from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
		import pyximport

		pyximport.install(setup_args={"""include_dirs""": np.get_include()})
		from . import algos_graphormer  # noqa E402





def     UpperCAmelCase						(   lowercase							,      lowercase = 512							):







				"""simple docstring"""



				__lowercase			      =							x.shape[1] if len(x.shape							) > 1 else 1
				__lowercase			      =							1 + np.arange(0							,      feature_num * offset							,      lowercase							,      dtype=np.intaa							)
				__lowercase			      =							x + feature_offset
				return x





def     UpperCAmelCase						(   lowercase							,      lowercase=True							):







				"""simple docstring"""



				requires_backends(lowercase							,      ['''cython''']							)

				if keep_features and "edge_attr" in item.keys():  # edge_attr
								__lowercase			      =							np.asarray(item['''edge_attr''']							,      dtype=np.intaa							)
				else:
								__lowercase			      =							np.ones((len(item['''edge_index'''][0]							), 1)							,      dtype=np.intaa							)  # same embedding for all

				if keep_features and "node_feat" in item.keys():  # input_nodes
								__lowercase			      =							np.asarray(item['''node_feat''']							,      dtype=np.intaa							)
				else:
								__lowercase			      =							np.ones((item['''num_nodes'''], 1)							,      dtype=np.intaa							)  # same embedding for all

				__lowercase			      =							np.asarray(item['''edge_index''']							,      dtype=np.intaa							)

				__lowercase			      =							convert_to_single_emb(lowercase							) + 1
				__lowercase			      =							item['''num_nodes''']

				if len(edge_attr.shape							) == 1:
								__lowercase			      =							edge_attr[:, None]
				__lowercase			      =							np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]							,      dtype=np.intaa							)
				__lowercase			      =							convert_to_single_emb(lowercase							) + 1

				# node adj matrix [num_nodes, num_nodes] bool
				__lowercase			      =							np.zeros([num_nodes, num_nodes]							,      dtype=lowercase							)
				__lowercase			      =							True

				__lowercase      ,					__lowercase			      =							algos_graphormer.floyd_warshall(lowercase							)
				__lowercase			      =							np.amax(lowercase							)

				__lowercase			      =							algos_graphormer.gen_edge_input(lowercase							,      lowercase							,      lowercase							)
				__lowercase			      =							np.zeros([num_nodes + 1, num_nodes + 1]							,      dtype=np.single							)  # with graph token

				# combine
				__lowercase			      =							input_nodes + 1  # we shift all indices by one for padding
				__lowercase			      =							attn_bias
				__lowercase			      =							attn_edge_type
				__lowercase			      =							shortest_path_result.astype(np.intaa							) + 1  # we shift all indices by one for padding
				__lowercase			      =							np.sum(lowercase							,      axis=1							).reshape(-1							) + 1  # we shift all indices by one for padding
				__lowercase			      =							item['''in_degree''']  # for undirected graph
				__lowercase			      =							input_edges + 1  # we shift all indices by one for padding
				if "labels" not in item:
								__lowercase			      =							item['''y''']

				return item




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__=20				,       lowerCAmelCase__=False						)					-> Any:

							'''simple docstring'''


							if not is_cython_available():
											raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''						)

							__lowercase			      =							spatial_pos_max
							__lowercase			      =							on_the_fly_processing





			def __call__(       self				,       lowerCAmelCase__						)					-> Dict[str, Any]:

							'''simple docstring'''


							if self.on_the_fly_processing:
											__lowercase			      =							[preprocess_item(lowerCAmelCase__						) for i in features]

							if not isinstance(features[0]				,       lowerCAmelCase__						):
											__lowercase			      =							[vars(lowerCAmelCase__						) for f in features]
							__lowercase			      =							{}

							__lowercase			      =							max(len(i['''input_nodes''']						) for i in features						)
							__lowercase			      =							len(features[0]['''input_nodes'''][0]						)
							__lowercase			      =							len(features[0]['''attn_edge_type'''][0][0]						)
							__lowercase			      =							max(len(i['''input_edges'''][0][0]						) for i in features						)
							__lowercase			      =							len(features[0]['''input_edges'''][0][0][0]						)
							__lowercase			      =							len(lowerCAmelCase__						)

							__lowercase			      =							torch.zeros(lowerCAmelCase__				,       max_node_num + 1				,       max_node_num + 1				,       dtype=torch.float						)
							__lowercase			      =							torch.zeros(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       dtype=torch.long						)
							__lowercase			      =							torch.zeros(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       dtype=torch.long						)
							__lowercase			      =							torch.zeros(lowerCAmelCase__				,       lowerCAmelCase__				,       dtype=torch.long						)
							__lowercase			      =							torch.zeros(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       dtype=torch.long						)
							__lowercase			      =							torch.zeros(
							    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       dtype=torch.long						)

							for ix, f in enumerate(lowerCAmelCase__						):
											for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
															__lowercase			      =							torch.tensor(f[k]						)

											if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]						) > 0:
															__lowercase			      =							float('''-inf'''						)

											__lowercase			      =							f['''attn_bias''']
											__lowercase			      =							f[
											    '''attn_edge_type'''
											]
											__lowercase			      =							f['''spatial_pos''']
											__lowercase			      =							f['''in_degree''']
											__lowercase			      =							f['''input_nodes''']
											__lowercase			      =							f['''input_edges''']

							__lowercase			      =							batch['''in_degree''']

							__lowercase			      =							features[0]['''labels''']
							if len(lowerCAmelCase__						) == 1:  # one task
											if isinstance(sample[0]				,       lowerCAmelCase__						):  # regression
															__lowercase			      =							torch.from_numpy(np.concatenate([i['''labels'''] for i in features]						)						)
											else:  # binary classification
															__lowercase			      =							torch.from_numpy(np.concatenate([i['''labels'''] for i in features]						)						)
							else:  # multi task classification, left to float to keep the NaNs
											__lowercase			      =							torch.from_numpy(np.stack([i['''labels'''] for i in features]				,       axis=0						)						)

							return batch
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  :					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        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__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        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, 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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[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

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