


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

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      List[str]=None ,  **lowerCAmelCase							:      int	):
													super().__init__(features=_A	)
													A_			    =		torch_tensor_kwargs
													import torch  # noqa import torch at initialization






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      Dict	):
													import torch

													if isinstance(_A ,  _A	) and column:
																				if all(
																				    isinstance(_A ,  torch.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype
																				    for x in column	):
																											return torch.stack(_A	)
													return column






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Optional[Any]	):
													import torch

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

													A_			    =		{}

													if isinstance(_A ,  (np.number, np.ndarray)	) and np.issubdtype(value.dtype ,  np.integer	):
																				A_			    =		{"""dtype""": torch.intaa}
													elif isinstance(_A ,  (np.number, np.ndarray)	) and np.issubdtype(value.dtype ,  np.floating	):
																				A_			    =		{"""dtype""": torch.floataa}
													elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																				import PIL.Image

																				if isinstance(_A ,  PIL.Image.Image	):
																											A_			    =		np.asarray(_A	)
													return torch.tensor(_A ,  **{**default_dtype, **self.torch_tensor_kwargs}	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      Dict	):
													import torch

													# support for torch, tf, jax etc.
													if hasattr(_A ,  "__array__"	) and not isinstance(_A ,  torch.Tensor	):
																				A_			    =		data_struct.__array__()
													# support for nested types like struct of list of struct
													if isinstance(_A ,  np.ndarray	):
																				if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																											return self._consolidate([self.recursive_tensorize(_A	) for substruct in data_struct]	)
													elif isinstance(_A ,  (list, tuple)	):
																				return self._consolidate([self.recursive_tensorize(_A	) for substruct in data_struct]	)
													return self._tensorize(_A	)






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      Dict	):
													return map_nested(self._recursive_tensorize ,  _A ,  map_list=_A	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      Dict	):
													A_			    =		self.numpy_arrow_extractor().extract_row(_A	)
													A_			    =		self.python_features_decoder.decode_row(_A	)
													return self.recursive_tensorize(_A	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      Dict	):
													A_			    =		self.numpy_arrow_extractor().extract_column(_A	)
													A_			    =		self.python_features_decoder.decode_column(_A ,  pa_table.column_names[0]	)
													A_			    =		self.recursive_tensorize(_A	)
													A_			    =		self._consolidate(_A	)
													return column






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      Any	):
													A_			    =		self.numpy_arrow_extractor().extract_batch(_A	)
													A_			    =		self.python_features_decoder.decode_batch(_A	)
													A_			    =		self.recursive_tensorize(_A	)
													for column_name in batch:
																				A_			    =		self._consolidate(batch[column_name]	)
													return batch












"""simple docstring"""


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



