



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__A				    =						logging.get_logger(__name__)

__A				    =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A				    =						{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

__A				    =						{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}






class 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					int		   =							VOCAB_FILES_NAMES
	a__      :					Dict		   =							PRETRAINED_VOCAB_FILES_MAP
	a__      :					Union[str, Any]		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__      :					Any		   =							["input_ids", "attention_mask"]
	a__      :					str		   =							LongformerTokenizer




	def __init__(	self	:						Any						,  __UpperCAmelCase	:						Any=None						,  __UpperCAmelCase	:						str=None						,  __UpperCAmelCase	:						Any=None						,  __UpperCAmelCase	:						str="replace"						,  __UpperCAmelCase	:						Dict="<s>"						,  __UpperCAmelCase	:						str="</s>"						,  __UpperCAmelCase	:						List[str]="</s>"						,  __UpperCAmelCase	:						int="<s>"						,  __UpperCAmelCase	:						Any="<unk>"						,  __UpperCAmelCase	:						List[Any]="<pad>"						,  __UpperCAmelCase	:						Optional[Any]="<mask>"						,  __UpperCAmelCase	:						Any=False						,  __UpperCAmelCase	:						str=True						,  **__UpperCAmelCase	:						Tuple						,  )			-> Tuple:
							super().__init__(
							    __UpperCAmelCase						,  __UpperCAmelCase						,  tokenizer_file=__UpperCAmelCase						,  errors=__UpperCAmelCase						,  bos_token=__UpperCAmelCase						,  eos_token=__UpperCAmelCase						,  sep_token=__UpperCAmelCase						,  cls_token=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  mask_token=__UpperCAmelCase						,  add_prefix_space=__UpperCAmelCase						,  trim_offsets=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

							UpperCAmelCase_=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
							if pre_tok_state.get("""add_prefix_space"""						,  __UpperCAmelCase     ) != add_prefix_space:
													UpperCAmelCase_=   getattr(__UpperCAmelCase						,  pre_tok_state.pop("""type"""     )     )
													UpperCAmelCase_=   add_prefix_space
													UpperCAmelCase_=   pre_tok_class(**__UpperCAmelCase     )

							UpperCAmelCase_=   add_prefix_space

							UpperCAmelCase_=   """post_processor"""
							UpperCAmelCase_=   getattr(self.backend_tokenizer						,  __UpperCAmelCase						,  __UpperCAmelCase     )
							if tokenizer_component_instance:
													UpperCAmelCase_=   json.loads(tokenizer_component_instance.__getstate__()     )

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																			UpperCAmelCase_=   tuple(state["""sep"""]     )
													if "cls" in state:
																			UpperCAmelCase_=   tuple(state["""cls"""]     )

													UpperCAmelCase_=   False

													if state.get("""add_prefix_space"""						,  __UpperCAmelCase     ) != add_prefix_space:
																			UpperCAmelCase_=   add_prefix_space
																			UpperCAmelCase_=   True

													if state.get("""trim_offsets"""						,  __UpperCAmelCase     ) != trim_offsets:
																			UpperCAmelCase_=   trim_offsets
																			UpperCAmelCase_=   True

													if changes_to_apply:
																			UpperCAmelCase_=   getattr(__UpperCAmelCase						,  state.pop("""type"""     )     )
																			UpperCAmelCase_=   component_class(**__UpperCAmelCase     )
																			setattr(self.backend_tokenizer						,  __UpperCAmelCase						,  __UpperCAmelCase     )




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> str:
							if self._mask_token is None:
													if self.verbose:
																			logger.error("""Using mask_token, but it is not set yet."""     )
													return None
							return str(self._mask_token     )




	@mask_token.setter
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]						,  __UpperCAmelCase	:						Tuple     )			-> Optional[int]:
							UpperCAmelCase_=   AddedToken(__UpperCAmelCase						,  lstrip=__UpperCAmelCase						,  rstrip=__UpperCAmelCase     ) if isinstance(__UpperCAmelCase						,  __UpperCAmelCase     ) else value
							UpperCAmelCase_=   value




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  *__UpperCAmelCase	:						List[str]						,  **__UpperCAmelCase	:						Optional[int]     )			-> BatchEncoding:
							UpperCAmelCase_=   kwargs.get("""is_split_into_words"""						,  __UpperCAmelCase     )
							assert self.add_prefix_space or not is_split_into_words, (
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*__UpperCAmelCase						,  **__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  *__UpperCAmelCase	:						Dict						,  **__UpperCAmelCase	:						Optional[Any]     )			-> BatchEncoding:
							UpperCAmelCase_=   kwargs.get("""is_split_into_words"""						,  __UpperCAmelCase     )

							assert self.add_prefix_space or not is_split_into_words, (
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*__UpperCAmelCase						,  **__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						Optional[str] = None     )			-> Tuple[str]:
							UpperCAmelCase_=   self._tokenizer.model.save(__UpperCAmelCase						,  name=__UpperCAmelCase     )
							return tuple(__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						List[Any]=None     )			-> str:
							UpperCAmelCase_=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
													return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




	def   _SCREAMING_SNAKE_CASE		(	self	:						str						,  __UpperCAmelCase	:						List[int]						,  __UpperCAmelCase	:						Optional[List[int]] = None     )			-> List[int]:
							UpperCAmelCase_=   [self.sep_token_id]
							UpperCAmelCase_=   [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]









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				    =						logging.get_logger(__name__)

__A				    =						{
    '''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 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					Dict		   =							"layoutlm"




	def __init__(	self	:						List[Any]						,  __UpperCAmelCase	:						int=30_522						,  __UpperCAmelCase	:						List[Any]=768						,  __UpperCAmelCase	:						Optional[int]=12						,  __UpperCAmelCase	:						List[str]=12						,  __UpperCAmelCase	:						int=3_072						,  __UpperCAmelCase	:						str="gelu"						,  __UpperCAmelCase	:						str=0.1						,  __UpperCAmelCase	:						Any=0.1						,  __UpperCAmelCase	:						Union[str, Any]=512						,  __UpperCAmelCase	:						Any=2						,  __UpperCAmelCase	:						Any=0.02						,  __UpperCAmelCase	:						Dict=1E-12						,  __UpperCAmelCase	:						Optional[int]=0						,  __UpperCAmelCase	:						List[str]="absolute"						,  __UpperCAmelCase	:						List[Any]=True						,  __UpperCAmelCase	:						Optional[int]=1_024						,  **__UpperCAmelCase	:						int						,  )			-> Union[str, Any]:
							super().__init__(pad_token_id=__UpperCAmelCase						,  **__UpperCAmelCase     )
							UpperCAmelCase_=   vocab_size
							UpperCAmelCase_=   hidden_size
							UpperCAmelCase_=   num_hidden_layers
							UpperCAmelCase_=   num_attention_heads
							UpperCAmelCase_=   hidden_act
							UpperCAmelCase_=   intermediate_size
							UpperCAmelCase_=   hidden_dropout_prob
							UpperCAmelCase_=   attention_probs_dropout_prob
							UpperCAmelCase_=   max_position_embeddings
							UpperCAmelCase_=   type_vocab_size
							UpperCAmelCase_=   initializer_range
							UpperCAmelCase_=   layer_norm_eps
							UpperCAmelCase_=   position_embedding_type
							UpperCAmelCase_=   use_cache
							UpperCAmelCase_=   max_ad_position_embeddings






class 		lowercase						( snake_case__):






	"""simple docstring"""


	def __init__(	self	:						List[str]						,  __UpperCAmelCase	:						PretrainedConfig						,  __UpperCAmelCase	:						str = "default"						,  __UpperCAmelCase	:						List[PatchingSpec] = None						,  )			-> str:
							super().__init__(__UpperCAmelCase						,  task=__UpperCAmelCase						,  patching_specs=__UpperCAmelCase     )
							UpperCAmelCase_=   config.max_ad_position_embeddings - 1




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> 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   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						PreTrainedTokenizer						,  __UpperCAmelCase	:						int = -1						,  __UpperCAmelCase	:						int = -1						,  __UpperCAmelCase	:						bool = False						,  __UpperCAmelCase	:						Optional[TensorType] = None						,  )			-> Mapping[str, Any]:
							UpperCAmelCase_=   super().generate_dummy_inputs(
							    __UpperCAmelCase						,  batch_size=__UpperCAmelCase						,  seq_length=__UpperCAmelCase						,  is_pair=__UpperCAmelCase						,  framework=__UpperCAmelCase     )

							# Generate a dummy bbox
							UpperCAmelCase_=   [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

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




