



"""simple docstring"""
from __future__ import annotations





def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	List[str]							):






    if len(UpperCAmelCase_							) == 0:
        raise ValueError('''find_min() arg is an empty sequence'''							)
    if (
        left >= len(UpperCAmelCase_							)
        or left < -len(UpperCAmelCase_							)
        or right >= len(UpperCAmelCase_							)
        or right < -len(UpperCAmelCase_							)
    ):
        raise IndexError('''list index out of range'''							)
    if left == right:
        return nums[left]
    _UpperCAmelCase							:						Union[str, Any]       							=					(left + right) >> 1  # the middle
    _UpperCAmelCase							:						int       							=					find_min(UpperCAmelCase_  , UpperCAmelCase_  , UpperCAmelCase_							)  # find min in range[left, mid]
    _UpperCAmelCase							:						Any       							=					find_min(UpperCAmelCase_  , mid + 1  , UpperCAmelCase_							)  # find min in range[mid + 1, right]

    return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)







"""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 tensorflow as tf
class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):





     '''simple docstring'''



     def __init__(      self ,			A=None ,			**A	)							->      List[str]:
         super().__init__(features=A	)
         _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
         import tensorflow as tf

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

         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      int:
         import tensorflow as tf

         if value is None:
             return value

         _UpperCAmelCase							:						Any       							=					{}

         if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
             _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
         elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
             _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A ,			PIL.Image.Image	):
                 _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)

         return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
         import tensorflow as tf

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

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





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





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





     def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
         _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
         _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
         for column_name in batch:
             _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
         return batch


