





# 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 tensorflow as tf



class       __lowerCAmelCase (    TensorFormatter[Mapping, '''tf.Tensor''', Mapping]  ):






			def __init__(self      ,							__magic_name__=None      ,							**__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							super().__init__(features=__magic_name__					)
							snake_case_		:					Union[str, Any]			    =						tf_tensor_kwargs
							import tensorflow as tf  # noqa: import tf at initialization






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Any:


							'''simple docstring'''



							import tensorflow as tf

							if isinstance(__magic_name__      ,							__magic_name__					) and column:
											if all(
											    isinstance(__magic_name__      ,							tf.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
															return tf.stack(__magic_name__					)
											elif all(
											    isinstance(__magic_name__      ,							(tf.Tensor, tf.RaggedTensor)					) and x.ndim == 1 and x.dtype == column[0].dtype
											    for x in column					):
															# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
															return tf.ragged.stack(__magic_name__					)

							return column






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Any:


							'''simple docstring'''



							import tensorflow as tf

							if value is None:
											return value

							snake_case_		:					int			    =						{}

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

											if isinstance(__magic_name__      ,							PIL.Image.Image					):
															snake_case_		:					Union[str, Any]			    =						np.asarray(__magic_name__					)

							return tf.convert_to_tensor(__magic_name__      ,							**{**default_dtype, **self.tf_tensor_kwargs}					)






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     int:


							'''simple docstring'''



							import tensorflow as tf

							# support for torch, tf, jax etc.
							if config.TORCH_AVAILABLE and "torch" in sys.modules:
											import torch

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






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



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






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Mapping:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						self.numpy_arrow_extractor().extract_row(__magic_name__					)
							snake_case_		:					int			    =						self.python_features_decoder.decode_row(__magic_name__					)
							return self.recursive_tensorize(__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     "tf.Tensor":


							'''simple docstring'''



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






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Mapping:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						self.numpy_arrow_extractor().extract_batch(__magic_name__					)
							snake_case_		:					Optional[int]			    =						self.python_features_decoder.decode_batch(__magic_name__					)
							snake_case_		:					Optional[Any]			    =						self.recursive_tensorize(__magic_name__					)
							for column_name in batch:
											snake_case_		:					Tuple			    =						self._consolidate(batch[column_name]					)
							return batch







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
							    ''' use LevitImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
