






# 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    a   (			TensorFormatter[Mapping, 'tf.Tensor', Mapping]       ):



      """simple docstring"""


      def __init__(				self :      Any		,				__lowercase :      Optional[Any]=None		,				**__lowercase :      str )							->				str:
        super().__init__(features=__lowercase )
        __UpperCAmelCase							:     Dict									=  tf_tensor_kwargs
        import tensorflow as tf  # noqa: import tf at initialization
      def      UpperCAmelCase				(				self :      str		,				__lowercase :      Dict )							->				Any:
        import tensorflow as tf

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

        return column
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      str )							->				str:
        import tensorflow as tf

        if value is None:
          return value

        __UpperCAmelCase							:     int									=  {}

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

          if isinstance(__lowercase		,				PIL.Image.Image ):
            __UpperCAmelCase							:     List[Any]									=  np.asarray(__lowercase )

        return tf.convert_to_tensor(__lowercase		,				**{**default_dtype, **self.tf_tensor_kwargs} )
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      int )							->				Dict:
        import tensorflow as tf

        # support for torch, tf, jax etc.
        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		,				tf.Tensor ):
          __UpperCAmelCase							:     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:  # tf tensors cannot be instantied from an array of objects
            return self._consolidate([self.recursive_tensorize(__lowercase ) for substruct in data_struct] )
        elif isinstance(__lowercase		,				(list, tuple) ):
          return self._consolidate([self.recursive_tensorize(__lowercase ) for substruct in data_struct] )
        return self._tensorize(__lowercase )
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      dict )							->				List[Any]:
        return map_nested(self._recursive_tensorize		,				__lowercase		,				map_list=__lowercase )
      def      UpperCAmelCase				(				self :      int		,				__lowercase :      pa.Table )							->				Mapping:
        __UpperCAmelCase							:     int									=  self.numpy_arrow_extractor().extract_row(__lowercase )
        __UpperCAmelCase							:     str									=  self.python_features_decoder.decode_row(__lowercase )
        return self.recursive_tensorize(__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int]		,				__lowercase :      pa.Table )							->				"tf.Tensor":
        __UpperCAmelCase							:     int									=  self.numpy_arrow_extractor().extract_column(__lowercase )
        __UpperCAmelCase							:     Union[str, Any]									=  self.python_features_decoder.decode_column(__lowercase		,				pa_table.column_names[0] )
        __UpperCAmelCase							:     Dict									=  self.recursive_tensorize(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  self._consolidate(__lowercase )
        return column




      def      UpperCAmelCase				(				self :      List[Any]		,				__lowercase :      pa.Table )							->				Mapping:
        __UpperCAmelCase							:     Union[str, Any]									=  self.numpy_arrow_extractor().extract_batch(__lowercase )
        __UpperCAmelCase							:     int									=  self.python_features_decoder.decode_batch(__lowercase )
        __UpperCAmelCase							:     Dict									=  self.recursive_tensorize(__lowercase )
        for column_name in batch:
          __UpperCAmelCase							:     Any									=  self._consolidate(batch[column_name] )
        return batch










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		:	Any      					=			{
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Optional[int]      					=			["DetaImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Tuple      					=			[
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_deta import DetaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )

else:
       import sys

       a		:	Any      					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


