
def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    if start == end:
        return start if lst[start] > item else start + 1
    if start > end:
        return start

    SCREAMING_SNAKE_CASE				:				int				      = (start + end) // 2
    if lst[mid] < item:
        return binary_search(a__ ,						a__ ,						mid + 1 ,						a__   )
    elif lst[mid] > item:
        return binary_search(a__ ,						a__ ,						a__ ,						mid - 1   )
    else:
        return mid







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = len(a__   )

    for index in range(1 ,						a__   ):
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = lst[index]
        SCREAMING_SNAKE_CASE				:				Any				      = binary_search(a__ ,						a__ ,						0 ,						index - 1   )
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

    return lst







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    if not left:
        return right

    if not right:
        return left

    if left[0] < right[0]:
        return [left[0], *merge(left[1:] ,						a__   )]

    return [right[0], *merge(a__ ,						right[1:]   )]







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = len(a__   )
    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = [], []
    SCREAMING_SNAKE_CASE				:				Tuple				      = [lst[0]]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
    SCREAMING_SNAKE_CASE				:				int				      = 1
    while i < length:
        if lst[i] < lst[i - 1]:
            runs.append(a__   )
            SCREAMING_SNAKE_CASE				:				Tuple				      = [lst[i]]
        else:
            new_run.append(lst[i]   )
        i += 1
    runs.append(a__   )

    for run in runs:
        sorted_runs.append(insertion_sort(a__   )   )
    for run in sorted_runs:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = merge(a__ ,						a__   )

    return sorted_array







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Tuple				      = [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
    SCREAMING_SNAKE_CASE				:				List[str]				      = tim_sort(a__   )
    print(a__   )


if __name__ == "__main__":
      main()


# 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
