




from typing import TYPE_CHECKING

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


_lowerCamelCase   :	List[Any]       =    {
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   :	str       =    ["XLMRobertaTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   :	Any       =    ["XLMRobertaTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   :	Optional[Any]       =    [
	    "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "XLMRobertaForCausalLM",
	    "XLMRobertaForMaskedLM",
	    "XLMRobertaForMultipleChoice",
	    "XLMRobertaForQuestionAnswering",
	    "XLMRobertaForSequenceClassification",
	    "XLMRobertaForTokenClassification",
	    "XLMRobertaModel",
	    "XLMRobertaPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   :	Optional[Any]       =    [
	    "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFXLMRobertaForCausalLM",
	    "TFXLMRobertaForMaskedLM",
	    "TFXLMRobertaForMultipleChoice",
	    "TFXLMRobertaForQuestionAnswering",
	    "TFXLMRobertaForSequenceClassification",
	    "TFXLMRobertaForTokenClassification",
	    "TFXLMRobertaModel",
	    "TFXLMRobertaPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   :	int       =    [
	    "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "FlaxXLMRobertaForMaskedLM",
	    "FlaxXLMRobertaForCausalLM",
	    "FlaxXLMRobertaForMultipleChoice",
	    "FlaxXLMRobertaForQuestionAnswering",
	    "FlaxXLMRobertaForSequenceClassification",
	    "FlaxXLMRobertaForTokenClassification",
	    "FlaxXLMRobertaModel",
	    "FlaxXLMRobertaPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_xlm_roberta import (
	    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XLMRobertaConfig,
	    XLMRobertaOnnxConfig,
	)

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta import XLMRobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMRobertaForCausalLM,
		    XLMRobertaForMaskedLM,
		    XLMRobertaForMultipleChoice,
		    XLMRobertaForQuestionAnswering,
		    XLMRobertaForSequenceClassification,
		    XLMRobertaForTokenClassification,
		    XLMRobertaModel,
		    XLMRobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm_roberta import (
		    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMRobertaForCausalLM,
		    TFXLMRobertaForMaskedLM,
		    TFXLMRobertaForMultipleChoice,
		    TFXLMRobertaForQuestionAnswering,
		    TFXLMRobertaForSequenceClassification,
		    TFXLMRobertaForTokenClassification,
		    TFXLMRobertaModel,
		    TFXLMRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xlm_roberta import (
		    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxXLMRobertaForCausalLM,
		    FlaxXLMRobertaForMaskedLM,
		    FlaxXLMRobertaForMultipleChoice,
		    FlaxXLMRobertaForQuestionAnswering,
		    FlaxXLMRobertaForSequenceClassification,
		    FlaxXLMRobertaForTokenClassification,
		    FlaxXLMRobertaModel,
		    FlaxXLMRobertaPreTrainedModel,
		)

else:
	import sys

	_lowerCamelCase   :	Optional[int]       =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__UpperCamelCase   :			Optional[int]          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Union[str, Any]						, *__magic_name__		:int						, **__magic_name__		:str							):







								'''simple docstring'''







								warnings.warn(
								    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
								    """ Please use LayoutLMv2ImageProcessor instead."""						, __magic_name__						, )
								super().__init__(*__magic_name__						, **__magic_name__							)

