





'''simple docstring'''







# 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 							__SCREAMING_SNAKE_CASE			(       TensorFormatter[Mapping, "tf.Tensor", Mapping]      ):
      '''simple docstring'''



      def __init__(							self    : Union[str, Any]   ,UpperCamelCase    : Any=None   ,**UpperCamelCase    : List[Any]  )		->  Dict:
           super().__init__(features=UpperCamelCase  )
           _lowercase							:      Any  						=							tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization

      def   _lowerCamelCase		(							self    : int   ,UpperCamelCase    : Optional[Any]  )		->  List[Any]:
           import tensorflow as tf

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

           return column

      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : str  )		->  Optional[Any]:
           import tensorflow as tf

           if value is None:
                return value

           _lowercase							:      Any  						=							{}

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

                if isinstance(UpperCamelCase   ,PIL.Image.Image  ):
                     _lowercase							:      Tuple  						=							np.asarray(UpperCamelCase  )

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

      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : Any  )		->  Optional[Any]:
           import tensorflow as tf

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

                if isinstance(UpperCamelCase   ,torch.Tensor  ):
                     return self._tensorize(data_struct.detach().cpu().numpy()[()]  )
           if hasattr(UpperCamelCase   ,'__array__'  ) and not isinstance(UpperCamelCase   ,tf.Tensor  ):
                _lowercase							:      List[str]  						=							data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(UpperCamelCase   ,np.ndarray  ):
                if data_struct.dtype == object:  # tf 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   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : dict  )		->  List[str]:
           return map_nested(self._recursive_tensorize   ,UpperCamelCase   ,map_list=UpperCamelCase  )

      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : pa.Table  )		->  Mapping:
           _lowercase							:      List[Any]  						=							self.numpy_arrow_extractor().extract_row(UpperCamelCase  )
           _lowercase							:      Any  						=							self.python_features_decoder.decode_row(UpperCamelCase  )
           return self.recursive_tensorize(UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Tuple   ,UpperCamelCase    : pa.Table  )		->  "tf.Tensor":
           _lowercase							:      Any  						=							self.numpy_arrow_extractor().extract_column(UpperCamelCase  )
           _lowercase							:      Any  						=							self.python_features_decoder.decode_column(UpperCamelCase   ,pa_table.column_names[0]  )
           _lowercase							:      str  						=							self.recursive_tensorize(UpperCamelCase  )
           _lowercase							:      Tuple  						=							self._consolidate(UpperCamelCase  )
           return column



      def   _lowerCamelCase		(							self    : str   ,UpperCamelCase    : pa.Table  )		->  Mapping:
           _lowercase							:      Optional[int]  						=							self.numpy_arrow_extractor().extract_batch(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							self.python_features_decoder.decode_batch(UpperCamelCase  )
           _lowercase							:      Optional[int]  						=							self.recursive_tensorize(UpperCamelCase  )
           for column_name in batch:
                _lowercase							:      Dict  						=							self._consolidate(batch[column_name]  )
           return batch






'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A						    =							logging.get_logger(__name__)



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Tuple   ,*UpperCamelCase    : str   ,**UpperCamelCase    : List[Any]  )		->  None:
           warnings.warn(
               'The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
               ' Please use MobileNetV2ImageProcessor instead.'   ,UpperCamelCase   ,)
           super().__init__(*UpperCamelCase   ,**UpperCamelCase  )