






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


__lowerCamelCase						= logging.get_logger(__name__)

__lowerCamelCase						= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						= {
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase						= {
    'microsoft/layoutlm-base-uncased': 512,
    'microsoft/layoutlm-large-uncased': 512,
}

__lowerCamelCase						= {
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  VOCAB_FILES_NAMES
						lowercase									=  PRETRAINED_VOCAB_FILES_MAP
						lowercase									=  PRETRAINED_INIT_CONFIGURATION
						lowercase									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase									=  LayoutLMTokenizer




						def __init__(    self		,	lowercase=None		,	lowercase=None		,	lowercase=True		,	lowercase="[UNK]"		,	lowercase="[SEP]"		,	lowercase="[PAD]"		,	lowercase="[CLS]"		,	lowercase="[MASK]"		,	lowercase=True		,	lowercase=None		,	**lowercase		,	)			->						Union[str, Any]:
													super().__init__(
													    _lowerCamelCase		,	tokenizer_file=_lowerCamelCase		,	do_lower_case=_lowerCamelCase		,	unk_token=_lowerCamelCase		,	sep_token=_lowerCamelCase		,	pad_token=_lowerCamelCase		,	cls_token=_lowerCamelCase		,	mask_token=_lowerCamelCase		,	tokenize_chinese_chars=_lowerCamelCase		,	strip_accents=_lowerCamelCase		,	**_lowerCamelCase		,	)

													_a  :    Tuple			=						json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
													if (
													    normalizer_state.get('''lowercase'''		,	_lowerCamelCase       ) != do_lower_case
													    or normalizer_state.get('''strip_accents'''		,	_lowerCamelCase       ) != strip_accents
													    or normalizer_state.get('''handle_chinese_chars'''		,	_lowerCamelCase       ) != tokenize_chinese_chars
													):
																				_a  :    Tuple			=						getattr(_lowerCamelCase		,	normalizer_state.pop('''type'''       )       )
																				_a  :    int			=						do_lower_case
																				_a  :    Union[str, Any]			=						strip_accents
																				_a  :    Dict			=						tokenize_chinese_chars
																				_a  :    Optional[int]			=						normalizer_class(**_lowerCamelCase       )

													_a  :    str			=						do_lower_case




						def      snake_case__(    self		,	lowercase		,	lowercase=None       )			->						str:
													_a  :    Dict			=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

													if token_ids_a:
																				output += token_ids_a + [self.sep_token_id]

													return output




						def      snake_case__(    self		,	lowercase		,	lowercase = None       )			->						Tuple:
													_a  :    Union[str, Any]			=						[self.sep_token_id]
													_a  :    int			=						[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       ) * [0] + len(token_ids_a + sep       ) * [1]







						def      snake_case__(    self		,	lowercase		,	lowercase = None       )			->						Dict:
													_a  :    Tuple			=						self._tokenizer.model.save(_lowerCamelCase		,	name=_lowerCamelCase       )
													return tuple(_lowerCamelCase       )

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




