


'''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				:							Dict		       =		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:							List[str]		       =		{
    """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 				__lowerCAmelCase      (      UpperCamelCase__	):

						"""simple docstring"""






						_UpperCAmelCase		:					str      	="""layoutlm"""






						def __init__(     self       :			int   ,     lowerCAmelCase       :			Union[str, Any]=3_05_22   ,     lowerCAmelCase       :			Tuple=7_68   ,     lowerCAmelCase       :			Tuple=12   ,     lowerCAmelCase       :			List[Any]=12   ,     lowerCAmelCase       :			Any=30_72   ,     lowerCAmelCase       :			int="gelu"   ,     lowerCAmelCase       :			str=0.1   ,     lowerCAmelCase       :			List[Any]=0.1   ,     lowerCAmelCase       :			List[Any]=5_12   ,     lowerCAmelCase       :			List[str]=2   ,     lowerCAmelCase       :			Union[str, Any]=0.0_2   ,     lowerCAmelCase       :			Any=1e-12   ,     lowerCAmelCase       :			Dict=0   ,     lowerCAmelCase       :			int="absolute"   ,     lowerCAmelCase       :			int=True   ,     lowerCAmelCase       :			List[Any]=10_24   ,     **lowerCAmelCase       :			int   ,     ):
													super().__init__(pad_token_id=lowerCAmelCase__   ,     **lowerCAmelCase__  )
													A_ 						=		vocab_size
													A_ 						=		hidden_size
													A_ 						=		num_hidden_layers
													A_ 						=		num_attention_heads
													A_ 						=		hidden_act
													A_ 						=		intermediate_size
													A_ 						=		hidden_dropout_prob
													A_ 						=		attention_probs_dropout_prob
													A_ 						=		max_position_embeddings
													A_ 						=		type_vocab_size
													A_ 						=		initializer_range
													A_ 						=		layer_norm_eps
													A_ 						=		position_embedding_type
													A_ 						=		use_cache
													A_ 						=		max_ad_position_embeddings






class 				__lowerCAmelCase      (      UpperCamelCase__	):

						"""simple docstring"""






						def __init__(     self       :			int   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			List[Any] = "default"   ,     lowerCAmelCase       :			Optional[Any] = None   ,     ):
													super().__init__(lowerCAmelCase__   ,     task=lowerCAmelCase__   ,     patching_specs=lowerCAmelCase__  )
													A_ 						=		config.max_ad_position_embeddings - 1






						@property
						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													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       :			Union[str, Any]   ,     lowerCAmelCase       :			List[Any]   ,     lowerCAmelCase       :			Tuple = -1   ,     lowerCAmelCase       :			Tuple = -1   ,     lowerCAmelCase       :			int = False   ,     lowerCAmelCase       :			str = None   ,     ):
													A_ 						=		super().generate_dummy_inputs(
													    lowerCAmelCase__   ,     batch_size=lowerCAmelCase__   ,     seq_length=lowerCAmelCase__   ,     is_pair=lowerCAmelCase__   ,     framework=lowerCAmelCase__  )

													# Generate a dummy bbox
													A_ 						=		[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

													A_ 						=		input_dict["input_ids"].shape
													A_ 						=		torch.tensor([*[box] * seq_length]  ).tile(lowerCAmelCase__   ,     1   ,     1  )
													return input_dict



'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													if text is None and images is None:
																				raise ValueError("You have to specify either text or images. Both cannot be none."  )

													if text is not None:
																				A_ 						=		self.tokenizer(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if text is not None and images is not None:
																				A_ 						=		image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





