# 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




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)



