
"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


A__   :						Optional[int] 							=     logging.get_logger(__name__)

A__   :						Optional[Any] 							=     {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}







class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				'layoutlm'



     def __init__(   self			,      __UpperCamelCase=3_05_22			,      __UpperCamelCase=7_68			,      __UpperCamelCase=12			,      __UpperCamelCase=12			,      __UpperCamelCase=30_72			,      __UpperCamelCase="gelu"			,      __UpperCamelCase=0.1			,      __UpperCamelCase=0.1			,      __UpperCamelCase=5_12			,      __UpperCamelCase=2			,      __UpperCamelCase=0.02			,      __UpperCamelCase=1E-12			,      __UpperCamelCase=0			,      __UpperCamelCase="absolute"			,      __UpperCamelCase=True			,      __UpperCamelCase=10_24			,      **__UpperCamelCase			,      )->       str:
          super().__init__(pad_token_id=__UpperCamelCase			,      **__UpperCamelCase	)
          UpperCAmelCase__				:			List[Any]							=    vocab_size
          UpperCAmelCase__				:			Optional[int]							=    hidden_size
          UpperCAmelCase__				:			List[Any]							=    num_hidden_layers
          UpperCAmelCase__				:			Optional[Any]							=    num_attention_heads
          UpperCAmelCase__				:			List[Any]							=    hidden_act
          UpperCAmelCase__				:			Any							=    intermediate_size
          UpperCAmelCase__				:			Any							=    hidden_dropout_prob
          UpperCAmelCase__				:			str							=    attention_probs_dropout_prob
          UpperCAmelCase__				:			Union[str, Any]							=    max_position_embeddings
          UpperCAmelCase__				:			Tuple							=    type_vocab_size
          UpperCAmelCase__				:			Any							=    initializer_range
          UpperCAmelCase__				:			Any							=    layer_norm_eps
          UpperCAmelCase__				:			List[Any]							=    position_embedding_type
          UpperCAmelCase__				:			Tuple							=    use_cache
          UpperCAmelCase__				:			Any							=    max_ad_position_embeddings







class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase = "default"			,      __UpperCamelCase = None			,      )->       Optional[int]:
          super().__init__(__UpperCamelCase			,      task=__UpperCamelCase			,      patching_specs=__UpperCamelCase	)
          UpperCAmelCase__				:			List[str]							=    config.max_ad_position_embeddings - 1



     @property
     def 			lowerCAmelCase__							(   self	)->       Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "sequence"}),
                  ("bbox", {0: "batch", 1: "sequence"}),
                  ("attention_mask", {0: "batch", 1: "sequence"}),
                  ("token_type_ids", {0: "batch", 1: "sequence"}),
              ]	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase = -1			,      __UpperCamelCase = -1			,      __UpperCamelCase = False			,      __UpperCamelCase = None			,      )->       Mapping[str, Any]:
          UpperCAmelCase__				:			Any							=    super().generate_dummy_inputs(
              __UpperCamelCase			,      batch_size=__UpperCamelCase			,      seq_length=__UpperCamelCase			,      is_pair=__UpperCamelCase			,      framework=__UpperCamelCase	)

          # Generate a dummy bbox
          UpperCAmelCase__				:			Union[str, Any]							=    [48, 84, 73, 1_28]

          if not framework == TensorType.PYTORCH:
               raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."	)

          if not is_torch_available():
               raise ValueError("Cannot generate dummy inputs without PyTorch installed."	)
          import torch

          UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[int]							=    input_dict["input_ids"].shape
          UpperCAmelCase__				:			Any							=    torch.tensor([*[box] * seq_length]	).tile(__UpperCamelCase			,      1			,      1	)
          return input_dict





"""simple docstring"""




from typing import TYPE_CHECKING

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


A__   :						Optional[Any] 							=     {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						List[Any] 							=     ["""ViltFeatureExtractor"""]
    A__   :						Any 							=     ["""ViltImageProcessor"""]
    A__   :						int 							=     ["""ViltProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						str 							=     [
        """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViltForImageAndTextRetrieval""",
        """ViltForImagesAndTextClassification""",
        """ViltForTokenClassification""",
        """ViltForMaskedLM""",
        """ViltForQuestionAnswering""",
        """ViltLayer""",
        """ViltModel""",
        """ViltPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_vilt import ViltFeatureExtractor
        from .image_processing_vilt import ViltImageProcessor
        from .processing_vilt import ViltProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            ViltForTokenClassification,
            ViltLayer,
            ViltModel,
            ViltPreTrainedModel,
        )


else:
    import sys

    A__   :						Any 							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)



