
"""simple docstring"""






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 				lowercase__(				TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
	'''simple docstring'''

	def __init__(						self    :str		, lowerCamelCase_    :List[Any]=None		, **lowerCamelCase_    :Any					)							->			Any:







			'''simple docstring'''




			super().__init__(features=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= torch_tensor_kwargs
			import torch  # noqa import torch at initialization





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Dict					)							->			Any:







			'''simple docstring'''




			import torch

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





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :Dict					)							->			List[Any]:







			'''simple docstring'''




			import torch

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

			SCREAMING_SNAKE_CASE       :     Optional[int]				= {}

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

					if isinstance(lowerCamelCase_		, PIL.Image.Image					):
							SCREAMING_SNAKE_CASE       :     Any				= np.asarray(lowerCamelCase_					)
			return torch.tensor(lowerCamelCase_		, **{**default_dtype, **self.torch_tensor_kwargs}					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :int					)							->			Union[str, Any]:







			'''simple docstring'''




			import torch

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





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :dict					)							->			Dict:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :pa.Table					)							->			Mapping:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.numpy_arrow_extractor().extract_row(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.python_features_decoder.decode_row(lowerCamelCase_					)
			return self.recursive_tensorize(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :pa.Table					)							->			"torch.Tensor":







			'''simple docstring'''




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







	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :pa.Table					)							->			Mapping:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.numpy_arrow_extractor().extract_batch(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Tuple				= self.python_features_decoder.decode_batch(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.recursive_tensorize(lowerCamelCase_					)
			for column_name in batch:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self._consolidate(batch[column_name]					)
			return batch







"""simple docstring"""






def __A						(   a_ :							list		,		a_ :							int		,		a_ :							int = 0		,		a_ :							int = 0     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= right or len(a_     ) - 1
		if left > right:
				return -1
		elif list_data[left] == key:
				return left
		elif list_data[right] == key:
				return right
		else:
				return search(a_		,		a_		,		left + 1		,		right - 1     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





