'''simple docstring'''


from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def    _lowerCamelCase       ( lowerCamelCase_   :				Dict  ):



     """simple docstring"""





     if is_torch_available():
          import torch

          if isinstance(lowerCamelCase_      ,	torch.Tensor  ):
               return True

     return isinstance(lowerCamelCase_      ,	np.ndarray  )






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = fields(self		)

       # Safety and consistency checks
       if not len(snake_case_		):
            raise ValueError(F'''{self.__class__.__name__} has no fields.'''		)

       UpperCAmelCase_				:  Tuple  = getattr(self ,						class_fields[0].name		)
       UpperCAmelCase_				:  str  = all(getattr(self ,						field.name		) is None for field in class_fields[1:]		)

       if other_fields_are_none and isinstance(snake_case_ ,						snake_case_		):
            for key, value in first_field.items():
                 UpperCAmelCase_				:  Optional[Any]  = value
       else:
            for field in class_fields:
                 UpperCAmelCase_				:  Dict  = getattr(self ,						field.name		)
                 if v is not None:
                      UpperCAmelCase_				:  Optional[Any]  = v


  def __delitem__(					self ,						*snake_case_ ,						**snake_case_		):




       '''simple docstring'''

       raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''		)


  def _UpperCamelCase	(					self ,						*snake_case_ ,						**snake_case_		):




       '''simple docstring'''

       raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''		)


  def _UpperCamelCase	(					self ,						*snake_case_ ,						**snake_case_		):




       '''simple docstring'''

       raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''		)


  def _UpperCamelCase	(					self ,						*snake_case_ ,						**snake_case_		):




       '''simple docstring'''

       raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''		)


  def __getitem__(					self ,						snake_case_		):




       '''simple docstring'''

       if isinstance(snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  Tuple  = dict(self.items()		)
            return inner_dict[k]
       else:
            return self.to_tuple()[k]


  def __setattr__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if name in self.keys() and value is not None:
            # Don't call self.__setitem__ to avoid recursion errors
            super().__setitem__(snake_case_ ,						snake_case_		)
       super().__setattr__(snake_case_ ,						snake_case_		)


  def __setitem__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       super().__setitem__(snake_case_ ,						snake_case_		)
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return tuple(self[k] for k in self.keys()		)














"""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


