

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  ,    A_=None  ,    **A_		):







										'''simple docstring'''


										super().__init__(features=A_		)
										_UpperCAmelCase				:						Tuple       						=			np_array_kwargs


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										if isinstance(A_  ,    A_		):
														if column and all(
														    isinstance(A_  ,    np.ndarray		) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column		):
																		return np.stack(A_		)
														else:
																		# don't use np.array(column, dtype=object)
																		# since it fails in certain cases
																		# see https://stackoverflow.com/q/51005699
																		_UpperCAmelCase				:						List[str]       						=			np.empty(len(A_		)  ,    dtype=A_		)
																		_UpperCAmelCase				:						List[Any]       						=			column
																		return out
										return column


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										if isinstance(A_  ,    (str, bytes, type(A_		))		):
														return value
										elif isinstance(A_  ,    (np.character, np.ndarray)		) and np.issubdtype(value.dtype  ,    np.character		):
														return value
										elif isinstance(A_  ,    np.number		):
														return value

										_UpperCAmelCase				:						str       						=			{}

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

														if isinstance(A_  ,    PIL.Image.Image		):
																		return np.asarray(A_  ,    **self.np_array_kwargs		)

										return np.asarray(A_  ,    **{**default_dtype, **self.np_array_kwargs}		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(A_  ,    torch.Tensor		):
																		return self._tensorize(data_struct.detach().cpu().numpy()[()]		)
										if hasattr(A_  ,    "__array__"		) and not isinstance(A_  ,    (np.ndarray, np.character, np.number)		):
														_UpperCAmelCase				:						List[Any]       						=			data_struct.__array__()
										# support for nested types like struct of list of struct
										if isinstance(A_  ,    np.ndarray		):
														if data_struct.dtype == object:
																		return self._consolidate([self.recursive_tensorize(A_		) for substruct in data_struct]		)
										if isinstance(A_  ,    (list, tuple)		):
														return self._consolidate([self.recursive_tensorize(A_		) for substruct in data_struct]		)
										return self._tensorize(A_		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			self.numpy_arrow_extractor().extract_row(A_		)
										_UpperCAmelCase				:						Any       						=			self.python_features_decoder.decode_row(A_		)
										return self.recursive_tensorize(A_		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			self.numpy_arrow_extractor().extract_column(A_		)
										_UpperCAmelCase				:						Dict       						=			self.python_features_decoder.decode_column(A_  ,    pa_table.column_names[0]		)
										_UpperCAmelCase				:						List[Any]       						=			self.recursive_tensorize(A_		)
										_UpperCAmelCase				:						Any       						=			self._consolidate(A_		)
										return column





						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			self.numpy_arrow_extractor().extract_batch(A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			self.python_features_decoder.decode_batch(A_		)
										_UpperCAmelCase				:						List[str]       						=			self.recursive_tensorize(A_		)
										for column_name in batch:
														_UpperCAmelCase				:						int       						=			self._consolidate(batch[column_name]		)
										return batch









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = ['MPNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase				       = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

