

'''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       :      List[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   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							"layoutlm"







		def __init__(			self   ,      snake_case=3_0_5_2_2   ,      snake_case=7_6_8   ,      snake_case=1_2   ,      snake_case=1_2   ,      snake_case=3_0_7_2   ,      snake_case="gelu"   ,      snake_case=0.1   ,      snake_case=0.1   ,      snake_case=5_1_2   ,      snake_case=2   ,      snake_case=0.02   ,      snake_case=1e-12   ,      snake_case=0   ,      snake_case="absolute"   ,      snake_case=True   ,      snake_case=1_0_2_4   ,      **snake_case   ,      ):


							'''simple docstring'''

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







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case = "default"   ,      snake_case = None   ,      ):


							'''simple docstring'''

							super().__init__(snake_case   ,      task=snake_case   ,      patching_specs=snake_case      )
							UpperCAmelCase		:							str        =       config.max_ad_position_embeddings - 1







		@property
		def 				A_	(			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 				A_	(			self   ,      snake_case   ,      snake_case = -1   ,      snake_case = -1   ,      snake_case = False   ,      snake_case = None   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       super().generate_dummy_inputs(
							    snake_case   ,      batch_size=snake_case   ,      seq_length=snake_case   ,      is_pair=snake_case   ,      framework=snake_case      )

							# Generate a dummy bbox
							UpperCAmelCase		:							Optional[int]        =       [4_8, 8_4, 7_3, 1_2_8]

							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		:							List[str]        =       input_dict["input_ids"].shape
							UpperCAmelCase		:							List[Any]        =       torch.tensor([*[box] * seq_length]      ).tile(snake_case   ,      1   ,      1      )
							return input_dict



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
