
"""simple docstring"""



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


a					:	Union[str, Any]							=		logging.get_logger(__name__)

a					:	Dict[Optional[str], Type[Formatter]]							=		{}
a					:	Dict[Optional[str], str]							=		{}
a					:	Dict[Optional[str], Exception]							=		{}


def  _UpperCamelCase	(						_A					,      _A					,      _A = None					,      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   aliases if aliases is not None else []
    if format_type in _FORMAT_TYPES:
        logger.warning(
            F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""      )
    _UpperCAmelCase          =   formatter_cls
    for alias in set(aliases + [format_type]      ):
        if alias in _FORMAT_TYPES_ALIASES:
            logger.warning(
                F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""      )
        _UpperCAmelCase          =   format_type


def  _UpperCamelCase	(						_A					,      _A					,      _A = None      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   aliases if aliases is not None else []
    for alias in set(aliases + [format_type]      ):
        _UpperCAmelCase          =   unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
 from .torch_formatter import TorchFormatter

 _register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
 a					:	Optional[Any]							=		ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
 _register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
 from .tf_formatter import TFFormatter

 _register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
 a					:	Dict							=		ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
 _register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
 from .jax_formatter import JaxFormatter

 _register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
 a					:	List[Any]							=		ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
 _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])


def  _UpperCamelCase	(						_A      )    ->  Optional[str]:





    """simple docstring"""

    if format_type in _FORMAT_TYPES_ALIASES:
        return _FORMAT_TYPES_ALIASES[format_type]
    else:
        return format_type







def  _UpperCamelCase	(						_A					,      **_A      )    ->  Formatter:





    """simple docstring"""

    _UpperCAmelCase          =   get_format_type_from_alias(_A      )
    if format_type in _FORMAT_TYPES:
        return _FORMAT_TYPES[format_type](**_A      )
    if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
        raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
    else:
        raise ValueError(
            F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None      )}, but got '{format_type}'"""      )

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a					:	Tuple							=		{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a					:	List[str]							=		[
     '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PegasusXForConditionalGeneration''',
     '''PegasusXModel''',
     '''PegasusXPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 a					:	Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)