






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


lowercase     :							Any	     =			logging.get_logger(__name__)

lowercase     :							Optional[int]	     =			{
    '''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_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      Optional[int]						 =    "layoutlm"






						def __init__(		self						, _SCREAMING_SNAKE_CASE=3_0522						, _SCREAMING_SNAKE_CASE=768						, _SCREAMING_SNAKE_CASE=12						, _SCREAMING_SNAKE_CASE=12						, _SCREAMING_SNAKE_CASE=3072						, _SCREAMING_SNAKE_CASE="gelu"						, _SCREAMING_SNAKE_CASE=0.1						, _SCREAMING_SNAKE_CASE=0.1						, _SCREAMING_SNAKE_CASE=512						, _SCREAMING_SNAKE_CASE=2						, _SCREAMING_SNAKE_CASE=0.02						, _SCREAMING_SNAKE_CASE=1e-12						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE="absolute"						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=1024						, **_SCREAMING_SNAKE_CASE						, )       -> List[str]:
								super().__init__(pad_token_id=lowercase__						, **lowercase__ )
								snake_case_ :							Any         =				vocab_size
								snake_case_ :							int         =				hidden_size
								snake_case_ :							List[str]         =				num_hidden_layers
								snake_case_ :							Dict         =				num_attention_heads
								snake_case_ :							Optional[int]         =				hidden_act
								snake_case_ :							Union[str, Any]         =				intermediate_size
								snake_case_ :							Dict         =				hidden_dropout_prob
								snake_case_ :							str         =				attention_probs_dropout_prob
								snake_case_ :							List[str]         =				max_position_embeddings
								snake_case_ :							Union[str, Any]         =				type_vocab_size
								snake_case_ :							Union[str, Any]         =				initializer_range
								snake_case_ :							Optional[Any]         =				layer_norm_eps
								snake_case_ :							Dict         =				position_embedding_type
								snake_case_ :							str         =				use_cache
								snake_case_ :							List[Any]         =				max_ad_position_embeddings




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = "default"						, _SCREAMING_SNAKE_CASE = None						, )       -> Union[str, Any]:
								super().__init__(lowercase__						, task=lowercase__						, patching_specs=lowercase__ )
								snake_case_ :							Dict         =				config.max_ad_position_embeddings - 1






						@property
						def 					_lowerCAmelCase				(		self )       -> Mapping[str, Mapping[int, str]]:
								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						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = None						, )       -> Mapping[str, Any]:
								snake_case_ :							List[str]         =				super().generate_dummy_inputs(
								    lowercase__						, batch_size=lowercase__						, seq_length=lowercase__						, is_pair=lowercase__						, framework=lowercase__ )

								# Generate a dummy bbox
								snake_case_ :							List[Any]         =				[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

								snake_case_       ,				snake_case_ :							Optional[Any]         =				input_dict["input_ids"].shape
								snake_case_ :							Dict         =				torch.tensor([*[box] * seq_length] ).tile(lowercase__						, 1						, 1 )
								return input_dict


"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										SCREAMING_SNAKE_CASE				=	hash_data.replace('\\',     '/'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	str(SCREAMING_SNAKE_CASE_			)
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(SCREAMING_SNAKE_CASE_			)

					return hash_data.encode('utf-8'			)




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	'\nFAILURE TO GENERATE DATASET: Invalid key type detected'
									SCREAMING_SNAKE_CASE				=	f'''\nFound Key {hash_data} of type {type(lowercase__			)}'''
									SCREAMING_SNAKE_CASE				=	'\nKeys should be either str, int or bytes type'
									super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''			)






class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__		,	lowercase__=""			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	key
									SCREAMING_SNAKE_CASE				=	duplicate_key_indices
									SCREAMING_SNAKE_CASE				=	fix_msg
									SCREAMING_SNAKE_CASE				=	'Found multiple examples generated with the same key'
									if len(lowercase__			) <= 20:
														SCREAMING_SNAKE_CASE				=	f'''\nThe examples at index {', '.join(lowercase__			)} have the key {key}'''
									else:
														SCREAMING_SNAKE_CASE				=	f'''\nThe examples at index {', '.join(duplicate_key_indices[:20]			)}... ({len(lowercase__			) - 20} more) have the key {key}'''
									SCREAMING_SNAKE_CASE				=	'\n' + fix_msg if fix_msg else ''
									super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''			)






class      UpperCamelCase	:

				"""simple docstring"""



				def __init__(				self		,	lowercase__			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	hashlib.mda(_as_bytes(lowercase__			)			)




				def 							A					(				self		,	lowercase__			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self._split_mda.copy()
									SCREAMING_SNAKE_CASE				=	_as_bytes(lowercase__			)
									mda.update(lowercase__			)
									# Convert to integer with hexadecimal conversion
									return int(mda.hexdigest()		,	16			)
