"""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


lowerCAmelCase_:       List[str]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Tuple			     =       {
    "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  a__     ( _a						):
   snake_case_				=   "layoutlm"


   def __init__(  self,					_UpperCAmelCase=3_0522,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.1,					_UpperCAmelCase=0.1,					_UpperCAmelCase=512,					_UpperCAmelCase=2,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=0,					_UpperCAmelCase="absolute",					_UpperCAmelCase=True,					_UpperCAmelCase=1024,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(pad_token_id=_UpperCAmelCase,					**_UpperCAmelCase	)
        lowercase__								=				vocab_size
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				hidden_act
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				max_position_embeddings
        lowercase__								=				type_vocab_size
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				position_embedding_type
        lowercase__								=				use_cache
        lowercase__								=				max_ad_position_embeddings






class  a__     ( _a						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase = "default",					_UpperCAmelCase = None,					):
        '''simple docstring'''



        super().__init__(_UpperCAmelCase,					task=_UpperCAmelCase,					patching_specs=_UpperCAmelCase	)
        lowercase__								=				config.max_ad_position_embeddings - 1


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        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 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = -1,					_UpperCAmelCase = -1,					_UpperCAmelCase = False,					_UpperCAmelCase = None,					):
        '''simple docstring'''



        lowercase__								=				super().generate_dummy_inputs(
            _UpperCAmelCase,					batch_size=_UpperCAmelCase,					seq_length=_UpperCAmelCase,					is_pair=_UpperCAmelCase,					framework=_UpperCAmelCase	)

        # Generate a dummy bbox
        lowercase__								=				[48, 84, 73, 128]

        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

        lowercase__			,    lowercase__								=				input_dict["input_ids"].shape
        lowercase__								=				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


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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




