

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]       ):
						"""simple docstring"""






						def __init__(						self     ,			_lowercase=None     ,			**_lowercase						)    ->   List[str]:
									super().__init__(features=_lowercase						)
									a_	:	Optional[int]          =   np_array_kwargs





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Tuple:
									if isinstance(_lowercase     ,			_lowercase						):
												if column and all(
												    isinstance(_lowercase     ,			np.ndarray						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
															return np.stack(_lowercase						)
												else:
															# don't use np.array(column, dtype=object)
															# since it fails in certain cases
															# see https://stackoverflow.com/q/51005699
															a_	:	List[Any]          =   np.empty(len(_lowercase						)     ,			dtype=_lowercase						)
															a_	:	Any          =   column
															return out
									return column





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Union[str, Any]:
									if isinstance(_lowercase     ,			(str, bytes, type(_lowercase						))						):
												return value
									elif isinstance(_lowercase     ,			(np.character, np.ndarray)						) and np.issubdtype(value.dtype     ,			np.character						):
												return value
									elif isinstance(_lowercase     ,			np.number						):
												return value

									a_	:	int          =   {}

									if isinstance(_lowercase     ,			np.ndarray						) and np.issubdtype(value.dtype     ,			np.integer						):
												a_	:	List[str]          =   {"""dtype""": np.intaa}
									elif isinstance(_lowercase     ,			np.ndarray						) and np.issubdtype(value.dtype     ,			np.floating						):
												a_	:	Dict          =   {"""dtype""": np.floataa}
									elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

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

									return np.asarray(_lowercase     ,			**{**default_dtype, **self.np_array_kwargs}						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Optional[int]:
									# support for torch, tf, jax etc.
									if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_lowercase     ,			torch.Tensor						):
															return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
									if hasattr(_lowercase     ,			"""__array__"""						) and not isinstance(_lowercase     ,			(np.ndarray, np.character, np.number)						):
												a_	:	Optional[int]          =   data_struct.__array__()
									# support for nested types like struct of list of struct
									if isinstance(_lowercase     ,			np.ndarray						):
												if data_struct.dtype == object:
															return self._consolidate([self.recursive_tensorize(_lowercase						) for substruct in data_struct]						)
									if isinstance(_lowercase     ,			(list, tuple)						):
												return self._consolidate([self.recursive_tensorize(_lowercase						) for substruct in data_struct]						)
									return self._tensorize(_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   int:
									return map_nested(self._recursive_tensorize     ,			_lowercase     ,			map_list=_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Mapping:
									a_	:	Optional[int]          =   self.numpy_arrow_extractor().extract_row(_lowercase						)
									a_	:	List[Any]          =   self.python_features_decoder.decode_row(_lowercase						)
									return self.recursive_tensorize(_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   np.ndarray:
									a_	:	List[Any]          =   self.numpy_arrow_extractor().extract_column(_lowercase						)
									a_	:	Dict          =   self.python_features_decoder.decode_column(_lowercase     ,			pa_table.column_names[0]						)
									a_	:	Union[str, Any]          =   self.recursive_tensorize(_lowercase						)
									a_	:	Dict          =   self._consolidate(_lowercase						)
									return column





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Mapping:
									a_	:	List[str]          =   self.numpy_arrow_extractor().extract_batch(_lowercase						)
									a_	:	str          =   self.python_features_decoder.decode_batch(_lowercase						)
									a_	:	Tuple          =   self.recursive_tensorize(_lowercase						)
									for column_name in batch:
												a_	:	Tuple          =   self._consolidate(batch[column_name]						)
									return batch





from collections import defaultdict


def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			a_	:	defaultdict[str, int]          =   defaultdict(a__)
			# Creating a dictionary containing count of each word
			for word in sentence.split():
						occurrence[word] += 1
			return occurrence


if __name__ == "__main__":
	for word, count in word_occurrence("""INPUT STRING""").items():
		print(F"""{word}: {count}""")


