



import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    LayoutLMForMaskedLM,
							    LayoutLMForQuestionAnswering,
							    LayoutLMForSequenceClassification,
							    LayoutLMForTokenClassification,
							    LayoutLMModel,
							)
class      A__       :





							def __init__(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       int=13					,			_UpperCAmelCase  :       Union[str, Any]=7					,			_UpperCAmelCase  :       Dict=True					,			_UpperCAmelCase  :       Any=True					,			_UpperCAmelCase  :       Optional[int]=True					,			_UpperCAmelCase  :       str=True					,			_UpperCAmelCase  :       Any=99					,			_UpperCAmelCase  :       str=32					,			_UpperCAmelCase  :       Optional[int]=5					,			_UpperCAmelCase  :       str=4					,			_UpperCAmelCase  :       str=37					,			_UpperCAmelCase  :       Dict="gelu"					,			_UpperCAmelCase  :       int=0.1					,			_UpperCAmelCase  :       int=0.1					,			_UpperCAmelCase  :       Any=5_12					,			_UpperCAmelCase  :       List[str]=16					,			_UpperCAmelCase  :       Dict=2					,			_UpperCAmelCase  :       List[str]=0.02					,			_UpperCAmelCase  :       int=3					,			_UpperCAmelCase  :       List[str]=4					,			_UpperCAmelCase  :       List[Any]=None					,			_UpperCAmelCase  :       int=10_00					,			)						->		str:



											"""simple docstring"""


											__lowercase								= parent
											__lowercase								= batch_size
											__lowercase								= seq_length
											__lowercase								= is_training
											__lowercase								= use_input_mask
											__lowercase								= use_token_type_ids
											__lowercase								= use_labels
											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= intermediate_size
											__lowercase								= hidden_act
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= type_sequence_label_size
											__lowercase								= initializer_range
											__lowercase								= num_labels
											__lowercase								= num_choices
											__lowercase								= scope
											__lowercase								= range_bbox





							def       a__			(			self  :       Optional[int]    )						->		Dict:



											"""simple docstring"""


											__lowercase								= ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size    )

											__lowercase								= ids_tensor([self.batch_size, self.seq_length, 4]					,			self.range_bbox    )
											# Ensure that bbox is legal
											for i in range(bbox.shape[0]    ):
															for j in range(bbox.shape[1]    ):
																			if bbox[i, j, 3] < bbox[i, j, 1]:
																							__lowercase								= bbox[i, j, 3]
																							__lowercase								= bbox[i, j, 1]
																							__lowercase								= t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																							__lowercase								= bbox[i, j, 2]
																							__lowercase								= bbox[i, j, 0]
																							__lowercase								= t

											__lowercase								= None
											if self.use_input_mask:
															__lowercase								= ids_tensor([self.batch_size, self.seq_length]					,			vocab_size=2    )

											__lowercase								= None
											if self.use_token_type_ids:
															__lowercase								= ids_tensor([self.batch_size, self.seq_length]					,			self.type_vocab_size    )

											__lowercase								= None
											__lowercase								= None
											__lowercase								= None
											if self.use_labels:
															__lowercase								= ids_tensor([self.batch_size]					,			self.type_sequence_label_size    )
															__lowercase								= ids_tensor([self.batch_size, self.seq_length]					,			self.num_labels    )
															__lowercase								= ids_tensor([self.batch_size]					,			self.num_choices    )

											__lowercase								= self.get_config()

											return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





							def       a__			(			self  :       List[Any]    )						->		Tuple:



											"""simple docstring"""


											return LayoutLMConfig(
											    vocab_size=self.vocab_size					,			hidden_size=self.hidden_size					,			num_hidden_layers=self.num_hidden_layers					,			num_attention_heads=self.num_attention_heads					,			intermediate_size=self.intermediate_size					,			hidden_act=self.hidden_act					,			hidden_dropout_prob=self.hidden_dropout_prob					,			attention_probs_dropout_prob=self.attention_probs_dropout_prob					,			max_position_embeddings=self.max_position_embeddings					,			type_vocab_size=self.type_vocab_size					,			initializer_range=self.initializer_range					,			)





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Union[str, Any]					,			_UpperCAmelCase  :       int    )						->		List[str]:



											"""simple docstring"""


											__lowercase								= LayoutLMModel(config=_A    )
											model.to(_A    )
											model.eval()
											__lowercase								= model(_A					,			_A					,			attention_mask=_A					,			token_type_ids=_A    )
											__lowercase								= model(_A					,			_A					,			token_type_ids=_A    )
											__lowercase								= model(_A					,			_A    )
											self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)    )
											self.parent.assertEqual(result.pooler_output.shape					,			(self.batch_size, self.hidden_size)    )





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Optional[int]    )						->		List[str]:



											"""simple docstring"""


											__lowercase								= LayoutLMForMaskedLM(config=_A    )
											model.to(_A    )
											model.eval()
											__lowercase								= model(_A					,			_A					,			attention_mask=_A					,			token_type_ids=_A					,			labels=_A    )
											self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.vocab_size)    )





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       Tuple					,			_UpperCAmelCase  :       Dict					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Union[str, Any]    )						->		str:



											"""simple docstring"""


											__lowercase								= self.num_labels
											__lowercase								= LayoutLMForSequenceClassification(_A    )
											model.to(_A    )
											model.eval()
											__lowercase								= model(
											    _A					,			_A					,			attention_mask=_A					,			token_type_ids=_A					,			labels=_A    )
											self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Union[str, Any]					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       Union[str, Any]					,			_UpperCAmelCase  :       Dict    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								= self.num_labels
											__lowercase								= LayoutLMForTokenClassification(config=_A    )
											model.to(_A    )
											model.eval()
											__lowercase								= model(_A					,			_A					,			attention_mask=_A					,			token_type_ids=_A					,			labels=_A    )
											self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.num_labels)    )





							def       a__			(			self  :       Optional[Any]					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       int    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= LayoutLMForQuestionAnswering(config=_A    )
											model.to(_A    )
											model.eval()
											__lowercase								= model(
											    _A					,			bbox=_A					,			attention_mask=_A					,			token_type_ids=_A					,			start_positions=_A					,			end_positions=_A					,			)
											self.parent.assertEqual(result.start_logits.shape					,			(self.batch_size, self.seq_length)    )
											self.parent.assertEqual(result.end_logits.shape					,			(self.batch_size, self.seq_length)    )





							def       a__			(			self  :       int    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= self.prepare_config_and_inputs()
											(
											    __lowercase    
											)								= config_and_inputs
											__lowercase								= {
											    'input_ids': input_ids,
											    'bbox': bbox,
											    'token_type_ids': token_type_ids,
											    'attention_mask': input_mask,
											}
											return config, inputs_dict






@require_torch
class      A__       (						snake_case__   ,						snake_case__   ,						unittest.TestCase    ):
							lowerCAmelCase__		:						Optional[int]         =							(
							    (
							        LayoutLMModel,
							        LayoutLMForMaskedLM,
							        LayoutLMForSequenceClassification,
							        LayoutLMForTokenClassification,
							        LayoutLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							lowerCAmelCase__		:						Tuple         =							(
							    {
							        '''document-question-answering''': LayoutLMForQuestionAnswering,
							        '''feature-extraction''': LayoutLMModel,
							        '''fill-mask''': LayoutLMForMaskedLM,
							        '''text-classification''': LayoutLMForSequenceClassification,
							        '''token-classification''': LayoutLMForTokenClassification,
							        '''zero-shot''': LayoutLMForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCAmelCase__		:						List[str]         =							True





							def       a__			(			self  :       Optional[int]    )						->		Any:



											"""simple docstring"""


											__lowercase								= LayoutLMModelTester(self    )
											__lowercase								= ConfigTester(self					,			config_class=_A					,			hidden_size=37    )





							def       a__			(			self  :       Union[str, Any]    )						->		Any:



											"""simple docstring"""


											self.config_tester.run_common_tests()





							def       a__			(			self  :       int    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A    )





							def       a__			(			self  :       Any    )						->		str:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
															__lowercase								= type
															self.model_tester.create_and_check_model(*_A    )





							def       a__			(			self  :       Optional[Any]    )						->		List[str]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A    )





							def       a__			(			self  :       Dict    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A    )





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A    )





							def       a__			(			self  :       Optional[Any]    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A    )





							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
							def       a__			(			self  :       int    )						->		Tuple:



											"""simple docstring"""


											pass






def 							__SCREAMING_SNAKE_CASE       (		)					->						Optional[int]:
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				__lowercase								= torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]   ,							device=SCREAMING_SNAKE_CASE      )  # noqa: E231
				__lowercase								= torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]   ,							device=SCREAMING_SNAKE_CASE      )  # noqa: E231
				__lowercase								= torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]   ,							device=SCREAMING_SNAKE_CASE      )  # noqa: E231
				__lowercase								= torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]   ,							device=SCREAMING_SNAKE_CASE      )  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				__lowercase								= torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]   ,							device=SCREAMING_SNAKE_CASE      )  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels
@require_torch
class      A__       (						unittest.TestCase    ):





							@slow
							def       a__			(			self  :       List[Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(_A    )

											__lowercase								= prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase								= model(input_ids=_A					,			bbox=_A					,			attention_mask=_A					,			token_type_ids=_A    )

											# test the sequence output on [0, :3, :3]
											__lowercase								= torch.tensor(
											    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]					,			device=_A					,			)

											self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,			_A					,			atol=1e-3    )    )

											# test the pooled output on [1, :3]
											__lowercase								= torch.tensor([-0.6_580, -0.0_214, 0.8_552]					,			device=_A    )

											self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]					,			_A					,			atol=1e-3    )    )





							@slow
							def       a__			(			self  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'					,			num_labels=2    ).to(
											    _A    )

											__lowercase								= prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase								= model(
											    input_ids=_A					,			bbox=_A					,			attention_mask=_A					,			token_type_ids=_A					,			labels=torch.tensor([1, 1]					,			device=_A    )					,			)

											# test whether we get a loss as a scalar
											__lowercase								= outputs.loss
											__lowercase								= torch.Size([]    )
											self.assertEqual(loss.shape					,			_A    )

											# test the shape of the logits
											__lowercase								= outputs.logits
											__lowercase								= torch.Size((2, 2)    )
											self.assertEqual(logits.shape					,			_A    )





							@slow
							def       a__			(			self  :       Optional[Any]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'					,			num_labels=13    ).to(
											    _A    )

											__lowercase								= prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase								= model(
											    input_ids=_A					,			bbox=_A					,			attention_mask=_A					,			token_type_ids=_A					,			labels=_A    )

											# test the loss calculation to be around 2.65
											# expected_loss = torch.tensor(2.65, device=torch_device)

											# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
											# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

											# test the shape of the logits
											__lowercase								= outputs.logits
											__lowercase								= torch.Size((2, 25, 13)    )
											self.assertEqual(logits.shape					,			_A    )





							@slow
							def       a__			(			self  :       Any    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(_A    )

											__lowercase								= prepare_layoutlm_batch_inputs()

											# forward pass
											__lowercase								= model(input_ids=_A					,			bbox=_A					,			attention_mask=_A					,			token_type_ids=_A    )

											# test the shape of the logits
											__lowercase								= torch.Size((2, 25)    )
											self.assertEqual(outputs.start_logits.shape					,			_A    )
											self.assertEqual(outputs.end_logits.shape					,			_A    )







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							SCREAMING_SNAKE_CASE__							=    None


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__							=    {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__							=    {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						List[str]         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						List[Any]         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Dict         =							["input_ids", "attention_mask"]
							lowerCAmelCase__		:						List[str]         =							XLMRobertaTokenizer





							def __init__(			self  :       Tuple					,			_UpperCAmelCase  :       Tuple=None					,			_UpperCAmelCase  :       int=None					,			_UpperCAmelCase  :       Tuple="<s>"					,			_UpperCAmelCase  :       Dict="</s>"					,			_UpperCAmelCase  :       Dict="</s>"					,			_UpperCAmelCase  :       List[Any]="<s>"					,			_UpperCAmelCase  :       List[Any]="<unk>"					,			_UpperCAmelCase  :       Optional[int]="<pad>"					,			_UpperCAmelCase  :       Union[str, Any]="<mask>"					,			**_UpperCAmelCase  :       Optional[int]					,			)						->		Dict:



											"""simple docstring"""


											__lowercase								= AddedToken(_UpperCAmelCase					,			lstrip=_UpperCAmelCase					,			rstrip=_UpperCAmelCase    ) if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ) else mask_token

											super().__init__(
											    _UpperCAmelCase					,			tokenizer_file=_UpperCAmelCase					,			bos_token=_UpperCAmelCase					,			eos_token=_UpperCAmelCase					,			sep_token=_UpperCAmelCase					,			cls_token=_UpperCAmelCase					,			unk_token=_UpperCAmelCase					,			pad_token=_UpperCAmelCase					,			mask_token=_UpperCAmelCase					,			**_UpperCAmelCase					,			)

											__lowercase								= vocab_file
											__lowercase								= False if not self.vocab_file else True





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowercase								= [self.cls_token_id]
											__lowercase								= [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											__lowercase								= [self.sep_token_id]
											__lowercase								= [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]





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Optional[str] = None    )						->		Tuple[str]:



											"""simple docstring"""


											if not self.can_save_slow_tokenizer:
															raise ValueError(
															    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
															    'tokenizer.'    )

											if not os.path.isdir(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
															return
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(_UpperCAmelCase    ):
															copyfile(self.vocab_file					,			_UpperCAmelCase    )

											return (out_vocab_file,)





