
import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter





class 							A__		(     TensorFormatter[Mapping, np.ndarray, Mapping]):
      def __init__(				self   ,  __magic_name__=None   ,  **__magic_name__				):
       super().__init__(features=__magic_name__				)
       lowerCamelCase		:     Optional[Any]          =				np_array_kwargs
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       if isinstance(__magic_name__   ,  __magic_name__				):
        if column and all(
            isinstance(__magic_name__   ,  np.ndarray				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
         return np.stack(__magic_name__				)
        else:
         # don't use np.array(column, dtype=object)
         # since it fails in certain cases
         # see https://stackoverflow.com/q/51005699
         lowerCamelCase		:     Tuple          =				np.empty(len(__magic_name__				)   ,  dtype=__magic_name__				)
         lowerCamelCase		:     int          =				column
         return out
       return column
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       if isinstance(__magic_name__   ,  (str, bytes, type(__magic_name__				))				):
        return value
       elif isinstance(__magic_name__   ,  (np.character, np.ndarray)				) and np.issubdtype(value.dtype   ,  np.character				):
        return value
       elif isinstance(__magic_name__   ,  np.number				):
        return value

       lowerCamelCase		:     Union[str, Any]          =				{}

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

        if isinstance(__magic_name__   ,  PIL.Image.Image				):
         return np.asarray(__magic_name__   ,  **self.np_array_kwargs				)

       return np.asarray(__magic_name__   ,  **{**default_dtype, **self.np_array_kwargs}				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
        import torch

        if isinstance(__magic_name__   ,  torch.Tensor				):
         return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
       if hasattr(__magic_name__   ,  """__array__"""				) and not isinstance(__magic_name__   ,  (np.ndarray, np.character, np.number)				):
        lowerCamelCase		:     List[str]          =				data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(__magic_name__   ,  np.ndarray				):
        if data_struct.dtype == object:
         return self._consolidate([self.recursive_tensorize(__magic_name__				) for substruct in data_struct]				)
       if isinstance(__magic_name__   ,  (list, tuple)				):
        return self._consolidate([self.recursive_tensorize(__magic_name__				) for substruct in data_struct]				)
       return self._tensorize(__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       return map_nested(self._recursive_tensorize   ,  __magic_name__   ,  map_list=__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     Any          =				self.numpy_arrow_extractor().extract_row(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				self.python_features_decoder.decode_row(__magic_name__				)
       return self.recursive_tensorize(__magic_name__				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     int          =				self.numpy_arrow_extractor().extract_column(__magic_name__				)
       lowerCamelCase		:     List[str]          =				self.python_features_decoder.decode_column(__magic_name__   ,  pa_table.column_names[0]				)
       lowerCamelCase		:     Tuple          =				self.recursive_tensorize(__magic_name__				)
       lowerCamelCase		:     Optional[Any]          =				self._consolidate(__magic_name__				)
       return column






      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase		:     List[Any]          =				self.numpy_arrow_extractor().extract_batch(__magic_name__				)
       lowerCamelCase		:     str          =				self.python_features_decoder.decode_batch(__magic_name__				)
       lowerCamelCase		:     List[str]          =				self.recursive_tensorize(__magic_name__				)
       for column_name in batch:
        lowerCamelCase		:     Optional[int]          =				self._consolidate(batch[column_name]				)
       return batch






import sys





def    _a (						lowerCamelCase,     lowerCamelCase							):
 if not numbers:
  return 0
 if target == 0 and target in numbers:
  return 0
 if not isinstance(lowerCamelCase,     (list, tuple)							) or not all(
     isinstance(lowerCamelCase,     lowerCamelCase							) for number in numbers							):
  raise ValueError("""numbers must be an iterable of integers"""							)

 lowerCamelCase		:     str          =				0
 lowerCamelCase		:     List[str]          =				sys.maxsize

 while right < len(lowerCamelCase							):
  curr_sum += numbers[right]
  while curr_sum >= target and left <= right:
   lowerCamelCase		:     Any          =				min(lowerCamelCase,     right - left + 1							)
   curr_sum -= numbers[left]
   left += 1
  right += 1

 return 0 if min_len == sys.maxsize else min_len




