"""simple docstring"""





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


      def __init__(       self		,      a=None		,      **a							)	-> Optional[Any]:
             super().__init__(features=a							)
             lowercase__      :		Optional[int]          =     np_array_kwargs


      def 		_UpperCAmelCase	(       self		,      a							)	-> int:
             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
                           lowercase__      :		Union[str, Any]          =     np.empty(len(a							)		,      dtype=a							)
                           lowercase__      :		Tuple          =     column
                           return out
             return column


      def 		_UpperCAmelCase	(       self		,      a							)	-> Dict:
             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

             lowercase__      :		Optional[Any]          =     {}

             if isinstance(a		,      np.ndarray							) and np.issubdtype(value.dtype		,      np.integer							):
                    lowercase__      :		List[str]          =     {'dtype': np.intaa}
             elif isinstance(a		,      np.ndarray							) and np.issubdtype(value.dtype		,      np.floating							):
                    lowercase__      :		str          =     {'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							)	-> Union[str, Any]:
             # support for torch, tf, jax etc.
             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)							):
                    lowercase__      :		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							)	-> Dict:
             return map_nested(self._recursive_tensorize		,      a		,      map_list=a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Mapping:
             lowercase__      :		Tuple          =     self.numpy_arrow_extractor().extract_row(a							)
             lowercase__      :		str          =     self.python_features_decoder.decode_row(a							)
             return self.recursive_tensorize(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> np.ndarray:
             lowercase__      :		List[Any]          =     self.numpy_arrow_extractor().extract_column(a							)
             lowercase__      :		Dict          =     self.python_features_decoder.decode_column(a		,      pa_table.column_names[0]							)
             lowercase__      :		Optional[Any]          =     self.recursive_tensorize(a							)
             lowercase__      :		Any          =     self._consolidate(a							)
             return column


      def 		_UpperCAmelCase	(       self		,      a							)	-> Mapping:
             lowercase__      :		Union[str, Any]          =     self.numpy_arrow_extractor().extract_batch(a							)
             lowercase__      :		Dict          =     self.python_features_decoder.decode_batch(a							)
             lowercase__      :		Optional[int]          =     self.recursive_tensorize(a							)
             for column_name in batch:
                    lowercase__      :		str          =     self._consolidate(batch[column_name]							)
             return batch


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						Tuple     =   ["RetriBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase       :						Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

