

"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase__  		=		{
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		["""MobileBertTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		[
			    """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """MobileBertForMaskedLM""",
			    """MobileBertForMultipleChoice""",
			    """MobileBertForNextSentencePrediction""",
			    """MobileBertForPreTraining""",
			    """MobileBertForQuestionAnswering""",
			    """MobileBertForSequenceClassification""",
			    """MobileBertForTokenClassification""",
			    """MobileBertLayer""",
			    """MobileBertModel""",
			    """MobileBertPreTrainedModel""",
			    """load_tf_weights_in_mobilebert""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		[
			    """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFMobileBertForMaskedLM""",
			    """TFMobileBertForMultipleChoice""",
			    """TFMobileBertForNextSentencePrediction""",
			    """TFMobileBertForPreTraining""",
			    """TFMobileBertForQuestionAnswering""",
			    """TFMobileBertForSequenceClassification""",
			    """TFMobileBertForTokenClassification""",
			    """TFMobileBertMainLayer""",
			    """TFMobileBertModel""",
			    """TFMobileBertPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_mobilebert import (
			    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileBertConfig,
			    MobileBertOnnxConfig,
			)
			from .tokenization_mobilebert import MobileBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mobilebert_fast import MobileBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilebert import (
						    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileBertForMaskedLM,
						    MobileBertForMultipleChoice,
						    MobileBertForNextSentencePrediction,
						    MobileBertForPreTraining,
						    MobileBertForQuestionAnswering,
						    MobileBertForSequenceClassification,
						    MobileBertForTokenClassification,
						    MobileBertLayer,
						    MobileBertModel,
						    MobileBertPreTrainedModel,
						    load_tf_weights_in_mobilebert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_mobilebert import (
						    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertMainLayer,
						    TFMobileBertModel,
						    TFMobileBertPreTrainedModel,
						)

else:
			import sys

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





"""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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{}
lowercase__  		=		{}
lowercase__  		=		{}







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None							,					):
				_lowerCamelCase  :    str					=					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__})'''	)
				_lowerCamelCase  :    str					=					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})'''	)
								_lowerCamelCase  :    Dict					=					format_type







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None	):
				_lowerCamelCase  :    Dict					=					aliases if aliases is not None else []
				for alias in set(aliases + [format_type]	):
								_lowerCamelCase  :    Optional[Any]					=					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:
			lowercase__  		=		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:
			lowercase__  		=		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:
			lowercase__  		=		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
			_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def   _snake_case    (       lowercase__	):
				if format_type in _FORMAT_TYPES_ALIASES:
								return _FORMAT_TYPES_ALIASES[format_type]
				else:
								return format_type







def   _snake_case    (       lowercase__							,					**lowercase__	):
				_lowerCamelCase  :    List[str]					=					get_format_type_from_alias(lowercase__	)
				if format_type in _FORMAT_TYPES:
								return _FORMAT_TYPES[format_type](**lowercase__	)
				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}\''''	)