
# 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] ):
     def __init__(	self  :		str					,	__lowerCamelCase  :		int=None					,	**__lowerCamelCase  :		str	):



       """simple docstring"""
       super().__init__(features=__lowerCamelCase	)
       lowerCAmelCase__								=			tf_tensor_kwargs
       import tensorflow as tf  # noqa: import tf at initialization
     def 					A__			(	self  :		Any					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       import tensorflow as tf

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

       return column
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		Tuple	):



       """simple docstring"""
       import tensorflow as tf

       if value is None:
         return value

       lowerCAmelCase__								=			{}

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

         if isinstance(__lowerCamelCase					,	PIL.Image.Image	):
           lowerCAmelCase__								=			np.asarray(__lowerCamelCase	)

       return tf.convert_to_tensor(__lowerCamelCase					,	**{**default_dtype, **self.tf_tensor_kwargs}	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		Dict	):



       """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(__lowerCamelCase					,	torch.Tensor	):
           return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
       if hasattr(__lowerCamelCase					,	'''__array__'''	) and not isinstance(__lowerCamelCase					,	tf.Tensor	):
         lowerCAmelCase__								=			data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(__lowerCamelCase					,	np.ndarray	):
         if data_struct.dtype == object:  # tf 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 					A__			(	self  :		str					,	__lowerCamelCase  :		dict	):



       """simple docstring"""
       return map_nested(self._recursive_tensorize					,	__lowerCamelCase					,	map_list=__lowerCamelCase	)
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		pa.Table	):



       """simple docstring"""
       lowerCAmelCase__								=			self.numpy_arrow_extractor().extract_row(__lowerCamelCase	)
       lowerCAmelCase__								=			self.python_features_decoder.decode_row(__lowerCamelCase	)
       return self.recursive_tensorize(__lowerCamelCase	)
     def 					A__			(	self  :		Any					,	__lowerCamelCase  :		pa.Table	):



       """simple docstring"""
       lowerCAmelCase__								=			self.numpy_arrow_extractor().extract_column(__lowerCamelCase	)
       lowerCAmelCase__								=			self.python_features_decoder.decode_column(__lowerCamelCase					,	pa_table.column_names[0]	)
       lowerCAmelCase__								=			self.recursive_tensorize(__lowerCamelCase	)
       lowerCAmelCase__								=			self._consolidate(__lowerCamelCase	)
       return column



     def 					A__			(	self  :		int					,	__lowerCamelCase  :		pa.Table	):



       """simple docstring"""
       lowerCAmelCase__								=			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase	)
       lowerCAmelCase__								=			self.python_features_decoder.decode_batch(__lowerCamelCase	)
       lowerCAmelCase__								=			self.recursive_tensorize(__lowerCamelCase	)
       for column_name in batch:
         lowerCAmelCase__								=			self._consolidate(batch[column_name]	)
       return batch







from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		int					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			data
       lowerCAmelCase__								=			None



     def __str__(	self  :		int	):



       """simple docstring"""
       return F"""{self.data}"""





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			None
     def __iter__(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.front
       while node:
         yield node.data
         lowerCAmelCase__								=			node.next
     def __len__(	self  :		int	):



       """simple docstring"""
       return len(tuple(iter(self	)	)	)
     def __str__(	self  :		Optional[int]	):



       """simple docstring"""
       return " <- ".join(str(__lowerCamelCase	) for item in self	)
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       return len(self	) == 0
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			Node(__lowerCamelCase	)
       if self.is_empty():
         lowerCAmelCase__								=			lowerCAmelCase__								=			node
       else:
         assert isinstance(self.rear					,	__lowerCamelCase	)
         lowerCAmelCase__								=			node
         lowerCAmelCase__								=			node
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       if self.is_empty():
         raise IndexError('''dequeue from empty queue'''	)
       assert isinstance(self.front					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self.front
       lowerCAmelCase__								=			self.front.next
       if self.front is None:
         lowerCAmelCase__								=			None
       return node.data



     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			lowerCAmelCase__								=			None


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

      testmod()

