






"""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


_lowerCAmelCase  :  Dict  							=			logging.get_logger(__name__)

_lowerCAmelCase  :  Tuple  							=			{
    '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__ (						__magic_name__      ):
   SCREAMING_SNAKE_CASE_         ='''layoutlm'''
   def __init__(		self			:  Optional[Any]       , snake_case__			:  List[Any]=3_0_5_2_2       , snake_case__			:  Union[str, Any]=7_6_8       , snake_case__			:  List[str]=1_2       , snake_case__			:  Tuple=1_2       , snake_case__			:  int=3_0_7_2       , snake_case__			:  Dict="gelu"       , snake_case__			:  Union[str, Any]=0.1       , snake_case__			:  Optional[Any]=0.1       , snake_case__			:  Any=5_1_2       , snake_case__			:  List[Any]=2       , snake_case__			:  List[str]=0.02       , snake_case__			:  Union[str, Any]=1e-12       , snake_case__			:  Union[str, Any]=0       , snake_case__			:  Union[str, Any]="absolute"       , snake_case__			:  List[str]=True       , snake_case__			:  Dict=1_0_2_4       , **snake_case__			:  int       , ):


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




class  lowerCAmelCase__ (						__magic_name__      ):
   def __init__(		self			:  List[str]       , snake_case__			:  List[Any]       , snake_case__			:  Optional[Any] = "default"       , snake_case__			:  Optional[Any] = None       , ):


        '''simple docstring'''
        super().__init__(snake_case__       , task=snake_case__       , patching_specs=snake_case__       )
        UpperCAmelCase__	:						Optional[Any]       =       config.max_ad_position_embeddings - 1
   @property
   def 			__a       (		self			:  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 			__a       (		self			:  str       , snake_case__			:  Dict       , snake_case__			:  Optional[int] = -1       , snake_case__			:  str = -1       , snake_case__			:  List[str] = False       , snake_case__			:  Union[str, Any] = None       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[Any]       =       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__	:						Any       =       [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__	:						Tuple       =       input_dict['''input_ids'''].shape
        UpperCAmelCase__	:						Optional[int]       =       torch.tensor([*[box] * seq_length]       ).tile(snake_case__       , 1       , 1       )
        return input_dict










"""simple docstring"""



# Lint as: python3

import hashlib
from typing import Union


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, int, bytes]							)->		bytes:






     '''simple docstring'''



     if isinstance(snake_case ,				snake_case							):
          # Data already in bytes, returns as it as
          return hash_data
     elif isinstance(snake_case ,				snake_case							):
          # We keep the data as it as for it ot be later encoded to UTF-8
          # However replace `\\` with `/` for Windows compatibility
          UpperCAmelCase__	:						Union[str, Any]       =       hash_data.replace("\\" ,				"/"							)
     elif isinstance(snake_case ,				snake_case							):
          UpperCAmelCase__	:						int       =       str(snake_case							)
     else:
          # If data is not of the required type, raise error
          raise InvalidKeyError(snake_case							)

     return hash_data.encode("utf-8"							)




class  lowerCAmelCase__ (						__magic_name__      ):
   def __init__(		self			:  List[str]       , snake_case__			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
        UpperCAmelCase__	:						str       =       f'\nFound Key {hash_data} of type {type(snake_case__       )}'
        UpperCAmelCase__	:						Union[str, Any]       =       "\nKeys should be either str, int or bytes type"
        super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'       )




class  lowerCAmelCase__ (						__magic_name__      ):
   def __init__(		self			:  List[str]       , snake_case__			:  Dict       , snake_case__			:  Optional[int]       , snake_case__			:  List[str]=""       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       key
        UpperCAmelCase__	:						Optional[Any]       =       duplicate_key_indices
        UpperCAmelCase__	:						str       =       fix_msg
        UpperCAmelCase__	:						Any       =       "Found multiple examples generated with the same key"
        if len(snake_case__       ) <= 2_0:
             UpperCAmelCase__	:						int       =       f'\nThe examples at index {", ".join(snake_case__       )} have the key {key}'
        else:
             UpperCAmelCase__	:						str       =       f'\nThe examples at index {", ".join(duplicate_key_indices[:2_0]       )}... ({len(snake_case__       ) - 2_0} more) have the key {key}'
        UpperCAmelCase__	:						Any       =       "\n" + fix_msg if fix_msg else ""
        super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'       )




class  lowerCAmelCase__ :
   def __init__(		self			:  Dict       , snake_case__			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       hashlib.mda(_as_bytes(snake_case__       )       )




   def 			__a       (		self			:  int       , snake_case__			:  Union[str, int, bytes]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       self._split_mda.copy()
        UpperCAmelCase__	:						List[str]       =       _as_bytes(snake_case__       )
        mda.update(snake_case__       )
        # Convert to integer with hexadecimal conversion
        return int(mda.hexdigest()       , 1_6       )


