



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					"""encoder-decoder"""
							UpperCamelCase__				:      int         =					True


							def __init__(							self  ,  **a_		):
											super().__init__(**a_		)
											assert (
											    "encoder" in kwargs and "decoder" in kwargs
											), "Config has to be initialized with encoder and decoder config"
											a__				      =		kwargs.pop("""encoder"""		)
											a__				      =		encoder_config.pop("""model_type"""		)
											a__				      =		kwargs.pop("""decoder"""		)
											a__				      =		decoder_config.pop("""model_type"""		)

											from ..auto.configuration_auto import AutoConfig

											a__				      =		AutoConfig.for_model(a_  ,  **a_		)
											a__				      =		AutoConfig.for_model(a_  ,  **a_		)
											a__				      =		True


							@classmethod
							def _a      (							cls  ,  a_  ,  a_  ,  **a_		):
											logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
											a__				      =		True
											a__				      =		True

											return cls(encoder=encoder_config.to_dict()  ,  decoder=decoder_config.to_dict()  ,  **a_		)







							def _a      (							self		):
											a__				      =		copy.deepcopy(self.__dict__		)
											a__				      =		self.encoder.to_dict()
											a__				      =		self.decoder.to_dict()
											a__				      =		self.__class__.model_type
											return output






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,
)


UpperCAmelCase							 =					{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """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:
	UpperCAmelCase							 =					[
	    """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:
	UpperCAmelCase							 =					[
	    """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

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

