



"""simple docstring"""

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    __lowercase     (					TensorFormatter[Mapping, """torch.Tensor""", Mapping]):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=None   ,		**lowercase__       ):
											super().__init__(features=UpperCamelCase__       )
											snake_case_					:		int 					=					torch_tensor_kwargs
											import torch  # noqa import torch at initialization







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											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 						__UpperCamelCase							(self   ,		lowercase__       ):
											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()

											snake_case_					:		str 					=					{}

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

																if isinstance(UpperCamelCase__   ,		PIL.Image.Image       ):
																					snake_case_					:		List[Any] 					=					np.asarray(UpperCamelCase__       )
											return torch.tensor(UpperCamelCase__   ,		**{**default_dtype, **self.torch_tensor_kwargs}       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(UpperCamelCase__   ,		"""__array__"""       ) and not isinstance(UpperCamelCase__   ,		torch.Tensor       ):
																snake_case_					:		Any 					=					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 						__UpperCamelCase							(self   ,		lowercase__       ):
											return map_nested(self._recursive_tensorize   ,		UpperCamelCase__   ,		map_list=UpperCamelCase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		str 					=					self.numpy_arrow_extractor().extract_row(UpperCamelCase__       )
											snake_case_					:		Dict 					=					self.python_features_decoder.decode_row(UpperCamelCase__       )
											return self.recursive_tensorize(UpperCamelCase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					self.numpy_arrow_extractor().extract_column(UpperCamelCase__       )
											snake_case_					:		str 					=					self.python_features_decoder.decode_column(UpperCamelCase__   ,		pa_table.column_names[0]       )
											snake_case_					:		Union[str, Any] 					=					self.recursive_tensorize(UpperCamelCase__       )
											snake_case_					:		Dict 					=					self._consolidate(UpperCamelCase__       )
											return column







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		List[Any] 					=					self.numpy_arrow_extractor().extract_batch(UpperCamelCase__       )
											snake_case_					:		Union[str, Any] 					=					self.python_features_decoder.decode_batch(UpperCamelCase__       )
											snake_case_					:		Dict 					=					self.recursive_tensorize(UpperCamelCase__       )
											for column_name in batch:
																snake_case_					:		Optional[Any] 					=					self._consolidate(batch[column_name]       )
											return batch









"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  float				,    SCREAMING_SNAKE_CASE__ :  bool = False			):



					"""simple docstring"""





					snake_case_					:		dict 					=					{i: [] for i in range(SCREAMING_SNAKE_CASE__			)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE__			)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(i + 1				,    SCREAMING_SNAKE_CASE__			):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE__			)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE__			)
					return graph







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE__			) if i != j] for i in range(SCREAMING_SNAKE_CASE__			)
					}


if __name__ == "__main__":
			import doctest

			doctest.testmod()




