
'''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


SCREAMING_SNAKE_CASE    =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =					{
    "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        UpperCAmelCase_       (						UpperCamelCase_							):

 '''simple docstring'''
 lowercase_				:						str				=       "layoutlm"
 def __init__(				self		:     Tuple ,							snake_case__		:     Union[str, Any]=3_05_22 ,							snake_case__		:     List[Any]=7_68 ,							snake_case__		:     Union[str, Any]=12 ,							snake_case__		:     Tuple=12 ,							snake_case__		:     Tuple=30_72 ,							snake_case__		:     str="gelu" ,							snake_case__		:     Tuple=0.1 ,							snake_case__		:     List[Any]=0.1 ,							snake_case__		:     Dict=5_12 ,							snake_case__		:     Optional[int]=2 ,							snake_case__		:     Tuple=0.02 ,							snake_case__		:     int=1e-12 ,							snake_case__		:     Optional[Any]=0 ,							snake_case__		:     int="absolute" ,							snake_case__		:     List[Any]=True ,							snake_case__		:     str=10_24 ,							**snake_case__		:     Optional[Any] ,							):



   '''simple docstring'''




   super().__init__(pad_token_id=__UpperCamelCase ,							**__UpperCamelCase   )
   UpperCAmelCase__			:				int												=       vocab_size
   UpperCAmelCase__			:				Dict												=       hidden_size
   UpperCAmelCase__			:				Any												=       num_hidden_layers
   UpperCAmelCase__			:				List[Any]												=       num_attention_heads
   UpperCAmelCase__			:				Any												=       hidden_act
   UpperCAmelCase__			:				List[str]												=       intermediate_size
   UpperCAmelCase__			:				Union[str, Any]												=       hidden_dropout_prob
   UpperCAmelCase__			:				List[Any]												=       attention_probs_dropout_prob
   UpperCAmelCase__			:				List[str]												=       max_position_embeddings
   UpperCAmelCase__			:				Union[str, Any]												=       type_vocab_size
   UpperCAmelCase__			:				List[str]												=       initializer_range
   UpperCAmelCase__			:				Dict												=       layer_norm_eps
   UpperCAmelCase__			:				List[str]												=       position_embedding_type
   UpperCAmelCase__			:				Union[str, Any]												=       use_cache
   UpperCAmelCase__			:				Any												=       max_ad_position_embeddings






class        UpperCAmelCase_       (						UpperCamelCase_							):

 '''simple docstring'''
 def __init__(				self		:     Any ,							snake_case__		:     int ,							snake_case__		:     Optional[int] = "default" ,							snake_case__		:     Any = None ,							):



   '''simple docstring'''




   super().__init__(__UpperCamelCase ,							task=__UpperCamelCase ,							patching_specs=__UpperCamelCase   )
   UpperCAmelCase__			:				Any												=       config.max_ad_position_embeddings - 1
 @property
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''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        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     Any ,							snake_case__		:     Any = -1 ,							snake_case__		:     str = -1 ,							snake_case__		:     Tuple = False ,							snake_case__		:     List[str] = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       super().generate_dummy_inputs(
       __UpperCamelCase ,							batch_size=__UpperCamelCase ,							seq_length=__UpperCamelCase ,							is_pair=__UpperCamelCase ,							framework=__UpperCamelCase   )

   # Generate a dummy bbox
   UpperCAmelCase__			:				Tuple												=       [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__			:				Any												=       input_dict['''input_ids'''].shape
   UpperCAmelCase__			:				List[str]												=       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


SCREAMING_SNAKE_CASE    =					{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DonutSwinModel""",
          """DonutSwinPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					["""DonutFeatureExtractor"""]
      SCREAMING_SNAKE_CASE    =					["""DonutImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
      from .processing_donut import DonutProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_donut_swin import (
                DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                DonutSwinModel,
                DonutSwinPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_donut import DonutFeatureExtractor
            from .image_processing_donut import DonutImageProcessor

else:
      import sys

      SCREAMING_SNAKE_CASE    =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



