
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      A__       (						TensorFormatter[Mapping, np.ndarray, Mapping]    ):





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



											"""simple docstring"""


											super().__init__(features=_UpperCAmelCase    )
											__lowercase								= np_array_kwargs





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       List[Any]    )						->		Optional[int]:



											"""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       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       List[Any]    )						->		Union[str, Any]:



											"""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       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[Any]    )						->		str:



											"""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       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       dict    )						->		Optional[Any]:



											"""simple docstring"""


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





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       pa.Table    )						->		Mapping:



											"""simple docstring"""


											__lowercase								= self.numpy_arrow_extractor().extract_row(_UpperCAmelCase    )
											__lowercase								= self.python_features_decoder.decode_row(_UpperCAmelCase    )
											return self.recursive_tensorize(_UpperCAmelCase    )





							def       a__			(			self  :       Optional[Any]					,			_UpperCAmelCase  :       pa.Table    )						->		np.ndarray:



											"""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       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       pa.Table    )						->		Mapping:



											"""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







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





