





# 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 torch

class    lowerCAmelCase							(					TensorFormatter[Mapping, """torch.Tensor""", Mapping]			):
 def __init__(      self  :      str			,     UpperCAmelCase  :      str=None			,     **UpperCAmelCase  :      int	)			->	Optional[Any]:
     super().__init__(features=UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		torch_tensor_kwargs
     import torch  # noqa import torch at initialization
 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      List[Any]	)			->	Optional[Any]:
     import torch

     if isinstance(UpperCAmelCase			,     UpperCAmelCase	) and column:
         if all(
             isinstance(UpperCAmelCase			,     torch.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype
             for x in column	):
             return torch.stack(UpperCAmelCase	)
     return column
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Dict	)			->	List[Any]:
     import torch

     if isinstance(UpperCAmelCase			,     (str, bytes, type(UpperCAmelCase	))	):
         return value
     elif isinstance(UpperCAmelCase			,     (np.character, np.ndarray)	) and np.issubdtype(value.dtype			,     np.character	):
         return value.tolist()

     lowerCamelCase__    :      Union[str, Any]					 =		{}

     if isinstance(UpperCAmelCase			,     (np.number, np.ndarray)	) and np.issubdtype(value.dtype			,     np.integer	):
         lowerCamelCase__    :      Optional[Any]					 =		{'dtype': torch.intaa}
     elif isinstance(UpperCAmelCase			,     (np.number, np.ndarray)	) and np.issubdtype(value.dtype			,     np.floating	):
         lowerCamelCase__    :      List[Any]					 =		{'dtype': torch.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(UpperCAmelCase			,     PIL.Image.Image	):
             lowerCamelCase__    :      List[str]					 =		np.asarray(UpperCAmelCase	)
     return torch.tensor(UpperCAmelCase			,     **{**default_dtype, **self.torch_tensor_kwargs}	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Tuple	)			->	Optional[Any]:
     import torch

     # support for torch, tf, jax etc.
     if hasattr(UpperCAmelCase			,     '__array__'	) and not isinstance(UpperCAmelCase			,     torch.Tensor	):
         lowerCamelCase__    :      Optional[int]					 =		data_struct.__array__()
     # support for nested types like struct of list of struct
     if isinstance(UpperCAmelCase			,     np.ndarray	):
         if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(UpperCAmelCase	) for substruct in data_struct]	)
     elif isinstance(UpperCAmelCase			,     (list, tuple)	):
         return self._consolidate([self.recursive_tensorize(UpperCAmelCase	) for substruct in data_struct]	)
     return self._tensorize(UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      dict	)			->	List[str]:
     return map_nested(self._recursive_tensorize			,     UpperCAmelCase			,     map_list=UpperCAmelCase	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      pa.Table	)			->	Mapping:
     lowerCamelCase__    :      Tuple					 =		self.numpy_arrow_extractor().extract_row(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		self.python_features_decoder.decode_row(UpperCAmelCase	)
     return self.recursive_tensorize(UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      pa.Table	)			->	"torch.Tensor":
     lowerCamelCase__    :      Optional[int]					 =		self.numpy_arrow_extractor().extract_column(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.python_features_decoder.decode_column(UpperCAmelCase			,     pa_table.column_names[0]	)
     lowerCamelCase__    :      int					 =		self.recursive_tensorize(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		self._consolidate(UpperCAmelCase	)
     return column







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      pa.Table	)			->	Mapping:
     lowerCamelCase__    :      Optional[int]					 =		self.numpy_arrow_extractor().extract_batch(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		self.python_features_decoder.decode_batch(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.recursive_tensorize(UpperCAmelCase	)
     for column_name in batch:
         lowerCamelCase__    :      Optional[Any]					 =		self._consolidate(batch[column_name]	)
     return batch







"""simple docstring"""





from collections import defaultdict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = first_str.lower().strip()
		__SCREAMING_SNAKE_CASE            = second_str.lower().strip()

		# Remove whitespace
		__SCREAMING_SNAKE_CASE            = first_str.replace(""" """  ,				""""""				)
		__SCREAMING_SNAKE_CASE            = second_str.replace(""" """  ,				""""""				)

		# Strings of different lengths are not anagrams
		if len(UpperCamelCase_				) != len(UpperCamelCase_				):
				return False

		# Default values for count should be 0
		__SCREAMING_SNAKE_CASE            = defaultdict(UpperCamelCase_				)

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(UpperCamelCase_				)				):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							__magic_name__       =			input("Enter the first string ").strip()
							__magic_name__       =			input("Enter the second string ").strip()

							__magic_name__       =			check_anagrams(input_a, input_b)
							print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")




