
from typing import TYPE_CHECKING

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


snake_case__  							=							{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""DebertaV2TokenizerFast"""]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFDebertaV2ForMaskedLM""",
				    """TFDebertaV2ForQuestionAnswering""",
				    """TFDebertaV2ForSequenceClassification""",
				    """TFDebertaV2ForTokenClassification""",
				    """TFDebertaV2Model""",
				    """TFDebertaV2PreTrainedModel""",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DebertaV2ForMaskedLM""",
				    """DebertaV2ForMultipleChoice""",
				    """DebertaV2ForQuestionAnswering""",
				    """DebertaV2ForSequenceClassification""",
				    """DebertaV2ForTokenClassification""",
				    """DebertaV2Model""",
				    """DebertaV2PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deberta_va import (
				    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DebertaVaConfig,
				    DebertaVaOnnxConfig,
				)
				from .tokenization_deberta_va import DebertaVaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta_va import (
								    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaVaForMaskedLM,
								    TFDebertaVaForQuestionAnswering,
								    TFDebertaVaForSequenceClassification,
								    TFDebertaVaForTokenClassification,
								    TFDebertaVaModel,
								    TFDebertaVaPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta_va import (
								    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaVaForMaskedLM,
								    DebertaVaForMultipleChoice,
								    DebertaVaForQuestionAnswering,
								    DebertaVaForSequenceClassification,
								    DebertaVaForTokenClassification,
								    DebertaVaModel,
								    DebertaVaPreTrainedModel,
								)

else:
				import sys

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




snake_case__  							=							"""
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

snake_case__  							=							[{"""type""": """code""", """content""": INSTALL_CONTENT}]
snake_case__  							=							{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


