code: import sys
  from collections.abc import Mapping
  
  import numpy as np
  import pyarrow as pa
  
  from .. import config
  from ..utils.py_utils import map_nested
  from .formatting import TensorFormatter
  
  
  class _lowercase					(       TensorFormatter[Mapping, np.ndarray, Mapping]						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase=None     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														super().__init__(features=UpperCAmelCase  )
  														_lowercase				=	np_array_kwargs
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					if column and all(
  																					    isinstance(UpperCAmelCase     ,   np.ndarray  ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column  ):
  																												return np.stack(UpperCAmelCase  )
  																					else:
  																												# don't use np.array(column, dtype=object)
  																												# since it fails in certain cases
  																												# see https://stackoverflow.com/q/51005699
  																												_lowercase				=	np.empty(len(UpperCAmelCase  )     ,   dtype=UpperCAmelCase  )
  																												_lowercase				=	column
  																												return out
  														return column
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if isinstance(UpperCAmelCase     ,   (str, bytes, type(UpperCAmelCase  ))  ):
  																					return value
  														elif isinstance(UpperCAmelCase     ,   (np.character, np.ndarray)  ) and np.issubdtype(value.dtype     ,   np.character  ):
  																					return value
  														elif isinstance(UpperCAmelCase     ,   np.number  ):
  																					return value
  
  														_lowercase				=	{}
  
  														if isinstance(UpperCAmelCase     ,   np.ndarray  ) and np.issubdtype(value.dtype     ,   np.integer  ):
  																					_lowercase				=	{"""dtype""": np.intaa}
  														elif isinstance(UpperCAmelCase     ,   np.ndarray  ) and np.issubdtype(value.dtype     ,   np.floating  ):
  																					_lowercase				=	{"""dtype""": np.floataa}
  														elif config.PIL_AVAILABLE and "PIL" in sys.modules:
  																					import PIL.Image
  
  																					if isinstance(UpperCAmelCase     ,   PIL.Image.Image  ):
  																												return np.asarray(UpperCAmelCase     ,   **self.np_array_kwargs  )
  
  														return np.asarray(UpperCAmelCase     ,   **{**default_dtype, **self.np_array_kwargs}  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														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     ,   (np.ndarray, np.character, np.number)  ):
  																					_lowercase				=	data_struct.__array__()
  														# support for nested types like struct of list of struct
  														if isinstance(UpperCAmelCase     ,   np.ndarray  ):
  																					if data_struct.dtype == object:
  																												return self._consolidate([self.recursive_tensorize(UpperCAmelCase  ) for substruct in data_struct]  )
  														if isinstance(UpperCAmelCase     ,   (list, tuple)  ):
  																					return self._consolidate([self.recursive_tensorize(UpperCAmelCase  ) for substruct in data_struct]  )
  														return self._tensorize(UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return map_nested(self._recursive_tensorize     ,   UpperCAmelCase     ,   map_list=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.numpy_arrow_extractor().extract_row(UpperCAmelCase  )
  														_lowercase				=	self.python_features_decoder.decode_row(UpperCAmelCase  )
  														return self.recursive_tensorize(UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.numpy_arrow_extractor().extract_column(UpperCAmelCase  )
  														_lowercase				=	self.python_features_decoder.decode_column(UpperCAmelCase     ,   pa_table.column_names[0]  )
  														_lowercase				=	self.recursive_tensorize(UpperCAmelCase  )
  														_lowercase				=	self._consolidate(UpperCAmelCase  )
  														return column
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.numpy_arrow_extractor().extract_batch(UpperCAmelCase  )
  														_lowercase				=	self.python_features_decoder.decode_batch(UpperCAmelCase  )
  														_lowercase				=	self.recursive_tensorize(UpperCAmelCase  )
  														for column_name in batch:
  																					_lowercase				=	self._consolidate(batch[column_name]  )
  														return batch
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  