
import unittest

from transformers import AutoTokenizer, NystromformerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerModel,
		)
		from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class       __A:

					def __init__(	self					:     List[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Dict=1_3   ,					__UpperCamelCase					:     List[str]=7   ,					__UpperCamelCase					:     Union[str, Any]=True   ,					__UpperCamelCase					:     Tuple=True   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     List[Any]=9_9   ,					__UpperCamelCase					:     str=3_2   ,					__UpperCamelCase					:     Union[str, Any]=5   ,					__UpperCamelCase					:     Optional[int]=4   ,					__UpperCamelCase					:     Tuple=3_7   ,					__UpperCamelCase					:     str="gelu"   ,					__UpperCamelCase					:     Optional[Any]=0.1   ,					__UpperCamelCase					:     Any=0.1   ,					__UpperCamelCase					:     Optional[int]=5_1_2   ,					__UpperCamelCase					:     int=1_6   ,					__UpperCamelCase					:     str=2   ,					__UpperCamelCase					:     int=0.02   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=4   ,					__UpperCamelCase					:     Any=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					seq_length
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_input_mask
												lowerCamelCase_							  =					use_token_type_ids
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					type_vocab_size
												lowerCamelCase_							  =					type_sequence_label_size
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					num_choices
												lowerCamelCase_							  =					scope

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

												lowerCamelCase_							  =					None
												if self.use_input_mask:
																			lowerCamelCase_							  =					random_attention_mask([self.batch_size, self.seq_length]   )

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

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

												lowerCamelCase_							  =					self.get_config()

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

					def    lowercase__      (	self					:     int   ):
												return NystromformerConfig(
												    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   ,					is_decoder=__UpperCamelCase   ,					initializer_range=self.initializer_range   ,					)

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ):
												lowerCamelCase_							  =					NystromformerModel(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					NystromformerForMaskedLM(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)   )

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					NystromformerForQuestionAnswering(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(
												    __UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					start_positions=__UpperCamelCase   ,					end_positions=__UpperCamelCase   ,					)
												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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					NystromformerForSequenceClassification(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					NystromformerForTokenClassification(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.num_labels)   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					self.num_choices
												lowerCamelCase_							  =					NystromformerForMultipleChoice(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					input_ids.unsqueeze(1   ).expand(-1   ,					self.num_choices   ,					-1   ).contiguous()
												lowerCamelCase_							  =					token_type_ids.unsqueeze(1   ).expand(-1   ,					self.num_choices   ,					-1   ).contiguous()
												lowerCamelCase_							  =					input_mask.unsqueeze(1   ).expand(-1   ,					self.num_choices   ,					-1   ).contiguous()
												lowerCamelCase_							  =					model(
												    __UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					labels=__UpperCamelCase   ,					)
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_choices)   )



					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												(
												    (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   
												)							  =					config_and_inputs
												lowerCamelCase_							  =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
												return config, inputs_dict





@require_torch
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (
					    (
					        NystromformerModel,
					        NystromformerForMaskedLM,
					        NystromformerForMultipleChoice,
					        NystromformerForQuestionAnswering,
					        NystromformerForSequenceClassification,
					        NystromformerForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE			    =    (
					    {
					        '''feature-extraction''': NystromformerModel,
					        '''fill-mask''': NystromformerForMaskedLM,
					        '''question-answering''': NystromformerForQuestionAnswering,
					        '''text-classification''': NystromformerForSequenceClassification,
					        '''token-classification''': NystromformerForTokenClassification,
					        '''zero-shot''': NystromformerForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					NystromformerModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					hidden_size=3_7   )

					def    lowercase__      (	self					:     Tuple   ):
												self.config_tester.run_common_tests()

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			lowerCamelCase_							  =					type
																			self.model_tester.create_and_check_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     Optional[Any]   ):
												for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					NystromformerModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )





@require_torch
class       __A(				unittest.TestCase       ):

					@slow
					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""   )
												lowerCamelCase_							  =					torch.tensor([[0, 1, 2, 3, 4, 5]]   )

												with torch.no_grad():
																			lowerCamelCase_							  =					model(__UpperCamelCase   )[0]

												lowerCamelCase_							  =					torch.Size((1, 6, 7_6_8)   )
												self.assertEqual(output.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					torch.tensor(
												    [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]   )

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



					@slow
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""the [MASK] of Belgium is Brussels"""

												lowerCamelCase_							  =					AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""   )
												lowerCamelCase_							  =					NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""   )

												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ,					return_tensors="""pt"""   )

												with torch.no_grad():
																			lowerCamelCase_							  =					model(encoding.input_ids   ).logits

												lowerCamelCase_							  =					token_logits[:, 2, :].argmax(-1   )[0]

												self.assertEqual(tokenizer.decode(__UpperCamelCase   )   ,					"""capital"""   )







import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt'''}

lowercase        =  {
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowercase        =  {
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]       )  ->    Dict:
							lowerCamelCase_							  =					collections.OrderedDict()
							with open(UpperCAmelCase__			,				"""r"""			,				encoding="""utf-8"""       ) as reader:
														lowerCamelCase_							  =					reader.readlines()
							for index, token in enumerate(UpperCAmelCase__       ):
														lowerCamelCase_							  =					token.rstrip("""\n"""       )
														lowerCamelCase_							  =					index
							return vocab





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[str]="<unk>"   ,					__UpperCamelCase					:     List[str]=2_0_0   ):
												lowerCamelCase_							  =					vocab
												lowerCamelCase_							  =					unk_token
												lowerCamelCase_							  =					max_input_chars_per_word



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												if len(__UpperCamelCase   ) > self.max_input_chars_per_word:
																			return [self.unk_token]

												lowerCamelCase_							  =					0
												lowerCamelCase_							  =					[]
												while start < len(__UpperCamelCase   ):
																			lowerCamelCase_							  =					len(__UpperCamelCase   )
																			lowerCamelCase_							  =					None
																			while start < end:
																										lowerCamelCase_							  =					"""""".join(chars[start:end]   )
																										if substr in self.vocab:
																																	lowerCamelCase_							  =					substr
																																	break
																										end -= 1
																			if cur_substr is None:
																										sub_tokens.append(self.unk_token   )
																										start += 1
																			else:
																										sub_tokens.append(__UpperCamelCase   )
																										lowerCamelCase_							  =					end

												return sub_tokens





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE			    =    False

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]="<d>"   ,					__UpperCamelCase					:     List[str]="</d>"   ,					__UpperCamelCase					:     Optional[Any]="<s>"   ,					__UpperCamelCase					:     Union[str, Any]="</s>"   ,					__UpperCamelCase					:     List[str]="<pad>"   ,					__UpperCamelCase					:     Union[str, Any]="<unk>"   ,					__UpperCamelCase					:     List[Any]="</n>"   ,					__UpperCamelCase					:     Tuple="</_>"   ,					__UpperCamelCase					:     Optional[Any]="left"   ,					**__UpperCamelCase					:     List[str]   ,					):
												requires_backends(self   ,					["""jieba"""]   )
												super().__init__(
												    bod_token=__UpperCamelCase   ,					eod_token=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					line_token=__UpperCamelCase   ,					space_token=__UpperCamelCase   ,					padding_side=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												lowerCamelCase_							  =					bod_token
												lowerCamelCase_							  =					eod_token
												lowerCamelCase_							  =					load_vocab(__UpperCamelCase   )
												lowerCamelCase_							  =					self.encoder[space_token]
												lowerCamelCase_							  =					self.encoder[line_token]

												del self.encoder[space_token]
												del self.encoder[line_token]

												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												lowerCamelCase_							  =					{v: k for k, v in self.encoder.items()}

												lowerCamelCase_							  =					WordpieceTokenizer(vocab=self.encoder   ,					unk_token=self.unk_token   )

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.bod_token]

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.eod_token]

					@property
					def    lowercase__      (	self					:     int   ):
												return self.encoder["\n"]

					@property
					def    lowercase__      (	self					:     str   ):
												return len(self.encoder   )

					def    lowercase__      (	self					:     Any   ):
												return dict(self.encoder   ,					**self.added_tokens_encoder   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					[]
												for x in jieba.cut(__UpperCamelCase   ,					cut_all=__UpperCamelCase   ):
																			output_tokens.extend(self.wordpiece_tokenizer.tokenize(__UpperCamelCase   )   )
												return output_tokens

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ,					**__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					[i for i in token_ids if i >= 0]
												lowerCamelCase_							  =					[
												    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
												]
												return super()._decode(__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ):
												return token in self.encoder

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[str]   ):
												return "".join(__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     int   ):
												return self.encoder.get(__UpperCamelCase   ,					self.encoder.get(self.unk_token   )   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ):
												return self.decoder.get(__UpperCamelCase   ,					self.unk_token   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if os.path.isdir(__UpperCamelCase   ):
																			lowerCamelCase_							  =					os.path.join(
																			    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
												else:
																			lowerCamelCase_							  =					(filename_prefix + """-""" if filename_prefix else """""") + save_directory
												lowerCamelCase_							  =					0
												if " " in self.encoder:
																			lowerCamelCase_							  =					self.encoder[""" """]
																			del self.encoder[" "]
												if "\n" in self.encoder:
																			lowerCamelCase_							  =					self.encoder["""\n"""]
																			del self.encoder["\n"]
												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												with open(__UpperCamelCase   ,					"""w"""   ,					encoding="""utf-8"""   ) as writer:
																			for token, token_index in self.encoder.items():
																										if index != token_index:
																																	logger.warning(
																																	    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																																	    """ Please check that the vocabulary is not corrupted!"""   )
																																	lowerCamelCase_							  =					token_index
																										writer.write(token + """\n"""   )
																										index += 1
												return (vocab_file,)

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     List[int] = None   ):
												if token_ids_a is None:
																			return [self.bos_token_id] + token_ids_a
												return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ,					__UpperCamelCase					:     bool = False   ):

												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCamelCase   ,					token_ids_a=__UpperCamelCase   ,					already_has_special_tokens=__UpperCamelCase   )

												if token_ids_a is not None:
																			return [1] + ([0] * len(__UpperCamelCase   )) + [1] + ([0] * len(__UpperCamelCase   ))
												return [1] + ([0] * len(__UpperCamelCase   ))





