





'''simple docstring'''






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
		import torch







class 			lowerCamelCase					(						TensorFormatter[Mapping, "torch.Tensor", Mapping]  ):


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Any=None       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											super().__init__(features=__snake_case       )
											_snake_case:   str  					=		torch_tensor_kwargs
											import torch  # noqa import torch at initialization


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  Dict       ):





											'''simple docstring'''





											import torch

											if isinstance(__snake_case       ,     __snake_case       ) and column:
																	if all(
																	    isinstance(__snake_case       ,     torch.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype
																	    for x in column       ):
																							return torch.stack(__snake_case       )
											return column


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											import torch

											if isinstance(__snake_case       ,     (str, bytes, type(__snake_case       ))       ):
																	return value
											elif isinstance(__snake_case       ,     (np.character, np.ndarray)       ) and np.issubdtype(value.dtype       ,     np.character       ):
																	return value.tolist()

											_snake_case:   Dict  					=		{}

											if isinstance(__snake_case       ,     (np.number, np.ndarray)       ) and np.issubdtype(value.dtype       ,     np.integer       ):
																	_snake_case:   Optional[Any]  					=		{'dtype': torch.intaa}
											elif isinstance(__snake_case       ,     (np.number, np.ndarray)       ) and np.issubdtype(value.dtype       ,     np.floating       ):
																	_snake_case:   List[str]  					=		{'dtype': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(__snake_case       ,     PIL.Image.Image       ):
																							_snake_case:   Optional[Any]  					=		np.asarray(__snake_case       )
											return torch.tensor(__snake_case       ,     **{**default_dtype, **self.torch_tensor_kwargs}       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  str       ):





											'''simple docstring'''





											import torch

											# support for torch, tf, jax etc.
											if hasattr(__snake_case       ,     '__array__'       ) and not isinstance(__snake_case       ,     torch.Tensor       ):
																	_snake_case:   Optional[int]  					=		data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(__snake_case       ,     np.ndarray       ):
																	if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																							return self._consolidate([self.recursive_tensorize(__snake_case       ) for substruct in data_struct]       )
											elif isinstance(__snake_case       ,     (list, tuple)       ):
																	return self._consolidate([self.recursive_tensorize(__snake_case       ) for substruct in data_struct]       )
											return self._tensorize(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  dict       ):





											'''simple docstring'''





											return map_nested(self._recursive_tensorize       ,     __snake_case       ,     map_list=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  pa.Table       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.numpy_arrow_extractor().extract_row(__snake_case       )
											_snake_case:   List[str]  					=		self.python_features_decoder.decode_row(__snake_case       )
											return self.recursive_tensorize(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  pa.Table       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.numpy_arrow_extractor().extract_column(__snake_case       )
											_snake_case:   Dict  					=		self.python_features_decoder.decode_column(__snake_case       ,     pa_table.column_names[0]       )
											_snake_case:   Any  					=		self.recursive_tensorize(__snake_case       )
											_snake_case:   List[str]  					=		self._consolidate(__snake_case       )
											return column




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  pa.Table       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		self.numpy_arrow_extractor().extract_batch(__snake_case       )
											_snake_case:   Tuple  					=		self.python_features_decoder.decode_batch(__snake_case       )
											_snake_case:   List[Any]  					=		self.recursive_tensorize(__snake_case       )
											for column_name in batch:
																	_snake_case:   List[Any]  					=		self._consolidate(batch[column_name]       )
											return batch












'''simple docstring'''






from __future__ import annotations

A   : Union[str, Any]             =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A   : Optional[int]             =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						_snake_case:   List[Any]  					=		len(lowercase__						)

						for i in range(lowercase__						):
												_snake_case:   float  					=		-1
												for j in range(i + 1				,  lowercase__						):
																		if arr[i] < arr[j]:
																								_snake_case:   List[str]  					=		arr[j]
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						for i, outer in enumerate(lowercase__						):
												_snake_case:   float  					=		-1
												for inner in arr[i + 1 :]:
																		if outer < inner:
																								_snake_case:   List[Any]  					=		inner
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   int  					=		len(lowercase__						)
						_snake_case:   list[float]  					=		[]
						_snake_case:   list[float]  					=		[-1] * arr_size

						for index in reversed(range(lowercase__						)						):
												if stack:
																		while stack[-1] <= arr[index]:
																								stack.pop()
																								if not stack:
																														break
												if stack:
																		_snake_case:   Dict  					=		stack[-1]
												stack.append(arr[index]						)
						return result


if __name__ == "__main__":
		from doctest import testmod
		from timeit import timeit

		testmod()
		print(next_greatest_element_slow(arr))
		print(next_greatest_element_fast(arr))
		print(next_greatest_element(arr))

		A   : Union[str, Any]             =			(
		    'from __main__ import arr, next_greatest_element_slow, '
		    'next_greatest_element_fast, next_greatest_element'
		)
		print(
		    'next_greatest_element_slow():',
		    timeit('next_greatest_element_slow(arr)', setup=setup),
		)
		print(
		    'next_greatest_element_fast():',
		    timeit('next_greatest_element_fast(arr)', setup=setup),
		)
		print(
		    '     next_greatest_element():',
		    timeit('next_greatest_element(arr)', setup=setup),
		)





