





'''simple docstring'''






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       lowerCamelCase       (    TensorFormatter[Mapping, np.ndarray, Mapping]	):


					def __init__(       self						:	Dict							,  __snake_case						:	Tuple=None							,  **__snake_case						:	Tuple							):





											'''simple docstring'''





											super().__init__(features=__lowercase							)
											_snake_case: List[Any]	       =		np_array_kwargs


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											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
																							_snake_case: Any	       =		np.empty(len(__lowercase							)							,  dtype=__lowercase							)
																							_snake_case: Any	       =		column
																							return out
											return column


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	int							):





											'''simple docstring'''





											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

											_snake_case: Any	       =		{}

											if isinstance(__lowercase							,  np.ndarray							) and np.issubdtype(value.dtype							,  np.integer							):
																	_snake_case: List[str]	       =		{'''dtype''': np.intaa}
											elif isinstance(__lowercase							,  np.ndarray							) and np.issubdtype(value.dtype							,  np.floating							):
																	_snake_case: Tuple	       =		{'''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  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	str							):





											'''simple docstring'''





											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)							):
																	_snake_case: Union[str, Any]	       =		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  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	int							):





											'''simple docstring'''





											return map_nested(self._recursive_tensorize							,  __lowercase							,  map_list=__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.numpy_arrow_extractor().extract_row(__lowercase							)
											_snake_case: List[str]	       =		self.python_features_decoder.decode_row(__lowercase							)
											return self.recursive_tensorize(__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	Any							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		self.numpy_arrow_extractor().extract_column(__lowercase							)
											_snake_case: Dict	       =		self.python_features_decoder.decode_column(__lowercase							,  pa_table.column_names[0]							)
											_snake_case: List[str]	       =		self.recursive_tensorize(__lowercase							)
											_snake_case: int	       =		self._consolidate(__lowercase							)
											return column




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Any	       =		self.numpy_arrow_extractor().extract_batch(__lowercase							)
											_snake_case: List[Any]	       =		self.python_features_decoder.decode_batch(__lowercase							)
											_snake_case: List[Any]	       =		self.recursive_tensorize(__lowercase							)
											for column_name in batch:
																	_snake_case: int	       =		self._consolidate(batch[column_name]							)
											return batch












import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''				,							[
        (Version('''1.0.0'''		), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''		), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]				,							)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			str    		=Version('''1.0.0'''		)
		assert (version == other) is expected_equality
		assert (version != other) is not expected_equality
		assert (hash(SCREAMING_SNAKE_CASE_		) == hash(SCREAMING_SNAKE_CASE_		)) is expected_equality

