


"""simple docstring"""


# Lint as: python3
import itertools
import os
import re


A     :   Dict			    =			re.compile(R"([A-Z]+)([A-Z][a-z])")
A     :   Union[str, Any]			    =			re.compile(R"([a-z\d])([A-Z])")

A     :   Any			    =			re.compile(R"(?<!_)_(?!_)")
A     :   List[Any]			    =			re.compile(R"(_{2,})")

A     :   Union[str, Any]			    =			R"^\w+(\.\w+)*$"

A     :   Union[str, Any]			    =			R"<>:/\|?*"


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			_uppercase_uppercase_re.sub(R"\1_\2"    , _UpperCamelCase      )
			__lowerCAmelCase				    =			_lowercase_uppercase_re.sub(R"\1_\2"    , _UpperCamelCase      )
			return name.lower()


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			_single_underscore_re.split(_UpperCamelCase      )
			__lowerCAmelCase				    =			[_multiple_underscores_re.split(_UpperCamelCase      ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(_UpperCamelCase      ) if n != ""      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if os.path.basename(_UpperCamelCase      ) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"      )
			return camelcase_to_snakecase(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if os.path.basename(_UpperCamelCase      ) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"      )
			if not re.match(_split_re    , _UpperCamelCase      ):
						raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."      )
			return f"{filename_prefix_for_name(_UpperCamelCase      )}-{split}"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			filename_prefix_for_split(_UpperCamelCase    , _UpperCamelCase      )
			if filetype_suffix:
						prefix += f".{filetype_suffix}"
			__lowerCAmelCase				    =			os.path.join(_UpperCamelCase    , _UpperCamelCase      )
			return f"{filepath}*"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None    , _UpperCamelCase=None      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			filename_prefix_for_split(_UpperCamelCase    , _UpperCamelCase      )
			__lowerCAmelCase				    =			os.path.join(_UpperCamelCase    , _UpperCamelCase      )

			if shard_lengths:
						__lowerCAmelCase				    =			len(_UpperCamelCase      )
						__lowerCAmelCase				    =			[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(_UpperCamelCase      )]
						if filetype_suffix:
									__lowerCAmelCase				    =			[filename + f".{filetype_suffix}" for filename in filenames]
						return filenames
			else:
						__lowerCAmelCase				    =			prefix
						if filetype_suffix:
									filename += f".{filetype_suffix}"
						return [filename]












from typing import TYPE_CHECKING

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


lowercase  							=					{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TapasForMaskedLM",
	    "TapasForQuestionAnswering",
	    "TapasForSequenceClassification",
	    "TapasModel",
	    "TapasPreTrainedModel",
	    "load_tf_weights_in_tapas",
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFTapasForMaskedLM",
	    "TFTapasForQuestionAnswering",
	    "TFTapasForSequenceClassification",
	    "TFTapasModel",
	    "TFTapasPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

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





