code: # 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    __lowerCAmelCase  (       TensorFormatter[Mapping, 'tf.Tensor', Mapping]						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Dict       ,			lowerCamelCase       :		Any=None       ,			**lowerCamelCase       :		str  ) ->							List[Any]:
          super().__init__(features=lowerCamelCase  )
          _UpperCamelCase     :       str  						=  tf_tensor_kwargs
          import tensorflow as tf  # noqa: import tf at initialization
  
   def 					__snake_case					(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[str, Any]  ) ->							Any:
          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 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Union[str, Any]  ) ->							List[str]:
          import tensorflow as tf
  
          if value is None:
                 return value
  
          _UpperCamelCase     :       List[Any]  						=  {}
  
          if isinstance(lowerCamelCase       ,			(np.number, np.ndarray)  ) and np.issubdtype(value.dtype       ,			np.integer  ):
                 _UpperCamelCase     :       int  						=  {'dtype': tf.intaa}
          elif isinstance(lowerCamelCase       ,			(np.number, np.ndarray)  ) and np.issubdtype(value.dtype       ,			np.floating  ):
                 _UpperCamelCase     :       List[Any]  						=  {'dtype': tf.floataa}
          elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image
  
                 if isinstance(lowerCamelCase       ,			PIL.Image.Image  ):
                        _UpperCamelCase     :       Union[str, Any]  						=  np.asarray(lowerCamelCase  )
  
          return tf.convert_to_tensor(lowerCamelCase       ,			**{**default_dtype, **self.tf_tensor_kwargs}  )
  
   def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		Any  ) ->							str:
          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  ):
                 _UpperCamelCase     :       List[Any]  						=  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 					__snake_case					(			self       :		Tuple       ,			lowerCamelCase       :		dict  ) ->							Optional[Any]:
          return map_nested(self._recursive_tensorize       ,			lowerCamelCase       ,			map_list=lowerCamelCase  )
  
   def 					__snake_case					(			self       :		int       ,			lowerCamelCase       :		pa.Table  ) ->							Mapping:
          _UpperCamelCase     :       List[Any]  						=  self.numpy_arrow_extractor().extract_row(lowerCamelCase  )
          _UpperCamelCase     :       int  						=  self.python_features_decoder.decode_row(lowerCamelCase  )
          return self.recursive_tensorize(lowerCamelCase  )
  
   def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		pa.Table  ) ->							"tf.Tensor":
          _UpperCamelCase     :       str  						=  self.numpy_arrow_extractor().extract_column(lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  self.python_features_decoder.decode_column(lowerCamelCase       ,			pa_table.column_names[0]  )
          _UpperCamelCase     :       Tuple  						=  self.recursive_tensorize(lowerCamelCase  )
          _UpperCamelCase     :       Dict  						=  self._consolidate(lowerCamelCase  )
          return column
  
   def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		pa.Table  ) ->							Mapping:
          _UpperCamelCase     :       Optional[Any]  						=  self.numpy_arrow_extractor().extract_batch(lowerCamelCase  )
          _UpperCamelCase     :       Optional[int]  						=  self.python_features_decoder.decode_batch(lowerCamelCase  )
          _UpperCamelCase     :       List[Any]  						=  self.recursive_tensorize(lowerCamelCase  )
          for column_name in batch:
                 _UpperCamelCase     :       Optional[Any]  						=  self._consolidate(batch[column_name]  )
          return batch
  
  
  
  
style_context: from __future__ import annotations
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int				):
         assert (
             c % greatest_common_divisor(lowercase_		,						lowercase_				) == 0
         )  # greatest_common_divisor(a,b) function implemented below
         ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       int  						=  extended_gcd(lowercase_		,						lowercase_				)  # extended_gcd(a,b) function implemented below
         _UpperCamelCase     :       List[Any]  						=  c / d
         return (r * x, r * y)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int = 2				):
         ((_UpperCamelCase)						,      (_UpperCamelCase))     :       Tuple  						=  diophantine(lowercase_		,						lowercase_		,						lowercase_				)  # Initial value
         _UpperCamelCase     :       List[Any]  						=  greatest_common_divisor(lowercase_		,						lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  a // d
         _UpperCamelCase     :       Optional[Any]  						=  b // d
  
         for i in range(lowercase_				):
                _UpperCamelCase     :       Union[str, Any]  						=  xa + i * q
                _UpperCamelCase     :       List[Any]  						=  ya - i * p
                print(lowercase_		,						lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
         if a < b:
                _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  b, a
  
         while a % b != 0:
                _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  b, a % b
  
         return b
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
         assert a >= 0 and b >= 0
  
         if b == 0:
                _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  a, 1, 0
         else:
                ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       Any  						=  extended_gcd(lowercase_		,						a % b				)
                _UpperCamelCase     :       Any  						=  q
                _UpperCamelCase     :       str  						=  p - q * (a // b)
  
         assert a % d == 0 and b % d == 0
         assert d == a * x + b * y
  
         return (d, x, y)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod(name='''diophantine''', verbose=True)
   testmod(name='''diophantine_all_soln''', verbose=True)
   testmod(name='''extended_gcd''', verbose=True)
   testmod(name='''greatest_common_divisor''', verbose=True)
  
  
  
  