





'''simple docstring'''






from __future__ import annotations

from typing import Any
class 					__snake_case  :






   '''simple docstring'''





   def __init__(							self	,		__SCREAMING_SNAKE_CASE = 6       ):
    snake_case__     :					Tuple    =  None
    snake_case__     :					Optional[Any]    =  None
    self.create_linked_list(__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Dict    =  Node()
    snake_case__     :					Dict    =  current_node
    snake_case__     :					Optional[int]    =  current_node
    snake_case__     :					List[str]    =  current_node
    for _ in range(1	,		__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					Optional[Any]    =  Node()
     snake_case__     :					List[Any]    =  current_node
     snake_case__     :					Any    =  previous_node
     snake_case__     :					str    =  current_node
    snake_case__     :					Dict    =  self.front
    snake_case__     :					Tuple    =  previous_node

   def 		__UpperCamelCase						(							self       ):
    return (
        self.front == self.rear
        and self.front is not None
        and self.front.data is None
    )

   def 		__UpperCamelCase						(							self       ):
    self.check_can_perform_operation()
    return self.front.data if self.front else None

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):







    if self.rear is None:
     return

    self.check_is_full()
    if not self.is_empty():
     snake_case__     :					Any    =  self.rear.next
    if self.rear:
     snake_case__     :					Tuple    =  data

   def 		__UpperCamelCase						(							self       ):
    self.check_can_perform_operation()
    if self.rear is None or self.front is None:
     return None
    if self.front == self.rear:
     snake_case__     :					Optional[Any]    =  self.front.data
     snake_case__     :					Union[str, Any]    =  None
     return data

    snake_case__     :					int    =  self.front
    snake_case__     :					int    =  old_front.next
    snake_case__     :					int    =  old_front.data
    snake_case__     :					Any    =  None
    return data

   def 		__UpperCamelCase						(							self       ):







    if self.is_empty():
     raise Exception("""Empty Queue"""       )


   def 		__UpperCamelCase						(							self       ):







    if self.rear and self.rear.next == self.front:
     raise Exception("""Full Queue"""       )



class 					__snake_case  :






   '''simple docstring'''





   def __init__(							self       ):
    snake_case__     :					List[str]    =  None
    snake_case__     :					Union[str, Any]    =  None
    snake_case__     :					int    =  None


if __name__ == "__main__":
    import doctest

    doctest.testmod()








'''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]	):





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple=None  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             super().__init__(features=UpperCamelCase					)
             lowercase__      =					torch_tensor_kwargs
             import torch  # noqa import torch at initialization



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

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



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             import torch

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

             lowercase__      =					{}

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

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



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             import torch

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



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       dict					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_row(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_row(UpperCamelCase					)
             return self.recursive_tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







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






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







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




