

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





  def __init__(	self   :			str							,      _a   :			int=None							,      **_a   :			List[str]				)			->					List[str]:





       '''simple docstring'''






       super().__init__(features=lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=tf_tensor_kwargs
       import tensorflow as tf  # noqa: import tf at initialization





  def 		A						(	self   :			int							,      _a   :			Optional[int]				)			->					Dict:





       '''simple docstring'''






       import tensorflow as tf

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

       return column





  def 		A						(	self   :			Any							,      _a   :			List[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       import tensorflow as tf

       if value is None:
            return value

       _SCREAMING_SNAKE_CASE												={}

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

            if isinstance(lowerCamelCase__							,      PIL.Image.Image				):
                 _SCREAMING_SNAKE_CASE												=np.asarray(lowerCamelCase__				)

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





  def 		A						(	self   :			List[str]							,      _a   :			Optional[int]				)			->					Optional[Any]:





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





  def 		A						(	self   :			List[Any]							,      _a   :			str				)			->					Union[str, Any]:





       '''simple docstring'''






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





  def 		A						(	self   :			Tuple							,      _a   :			int				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.numpy_arrow_extractor().extract_row(lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=self.python_features_decoder.decode_row(lowerCamelCase__				)
       return self.recursive_tensorize(lowerCamelCase__				)





  def 		A						(	self   :			Tuple							,      _a   :			Dict				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.numpy_arrow_extractor().extract_column(lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=self.python_features_decoder.decode_column(lowerCamelCase__							,      pa_table.column_names[0]				)
       _SCREAMING_SNAKE_CASE												=self.recursive_tensorize(lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=self._consolidate(lowerCamelCase__				)
       return column





  def 		A						(	self   :			Union[str, Any]							,      _a   :			Optional[Any]				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.numpy_arrow_extractor().extract_batch(lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=self.python_features_decoder.decode_batch(lowerCamelCase__				)
       _SCREAMING_SNAKE_CASE												=self.recursive_tensorize(lowerCamelCase__				)
       for column_name in batch:
            _SCREAMING_SNAKE_CASE												=self._consolidate(batch[column_name]				)
       return batch



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase				:   str      				=  {"configuration_dinat": ["DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DinatConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase				:   Optional[int]      				=  [
         "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DinatForImageClassification",
         "DinatModel",
         "DinatPreTrainedModel",
         "DinatBackbone",
     ]

if TYPE_CHECKING:
     from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dinat import (
              DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DinatBackbone,
              DinatForImageClassification,
              DinatModel,
              DinatPreTrainedModel,
          )

else:
     import sys

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