






"""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 tensorflow as tf







class 							_SCREAMING_SNAKE_CASE    (	TensorFormatter[Mapping, 'tf.Tensor', Mapping]				):





							"""simple docstring"""





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

												'''simple docstring'''

												super().__init__(features=__A     )
												a__ 							=	tf_tensor_kwargs
												import tensorflow as tf  # noqa: import tf at initialization




							def 			lowercase		( self:  Optional[int]					,		__A:  List[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if isinstance(__A					,		__A     ) and column:
																	if all(
																	    isinstance(__A					,		tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																						return tf.stack(__A     )
																	elif all(
																	    isinstance(__A					,		(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
																	    for x in column     ):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(__A     )

												return column




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if value is None:
																	return value

												a__ 							=	{}

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

																	if isinstance(__A					,		PIL.Image.Image     ):
																						a__ 							=	np.asarray(__A     )

												return tf.convert_to_tensor(__A					,		**{**default_dtype, **self.tf_tensor_kwargs}     )




							def 			lowercase		( self:  List[Any]					,		__A:  str     ):

												'''simple docstring'''

												import tensorflow as tf

												# 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					,		tf.Tensor     ):
																	a__ 							=	data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__A					,		np.ndarray     ):
																	if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												elif isinstance(__A					,		(list, tuple)     ):
																	return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												return self._tensorize(__A     )




							def 			lowercase		( self:  int					,		__A:  dict     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_row(__A     )
												a__ 							=	self.python_features_decoder.decode_row(__A     )
												return self.recursive_tensorize(__A     )




							def 			lowercase		( self:  Dict					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_column(__A     )
												a__ 							=	self.python_features_decoder.decode_column(__A					,		pa_table.column_names[0]     )
												a__ 							=	self.recursive_tensorize(__A     )
												a__ 							=	self._consolidate(__A     )
												return column





							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_batch(__A     )
												a__ 							=	self.python_features_decoder.decode_batch(__A     )
												a__ 							=	self.recursive_tensorize(__A     )
												for column_name in batch:
																	a__ 							=	self._consolidate(batch[column_name]     )
												return batch










"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Optional[int]					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	data
												a__ 							=	None





							def __str__( self:  str     ):

												'''simple docstring'''

												return F'{self.data}'







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  int     ):

												'''simple docstring'''

												a__ 							=	None
												a__ 							=	None




							def __iter__( self:  Tuple     ):

												'''simple docstring'''

												a__ 							=	self.front
												while node:
																	yield node.data
																	a__ 							=	node.next




							def __len__( self:  Union[str, Any]     ):

												'''simple docstring'''

												return len(tuple(iter(self     )     )     )




							def __str__( self:  Any     ):

												'''simple docstring'''

												return " <- ".join(str(__A     ) for item in self     )




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												return len(self     ) == 0




							def 			lowercase		( self:  Tuple					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	Node(__A     )
												if self.is_empty():
																	a__ 							=	a__ 							=	node
												else:
																	assert isinstance(self.rear					,		__A     )
																	a__ 							=	node
																	a__ 							=	node




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												if self.is_empty():
																	raise IndexError('''dequeue from empty queue'''     )
												assert isinstance(self.front					,		__A     )
												a__ 							=	self.front
												a__ 							=	self.front.next
												if self.front is None:
																	a__ 							=	None
												return node.data





							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												a__ 							=	a__ 							=	None


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

	testmod()


