
# 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 	UpperCAmelCase_      (			TensorFormatter[Mapping, """tf.Tensor""", Mapping]					):


  '''simple docstring'''







  def __init__(			self      :			Any      ,							UpperCamelCase__      :			List[Any]=None      ,							**UpperCamelCase__      :			str   )      ->     Dict:







    """simple docstring"""






    super().__init__(features=UpperCamelCase__   )
    __magic_name__    					=	tf_tensor_kwargs
    import tensorflow as tf  # noqa: import tf at initialization
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    import tensorflow as tf

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

    return column
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    import tensorflow as tf

    if value is None:
      return value

    __magic_name__    					=	{}

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

      if isinstance(UpperCamelCase__      ,							PIL.Image.Image   ):
        __magic_name__    					=	np.asarray(UpperCamelCase__   )

    return tf.convert_to_tensor(UpperCamelCase__      ,							**{**default_dtype, **self.tf_tensor_kwargs}   )
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			Tuple   )      ->     List[Any]:







    """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(UpperCamelCase__      ,							torch.Tensor   ):
        return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
    if hasattr(UpperCamelCase__      ,							"""__array__"""   ) and not isinstance(UpperCamelCase__      ,							tf.Tensor   ):
      __magic_name__    					=	data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(UpperCamelCase__      ,							np.ndarray   ):
      if data_struct.dtype == object:  # tf 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 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			dict   )      ->     Tuple:







    """simple docstring"""






    return map_nested(self._recursive_tensorize      ,							UpperCamelCase__      ,							map_list=UpperCamelCase__   )
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			pa.Table   )      ->     Mapping:







    """simple docstring"""






    __magic_name__    					=	self.numpy_arrow_extractor().extract_row(UpperCamelCase__   )
    __magic_name__    					=	self.python_features_decoder.decode_row(UpperCamelCase__   )
    return self.recursive_tensorize(UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			pa.Table   )      ->     "tf.Tensor":







    """simple docstring"""






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





  def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			pa.Table   )      ->     Mapping:







    """simple docstring"""






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



import sys

sys.set_int_max_str_digits(0)  # type: ignore

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11
  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  if not f:
    return f

  # mark first 9 numbers
  __magic_name__    					=	int(str(A_ )[:9] )

  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10

  # check first 9 numbers for pandigitality

  for x in range(9 ):
    if not check_front[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers

  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers mod 1e9

  # mod m=1e9, done for fast optimisation
  __magic_name__    					=	[0] * 1000000
  __magic_name__    					=	1000000000

  for x in range(1000000 ):
    __magic_name__    					=	(aa + ba) % m
    __magic_name__    					=	ba % m
    __magic_name__    					=	ca % m
    if checka(A_ ):
      __magic_name__    					=	1

  for x in range(1000000 ):
    __magic_name__    					=	a + b
    __magic_name__    					=	b
    __magic_name__    					=	c
    # perform check only if in tocheck
    if tocheck[x + 3] and check(A_ ):
      return x + 3  # first 2 already done
  return -1


if __name__ == "__main__":
    print(F'''{solution() = }''')

