# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_camembert"] = ["CamembertTokenizer"] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_camembert_fast"] = ["CamembertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_camembert"] = [ "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "CamembertForCausalLM", "CamembertForMaskedLM", "CamembertForMultipleChoice", "CamembertForQuestionAnswering", "CamembertForSequenceClassification", "CamembertForTokenClassification", "CamembertModel", "CamembertPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_camembert"] = [ "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCamembertForCausalLM", "TFCamembertForMaskedLM", "TFCamembertForMultipleChoice", "TFCamembertForQuestionAnswering", "TFCamembertForSequenceClassification", "TFCamembertForTokenClassification", "TFCamembertModel", "TFCamembertPreTrainedModel", ] if TYPE_CHECKING: from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_camembert import CamembertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_camembert_fast import CamembertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_camembert import ( CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, CamembertForCausalLM, CamembertForMaskedLM, CamembertForMultipleChoice, CamembertForQuestionAnswering, CamembertForSequenceClassification, CamembertForTokenClassification, CamembertModel, CamembertPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_camembert import ( TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCamembertForCausalLM, TFCamembertForMaskedLM, TFCamembertForMultipleChoice, TFCamembertForQuestionAnswering, TFCamembertForSequenceClassification, TFCamembertForTokenClassification, TFCamembertModel, TFCamembertPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)