


from typing import TYPE_CHECKING

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


snake_case							:       List[str]     			=      {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      ['''DebertaTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Dict     			=      [
						    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaForMaskedLM''',
						    '''DebertaForQuestionAnswering''',
						    '''DebertaForSequenceClassification''',
						    '''DebertaForTokenClassification''',
						    '''DebertaModel''',
						    '''DebertaPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaForMaskedLM''',
						    '''TFDebertaForQuestionAnswering''',
						    '''TFDebertaForSequenceClassification''',
						    '''TFDebertaForTokenClassification''',
						    '''TFDebertaModel''',
						    '''TFDebertaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
						from .tokenization_deberta import DebertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_fast import DebertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta import (
												    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaForMaskedLM,
												    DebertaForQuestionAnswering,
												    DebertaForSequenceClassification,
												    DebertaForTokenClassification,
												    DebertaModel,
												    DebertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta import (
												    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaForMaskedLM,
												    TFDebertaForQuestionAnswering,
												    TFDebertaForSequenceClassification,
												    TFDebertaForTokenClassification,
												    TFDebertaModel,
												    TFDebertaPreTrainedModel,
												)


else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from typing import TYPE_CHECKING

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


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




