
'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase :  str    							=       {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       ["ConvBertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  int    							=       [
							    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ConvBertForMaskedLM",
							    "ConvBertForMultipleChoice",
							    "ConvBertForQuestionAnswering",
							    "ConvBertForSequenceClassification",
							    "ConvBertForTokenClassification",
							    "ConvBertLayer",
							    "ConvBertModel",
							    "ConvBertPreTrainedModel",
							    "load_tf_weights_in_convbert",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       [
							    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFConvBertForMaskedLM",
							    "TFConvBertForMultipleChoice",
							    "TFConvBertForQuestionAnswering",
							    "TFConvBertForSequenceClassification",
							    "TFConvBertForTokenClassification",
							    "TFConvBertLayer",
							    "TFConvBertModel",
							    "TFConvBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
							from .tokenization_convbert import ConvBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_convbert_fast import ConvBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_convbert import (
														    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConvBertForMaskedLM,
														    ConvBertForMultipleChoice,
														    ConvBertForQuestionAnswering,
														    ConvBertForSequenceClassification,
														    ConvBertForTokenClassification,
														    ConvBertLayer,
														    ConvBertModel,
														    ConvBertPreTrainedModel,
														    load_tf_weights_in_convbert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_convbert import (
														    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFConvBertForMaskedLM,
														    TFConvBertForMultipleChoice,
														    TFConvBertForQuestionAnswering,
														    TFConvBertForSequenceClassification,
														    TFConvBertForTokenClassification,
														    TFConvBertLayer,
														    TFConvBertModel,
														    TFConvBertPreTrainedModel,
														)


else:
							import sys

							_lowercase :  List[Any]    							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase :  Union[str, Any]    							=       {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       ["Pix2StructImageProcessor"]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Pix2StructPreTrainedModel",
							    "Pix2StructForConditionalGeneration",
							    "Pix2StructVisionModel",
							    "Pix2StructTextModel",
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

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



