from typing import TYPE_CHECKING

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


_UpperCAmelCase            =		{
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RoFormerForCausalLM""",
	    """RoFormerForMaskedLM""",
	    """RoFormerForMultipleChoice""",
	    """RoFormerForQuestionAnswering""",
	    """RoFormerForSequenceClassification""",
	    """RoFormerForTokenClassification""",
	    """RoFormerLayer""",
	    """RoFormerModel""",
	    """RoFormerPreTrainedModel""",
	    """load_tf_weights_in_roformer""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRoFormerForCausalLM""",
	    """TFRoFormerForMaskedLM""",
	    """TFRoFormerForMultipleChoice""",
	    """TFRoFormerForQuestionAnswering""",
	    """TFRoFormerForSequenceClassification""",
	    """TFRoFormerForTokenClassification""",
	    """TFRoFormerLayer""",
	    """TFRoFormerModel""",
	    """TFRoFormerPreTrainedModel""",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FlaxRoFormerForMaskedLM""",
	    """FlaxRoFormerForMultipleChoice""",
	    """FlaxRoFormerForQuestionAnswering""",
	    """FlaxRoFormerForSequenceClassification""",
	    """FlaxRoFormerForTokenClassification""",
	    """FlaxRoFormerModel""",
	    """FlaxRoFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

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







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_UpperCAmelCase            =		"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
	if pkg in deps:
		if pkg == "tokenizers":
			# must be loaded here, or else tqdm check may fail
			from .utils import is_tokenizers_available

			if not is_tokenizers_available():
				continue  # not required, check version only if installed

		require_version_core(deps[pkg])
	else:
		raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Dict=None     ):
		'''simple docstring'''




		require_version(deps[pkg]						,__lowercase     )






