
from typing import TYPE_CHECKING

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


lowerCamelCase_						: int         =  {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  ["""LayoutLMv2TokenizerFast"""]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""LayoutLMv2FeatureExtractor"""]
							lowerCamelCase_						: Any         =  ["""LayoutLMv2ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Tuple         =  [
							    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """LayoutLMv2ForQuestionAnswering""",
							    """LayoutLMv2ForSequenceClassification""",
							    """LayoutLMv2ForTokenClassification""",
							    """LayoutLMv2Layer""",
							    """LayoutLMv2Model""",
							    """LayoutLMv2PreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
							from .processing_layoutlmva import LayoutLMvaProcessor
							from .tokenization_layoutlmva import LayoutLMvaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlmva import (
														    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMvaForQuestionAnswering,
														    LayoutLMvaForSequenceClassification,
														    LayoutLMvaForTokenClassification,
														    LayoutLMvaLayer,
														    LayoutLMvaModel,
														    LayoutLMvaPreTrainedModel,
														)
else:
							import sys

							lowerCamelCase_						: Dict         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_						: Dict         =  logging.get_logger(__name__)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       Tuple=None						,	**snake_case_ :       List[str]      ):
							warnings.warn(
							    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
							    """instead."""						,	snake_case_						,	)
							super().__init__(args=snake_case_						,	**snake_case_      )






