


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


snake_case__     							= logging.get_logger(__name__)

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     snake_case__  (      UpperCamelCase__  ):
				__lowerCAmelCase		=      """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-1_2     ,      _UpperCAmelCase=0     ,      _UpperCAmelCase="absolute"     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=1024     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




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






class     snake_case__  (      UpperCamelCase__  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = "default"     ,      _UpperCAmelCase = None     ,      ):




										"""simple docstring"""




										super().__init__(lowerCAmelCase__     ,      task=lowerCAmelCase__     ,      patching_specs=lowerCAmelCase__      )
										__magic_name__				:							Union[str, Any]				=      config.max_ad_position_embeddings - 1
				@property
				def  UpperCamelCase_						(  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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = -1     ,      _UpperCAmelCase = -1     ,      _UpperCAmelCase = False     ,      _UpperCAmelCase = None     ,      ):




										"""simple docstring"""




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

										# Generate a dummy bbox
										__magic_name__				:							Tuple				=      [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

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




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

						# Save pytorch-model
						print(f"Save PyTorch model and config to {pytorch_dump_path}"				)
						model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


