



from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      1
     lowerCAmelCase       =      2
     lowerCAmelCase       =      3





     def __repr__(							self			:				Tuple					)->int:
      return F"""{self.__class__.__name__}.{self.name}"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  tuple[int, int]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						point
 return degrees(atana(y - miny		,						x - minx  )  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  tuple[int, int]		,						__lowerCamelCase :  tuple[int, int]		,						__lowerCamelCase :  tuple[int, int]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[str]							=						starting
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						via
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						target
 __SCREAMING_SNAKE_CASE							:     Any							=						degrees(atana(ya - ya		,						xa - xa  )  )
 via_angle %= 360
 __SCREAMING_SNAKE_CASE							:     List[str]							=						degrees(atana(ya - ya		,						xa - xa  )  )
 target_angle %= 360
 # t-
 #  \ \
 #   \ v
 #    \|
 #     s
 # via_angle is always lower than target_angle, if direction is left.
 # If they are same, it means they are on a same line of convex hull.
 if target_angle > via_angle:
  return Direction.left
 elif target_angle == via_angle:
  return Direction.straight
 else:
  return Direction.right






def 						_lowerCAmelCase    (					__lowerCamelCase :  list[tuple[int, int]]  ):



 """simple docstring"""





 if len(__lowerCamelCase  ) <= 2:
  # There is no convex hull
  raise ValueError("graham_scan: argument must contain more than 3 points."  )
 if len(__lowerCamelCase  ) == 3:
  return points
 # find the lowest and the most left point
 __SCREAMING_SNAKE_CASE							:     List[str]							=						0
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						maxsize, maxsize
 for i, point in enumerate(__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Any							=						point[0]
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						point[1]
  if y < miny:
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						y
   __SCREAMING_SNAKE_CASE							:     Dict							=						x
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						i
  if y == miny and x < minx:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						x
   __SCREAMING_SNAKE_CASE							:     Dict							=						i

    # remove the lowest and the most left point from points for preparing for sort
 points.pop(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						sorted(__lowerCamelCase		,						key=lambda __lowerCamelCase  : angle_comparer(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )  )
 # This insert actually costs complexity,
 # and you should instead add (minx, miny) into stack later.
 # I'm using insert just for easy understanding.
 sorted_points.insert(0		,						(minx, miny)  )

 __SCREAMING_SNAKE_CASE							:     deque[tuple[int, int]]							=						deque()
 stack.append(sorted_points[0]  )
 stack.append(sorted_points[1]  )
 stack.append(sorted_points[2]  )
 # In any ways, the first 3 points line are towards left.
 # Because we sort them the angle from minx, miny.
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						Direction.left

 for i in range(3		,						len(__lowerCamelCase  )  ):
  while True:
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						stack[-2]
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						stack[-1]
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sorted_points[i]
   __SCREAMING_SNAKE_CASE							:     str							=						check_direction(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )

   if next_direction == Direction.left:
    __SCREAMING_SNAKE_CASE							:     Optional[int]							=						Direction.left
    break
   if next_direction == Direction.straight:
    if current_direction == Direction.left:
     # We keep current_direction as left.
     # Because if the straight line keeps as straight,
     # we want to know if this straight line is towards left.
     break
    elif current_direction == Direction.right:
     # If the straight line is towards right,
     # every previous points on those straigh line is not convex hull.
     stack.pop()
   if next_direction == Direction.right:
    stack.pop()
  stack.append(sorted_points[i]  )
 return list(__lowerCamelCase  )








# 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     a_       (			TensorFormatter[Mapping, 'tf.Tensor', Mapping]							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->int:
           super().__init__(features=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           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 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Union[str, Any]:
           import tensorflow as tf

           if value is None:
               return value

           SCREAMING_SNAKE_CASE				:				int				      = {}

           if isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.integer   ):
               SCREAMING_SNAKE_CASE				:				List[Any]				      = {'''dtype''': tf.intaa}
           elif isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.floating   ):
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''dtype''': tf.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 tf.convert_to_tensor(_lowerCamelCase				,       **{**default_dtype, **self.tf_tensor_kwargs}   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           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   ):
               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:  # 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           return map_nested(self._recursive_tensorize				,       _lowerCamelCase				,       map_list=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				str				      = self.numpy_arrow_extractor().extract_row(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.python_features_decoder.decode_row(_lowerCamelCase   )
           return self.recursive_tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->"tf.Tensor":
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.numpy_arrow_extractor().extract_column(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = self.python_features_decoder.decode_column(_lowerCamelCase				,       pa_table.column_names[0]   )
           SCREAMING_SNAKE_CASE				:				str				      = self.recursive_tensorize(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self._consolidate(_lowerCamelCase   )
           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.numpy_arrow_extractor().extract_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = self.python_features_decoder.decode_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self.recursive_tensorize(_lowerCamelCase   )
           for column_name in batch:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self._consolidate(batch[column_name]   )
           return batch
