
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							UpperCAmelCase__					:	Union[str, Any]							       =					'''platform'''
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class       UpperCamelCase_      :




							'''simple docstring'''

							UpperCamelCase_        =				PegasusConfig
							UpperCamelCase_        =				{}
							UpperCamelCase_        =				"""gelu"""
							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase=13  ,    UpperCamelCase=7  ,    UpperCamelCase=True  ,    UpperCamelCase=False  ,    UpperCamelCase=99  ,    UpperCamelCase=32  ,    UpperCamelCase=5  ,    UpperCamelCase=4  ,    UpperCamelCase=37  ,    UpperCamelCase=0.1  ,    UpperCamelCase=0.1  ,    UpperCamelCase=20  ,    UpperCamelCase=2  ,    UpperCamelCase=1  ,    UpperCamelCase=0  ,    )			->						Union[str, Any]:
										UpperCamelCase__     :		Dict	 =					parent
										UpperCamelCase__     :		Dict	 =					batch_size
										UpperCamelCase__     :		Optional[int]	 =					seq_length
										UpperCamelCase__     :		int	 =					is_training
										UpperCamelCase__     :		List[str]	 =					use_labels
										UpperCamelCase__     :		List[Any]	 =					vocab_size
										UpperCamelCase__     :		List[Any]	 =					hidden_size
										UpperCamelCase__     :		Tuple	 =					num_hidden_layers
										UpperCamelCase__     :		Tuple	 =					num_attention_heads
										UpperCamelCase__     :		List[Any]	 =					intermediate_size

										UpperCamelCase__     :		Union[str, Any]	 =					hidden_dropout_prob
										UpperCamelCase__     :		Optional[Any]	 =					attention_probs_dropout_prob
										UpperCamelCase__     :		List[Any]	 =					max_position_embeddings
										UpperCamelCase__     :		Optional[int]	 =					eos_token_id
										UpperCamelCase__     :		List[str]	 =					pad_token_id
										UpperCamelCase__     :		List[str]	 =					bos_token_id
							def 							lowerCAmelCase__			(					self)			->						Tuple:
										UpperCamelCase__     :		Any	 =					ids_tensor([self.batch_size, self.seq_length - 1]  ,    self.vocab_size).clip(3  ,    self.vocab_size)
										UpperCamelCase__     :		str	 =					np.expand_dims(np.array([self.eos_token_id] * self.batch_size)  ,    1)
										UpperCamelCase__     :		Tuple	 =					np.concatenate([input_ids, eos_tensor]  ,    axis=1)

										UpperCamelCase__     :		Union[str, Any]	 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size)

										UpperCamelCase__     :		Union[str, Any]	 =					self.config_cls(
										    vocab_size=self.vocab_size  ,    d_model=self.hidden_size  ,    encoder_layers=self.num_hidden_layers  ,    decoder_layers=self.num_hidden_layers  ,    encoder_attention_heads=self.num_attention_heads  ,    decoder_attention_heads=self.num_attention_heads  ,    encoder_ffn_dim=self.intermediate_size  ,    decoder_ffn_dim=self.intermediate_size  ,    dropout=self.hidden_dropout_prob  ,    attention_dropout=self.attention_probs_dropout_prob  ,    max_position_embeddings=self.max_position_embeddings  ,    eos_token_ids=[2]  ,    bos_token_id=self.bos_token_id  ,    pad_token_id=self.pad_token_id  ,    decoder_start_token_id=self.pad_token_id  ,    **self.config_updates  ,    )
										UpperCamelCase__     :		Tuple	 =					prepare_pegasus_inputs_dict(UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase)
										return config, inputs_dict
							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase)			->						Dict:
										UpperCamelCase__     :		Optional[int]	 =					20
										UpperCamelCase__     :		int	 =					model_class_name(UpperCamelCase)

										UpperCamelCase__     :		Dict	 =					model.encode(inputs_dict['input_ids'])

										UpperCamelCase__ ,      UpperCamelCase__     :		List[str]	 =					(
										    inputs_dict['decoder_input_ids'],
										    inputs_dict['decoder_attention_mask'],
										)

										UpperCamelCase__     :		Optional[Any]	 =					model.init_cache(decoder_input_ids.shape[0]  ,    UpperCamelCase  ,    UpperCamelCase)
										UpperCamelCase__     :		Union[str, Any]	 =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,    dtype='i4')

										UpperCamelCase__     :		Union[str, Any]	 =					jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )
										UpperCamelCase__     :		List[Any]	 =					model.decode(
										    decoder_input_ids[:, :-1]  ,    UpperCamelCase  ,    decoder_attention_mask=UpperCamelCase  ,    past_key_values=UpperCamelCase  ,    decoder_position_ids=UpperCamelCase  ,    )

										UpperCamelCase__     :		Tuple	 =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,    dtype='i4')
										UpperCamelCase__     :		str	 =					model.decode(
										    decoder_input_ids[:, -1:]  ,    UpperCamelCase  ,    decoder_attention_mask=UpperCamelCase  ,    past_key_values=outputs_cache.past_key_values  ,    decoder_position_ids=UpperCamelCase  ,    )

										UpperCamelCase__     :		Union[str, Any]	 =					model.decode(UpperCamelCase  ,    UpperCamelCase)

										UpperCamelCase__     :		Union[str, Any]	 =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
										self.parent.assertTrue(diff < 1E-3  ,    msg=F"""Max diff is {diff}""")






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase)			->						Optional[int]:
										UpperCamelCase__     :		Optional[Any]	 =					20
										UpperCamelCase__     :		Any	 =					model_class_name(UpperCamelCase)

										UpperCamelCase__     :		Any	 =					model.encode(inputs_dict['input_ids'])

										UpperCamelCase__ ,      UpperCamelCase__     :		int	 =					(
										    inputs_dict['decoder_input_ids'],
										    inputs_dict['decoder_attention_mask'],
										)

										UpperCamelCase__     :		List[str]	 =					jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
										    ]  ,    axis=-1  ,    )

										UpperCamelCase__     :		Dict	 =					model.init_cache(decoder_input_ids.shape[0]  ,    UpperCamelCase  ,    UpperCamelCase)
										UpperCamelCase__     :		Union[str, Any]	 =					jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )

										UpperCamelCase__     :		Dict	 =					model.decode(
										    decoder_input_ids[:, :-1]  ,    UpperCamelCase  ,    decoder_attention_mask=UpperCamelCase  ,    past_key_values=UpperCamelCase  ,    decoder_position_ids=UpperCamelCase  ,    )
										UpperCamelCase__     :		Any	 =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,    dtype='i4')
										UpperCamelCase__     :		Any	 =					model.decode(
										    decoder_input_ids[:, -1:]  ,    UpperCamelCase  ,    past_key_values=outputs_cache.past_key_values  ,    decoder_attention_mask=UpperCamelCase  ,    decoder_position_ids=UpperCamelCase  ,    )

										UpperCamelCase__     :		Any	 =					model.decode(UpperCamelCase  ,    UpperCamelCase  ,    decoder_attention_mask=UpperCamelCase)

										UpperCamelCase__     :		Dict	 =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
										self.parent.assertTrue(diff < 1E-3  ,    msg=F"""Max diff is {diff}""")



def    _lowercase       (   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE=None					,     __SCREAMING_SNAKE_CASE=None					,     )						->  List[Any]:
			if attention_mask is None:
						UpperCamelCase__     :		Optional[Any]	 =					np.not_equal(lowerCamelCase__					,     config.pad_token_id			).astype(np.inta			)
			if decoder_attention_mask is None:
						UpperCamelCase__     :		Optional[Any]	 =					np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape					,     dtype=np.inta			),
						        np.not_equal(decoder_input_ids[:, 1:]					,     config.pad_token_id			).astype(np.inta			),
						    ]					,     axis=-1					,     )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}



@require_flax
class       UpperCamelCase_      (    snake_case_						,  unittest.TestCase ):




							'''simple docstring'''

							UpperCamelCase_        =				(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							UpperCamelCase_        =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							UpperCamelCase_        =				True
							UpperCamelCase_        =				False
							UpperCamelCase_        =				False
							UpperCamelCase_        =				False
							def 							lowerCAmelCase__			(					self)			->						str:
										UpperCamelCase__     :		str	 =					FlaxPegasusModelTester(self)
										UpperCamelCase__     :		str	 =					ConfigTester(self  ,    config_class=UpperCamelCase)
							def 							lowerCAmelCase__			(					self)			->						int:
										self.config_tester.run_common_tests()
							def 							lowerCAmelCase__			(					self)			->						Tuple:
										UpperCamelCase__ ,      UpperCamelCase__     :		List[Any]	 =					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase)
							def 							lowerCAmelCase__			(					self)			->						str:
										UpperCamelCase__ ,      UpperCamelCase__     :		str	 =					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase)
							def 							lowerCAmelCase__			(					self)			->						Optional[int]:
										UpperCamelCase__ ,      UpperCamelCase__     :		Dict	 =					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																UpperCamelCase__     :		str	 =					self._prepare_for_class(UpperCamelCase  ,    UpperCamelCase)
																UpperCamelCase__     :		Optional[Any]	 =					model_class(UpperCamelCase)

																@jax.jit
																def encode_jitted(UpperCamelCase  ,    UpperCamelCase=None  ,    **UpperCamelCase):
																			return model.encode(input_ids=UpperCamelCase  ,    attention_mask=UpperCamelCase)

																with self.subTest('JIT Enabled'):
																			UpperCamelCase__     :		List[Any]	 =					encode_jitted(**UpperCamelCase).to_tuple()

																with self.subTest('JIT Disabled'):
																			with jax.disable_jit():
																						UpperCamelCase__     :		Optional[int]	 =					encode_jitted(**UpperCamelCase).to_tuple()

																self.assertEqual(len(UpperCamelCase)  ,    len(UpperCamelCase))
																for jitted_output, output in zip(UpperCamelCase  ,    UpperCamelCase):
																			self.assertEqual(jitted_output.shape  ,    output.shape)
							def 							lowerCAmelCase__			(					self)			->						Any:
										UpperCamelCase__ ,      UpperCamelCase__     :		Tuple	 =					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																UpperCamelCase__     :		Optional[Any]	 =					model_class(UpperCamelCase)
																UpperCamelCase__     :		str	 =					model.encode(inputs_dict['input_ids']  ,    inputs_dict['attention_mask'])

																UpperCamelCase__     :		List[str]	 =					{
																    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																    'encoder_outputs': encoder_outputs,
																}

																@jax.jit
																def decode_jitted(UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase):
																			return model.decode(
																			    decoder_input_ids=UpperCamelCase  ,    decoder_attention_mask=UpperCamelCase  ,    encoder_outputs=UpperCamelCase  ,    )

																with self.subTest('JIT Enabled'):
																			UpperCamelCase__     :		Optional[int]	 =					decode_jitted(**UpperCamelCase).to_tuple()

																with self.subTest('JIT Disabled'):
																			with jax.disable_jit():
																						UpperCamelCase__     :		Optional[int]	 =					decode_jitted(**UpperCamelCase).to_tuple()

																self.assertEqual(len(UpperCamelCase)  ,    len(UpperCamelCase))
																for jitted_output, output in zip(UpperCamelCase  ,    UpperCamelCase):
																			self.assertEqual(jitted_output.shape  ,    output.shape)
							@slow
							def 							lowerCAmelCase__			(					self)			->						Union[str, Any]:
										for model_class_name in self.all_model_classes:
													UpperCamelCase__     :		List[Any]	 =					model_class_name.from_pretrained('google/pegasus-large'  ,    from_pt=UpperCamelCase)
													UpperCamelCase__     :		Tuple	 =					np.ones((1, 1))
													UpperCamelCase__     :		Optional[int]	 =					model(UpperCamelCase)
													self.assertIsNotNone(UpperCamelCase)






							@slow
							def 							lowerCAmelCase__			(					self)			->						Union[str, Any]:
										UpperCamelCase__     :		List[Any]	 =					FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum')
										UpperCamelCase__     :		Optional[Any]	 =					PegasusTokenizer.from_pretrained('google/pegasus-xsum')

										UpperCamelCase__     :		str	 =					[
										    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
										    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ',
										]

										UpperCamelCase__     :		Union[str, Any]	 =					[
										    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
										    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
										]

										UpperCamelCase__     :		Dict	 =					tokenizer(UpperCamelCase  ,    return_tensors='np'  ,    truncation=UpperCamelCase  ,    max_length=5_12  ,    padding=UpperCamelCase)
										UpperCamelCase__     :		Tuple	 =					model.generate(**UpperCamelCase  ,    num_beams=2).sequences
										UpperCamelCase__     :		Tuple	 =					tokenizer.batch_decode(UpperCamelCase  ,    skip_special_tokens=UpperCamelCase)
										assert tgt_text == decoded





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__A							=logging.get_logger(__name__)

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

__A							=[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

__A							={
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
__A							={F"""funnel-transformer/{name}""": 5_1_2 for name in _model_names}
__A							={F"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					VOCAB_FILES_NAMES
	lowerCAmelCase__   =					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__   =					PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__   =					FunnelTokenizer
	lowerCAmelCase__   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__   =					2



	def __init__(       self		,    lowercase=None		,    lowercase=None		,    lowercase=True		,    lowercase="<unk>"		,    lowercase="<sep>"		,    lowercase="<pad>"		,    lowercase="<cls>"		,    lowercase="<mask>"		,    lowercase="<s>"		,    lowercase="</s>"		,    lowercase=True		,    lowercase=True		,    lowercase=None		,    lowercase="##"		,    **lowercase		,    )							->			List[str]:
					super().__init__(
					    lowercase		,    tokenizer_file=lowercase		,    do_lower_case=lowercase		,    unk_token=lowercase		,    sep_token=lowercase		,    pad_token=lowercase		,    cls_token=lowercase		,    mask_token=lowercase		,    bos_token=lowercase		,    eos_token=lowercase		,    clean_text=lowercase		,    tokenize_chinese_chars=lowercase		,    strip_accents=lowercase		,    wordpieces_prefix=lowercase		,    **lowercase		,    )

					lowerCamelCase_         =     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
					if (
					    normalizer_state.get("lowercase"		,    lowercase					) != do_lower_case
					    or normalizer_state.get("strip_accents"		,    lowercase					) != strip_accents
					    or normalizer_state.get("handle_chinese_chars"		,    lowercase					) != tokenize_chinese_chars
					):
									lowerCamelCase_         =     getattr(lowercase		,    normalizer_state.pop("type"					)					)
									lowerCamelCase_         =     do_lower_case
									lowerCamelCase_         =     strip_accents
									lowerCamelCase_         =     tokenize_chinese_chars
									lowerCamelCase_         =     normalizer_class(**lowercase					)

					lowerCamelCase_         =     do_lower_case



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=None					)							->			str:
					lowerCamelCase_         =     [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        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			List[int]:
					lowerCamelCase_         =     [self.sep_token_id]
					lowerCamelCase_         =     [self.cls_token_id]
					if token_ids_a is None:
									return len(cls					) * [self.cls_token_type_id] + len(token_ids_a + sep					) * [0]
					return len(cls					) * [self.cls_token_type_id] + len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			Tuple[str]:
					lowerCamelCase_         =     self._tokenizer.model.save(lowercase		,    name=lowercase					)
					return tuple(lowercase					)


