray   ):
														if data_struct.dtype == object:
																				return self._consolidate([self.recursive_tensorize(__A   ) for substruct in data_struct]   )
								if isinstance(__A       ,    (list, tuple)   ):
														return self._consolidate([self.recursive_tensorize(__A   ) for substruct in data_struct]   )
								return self._tensorize(__A   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								return map_nested(self._recursive_tensorize       ,    __A       ,    map_list=__A   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.numpy_arrow_extractor().extract_row(__A   )
								_SCREAMING_SNAKE_CASE					=			self.python_features_decoder.decode_row(__A   )
								return self.recursive_tensorize(__A   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.numpy_arrow_extractor().extract_column(__A   )
								_SCREAMING_SNAKE_CASE					=			self.python_features_decoder.decode_column(__A       ,    pa_table.column_names[0]   )
								_SCREAMING_SNAKE_CASE					=			self.recursive_tensorize(__A   )
								_SCREAMING_SNAKE_CASE					=			self._consolidate(__A   )
								return column




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.numpy_arrow_extractor().extract_batch(__A   )
								_SCREAMING_SNAKE_CASE					=			self.python_features_decoder.decode_batch(__A   )
								_SCREAMING_SNAKE_CASE					=			self.recursive_tensorize(__A   )
								for column_name in batch:
														_SCREAMING_SNAKE_CASE					=			self._consolidate(batch[column_name]   )
								return batch

"""simple docstring"""






import math as m






def 		_snake_case			(      lowercase__						:    Any ,	lowercase__						:    Any ,	lowercase__						:    Optional[int]=0.001						)    ->							Tuple:


			'''simple docstring'''





			return (f(a + h						) - f(a - h						)) / (2 * h)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    List[str]=0 ,	lowercase__						:    Union[str, Any]=1_0_0 ,	lowercase__						:    Any=0.0001 ,	lowercase__						:    List[str]=1E-6 ,	lowercase__						:    str=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    xa  # set the initial guess
			lowerCAmelCase_       :int     	=    [a]
			lowerCAmelCase_       :int     	=    abs(f(lowercase__						)						)
			lowerCAmelCase_       :Optional[int]     	=    lambda lowercase__						: calc_derivative(lowercase__ ,	lowercase__ ,	h=lowercase__						)  # noqa: E731  Derivative of f(x)
			for _ in range(lowercase__						):
						if fa(lowercase__						) == 0:
									raise ValueError("""No converging solution found"""						)
						lowerCAmelCase_       :Tuple     	=    a - f(lowercase__						) / fa(lowercase__						)  # Calculate the next estimate
						if logsteps:
									steps.append(lowercase__						)
						if error < maxerror:
									break
			else:
						raise ValueError("""Iteration limit reached, no converging solution found"""						)
			if logsteps:
						# If logstep is true, then log intermediate steps
						return a, error, steps
			return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				__UpperCAmelCase 					=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				__UpperCAmelCase					,					__UpperCAmelCase					,					__UpperCAmelCase 					=    newton_raphson(
				    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel('step')
				plt.ylabel('error')
				plt.show()
				print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")






