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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'layoutlm'


	def __init__(   self       ,  lowerCAmelCase_=30522       ,  lowerCAmelCase_=768       ,  lowerCAmelCase_=12       ,  lowerCAmelCase_=12       ,  lowerCAmelCase_=3072       ,  lowerCAmelCase_="gelu"       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=0.0_2       ,  lowerCAmelCase_=1e-12       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_="absolute"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=1024       ,  **lowerCAmelCase_       ,  ):
		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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = "default"       ,  lowerCAmelCase_ = None       ,  ):
		super().__init__(lowerCAmelCase_       ,  task=lowerCAmelCase_       ,  patching_specs=lowerCAmelCase_       )
		_lowercase   =config.max_ad_position_embeddings - 1


	@property
	def __lowerCAmelCase      (   self       ):
		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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = -1       ,  lowerCAmelCase_ = -1       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = None       ,  ):
		_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, 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

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





'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase       :     Dict			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """flaubert/flaubert_small_cased""": """https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json""",
    """flaubert/flaubert_base_uncased""": """https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json""",
    """flaubert/flaubert_base_cased""": """https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json""",
    """flaubert/flaubert_large_cased""": """https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"flaubert"
    A     :    List[str]				=			{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }





    def __init__(					self     :						str							,							UpperCAmelCase_     :						Optional[int]=False							,							UpperCAmelCase_     :						List[Any]=0.0							,							UpperCAmelCase_     :						List[Any]=3_0_1_4_5							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=1_2							,							UpperCAmelCase_     :						str=1_6							,							UpperCAmelCase_     :						Tuple=0.1							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Any=False							,							UpperCAmelCase_     :						str=False							,							UpperCAmelCase_     :						Tuple=1							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Tuple=5_1_2							,							UpperCAmelCase_     :						List[str]=2_0_4_8**-0.5							,							UpperCAmelCase_     :						List[Any]=1e-12							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=0							,							UpperCAmelCase_     :						List[Any]=1							,							UpperCAmelCase_     :						str=2							,							UpperCAmelCase_     :						Optional[Any]=3							,							UpperCAmelCase_     :						List[str]=5							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						List[Any]="first"							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Union[str, Any]=None							,							UpperCAmelCase_     :						Union[str, Any]=True							,							UpperCAmelCase_     :						Any=0.1							,							UpperCAmelCase_     :						List[Any]=5							,							UpperCAmelCase_     :						List[Any]=5							,							UpperCAmelCase_     :						Union[str, Any]=0							,							UpperCAmelCase_     :						Optional[Any]=0							,							UpperCAmelCase_     :						List[Any]=2							,							UpperCAmelCase_     :						List[Any]=0							,							**UpperCAmelCase_     :						List[Any]							,							):


         """simple docstring"""







         a					:							Any							= pre_norm
         a					:							str							= layerdrop
         a					:							int							= vocab_size
         a					:							str							= emb_dim
         a					:							Any							= n_layers
         a					:							Dict							= n_heads
         a					:							Union[str, Any]							= dropout
         a					:							List[Any]							= attention_dropout
         a					:							List[str]							= gelu_activation
         a					:							int							= sinusoidal_embeddings
         a					:							Union[str, Any]							= causal
         a					:							str							= asm
         a					:							Tuple							= n_langs
         a					:							Optional[Any]							= use_lang_emb
         a					:							str							= layer_norm_eps
         a					:							Optional[Any]							= bos_index
         a					:							Union[str, Any]							= eos_index
         a					:							str							= pad_index
         a					:							List[str]							= unk_index
         a					:							Dict							= mask_index
         a					:							Optional[Any]							= is_encoder
         a					:							str							= max_position_embeddings
         a					:							Optional[Any]							= embed_init_std
         a					:							str							= init_std
         a					:							Union[str, Any]							= summary_type
         a					:							Tuple							= summary_use_proj
         a					:							Tuple							= summary_activation
         a					:							str							= summary_proj_to_labels
         a					:							List[str]							= summary_first_dropout
         a					:							Any							= start_n_top
         a					:							Optional[Any]							= end_n_top
         a					:							List[Any]							= mask_token_id
         a					:							Optional[Any]							= lang_id

         if "n_words" in kwargs:
              a					:							int							= kwargs['n_words']

         super().__init__(pad_token_id=UpperCAmelCase_							,							bos_token_id=UpperCAmelCase_							,							**UpperCAmelCase_)






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         if self.task == "multiple-choice":
              a					:							int							= {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              a					:							Tuple							= {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

