




"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFLEDForConditionalGeneration, TFLEDModel

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

					__lowercase       :   str        =    hidden_dropout_prob
					__lowercase       :   List[str]        =    attention_probs_dropout_prob
					__lowercase       :   Optional[Any]        =    max_position_embeddings
					__lowercase       :   List[str]        =    eos_token_id
					__lowercase       :   str        =    pad_token_id
					__lowercase       :   Optional[int]        =    bos_token_id
					__lowercase       :   Dict        =    attention_window

					# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
					# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
					# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
					# because its local attention only attends to `self.attention_window` and one before and one after
					__lowercase       :   List[str]        =    self.attention_window + 2

					# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
					# the `test_attention_outputs` and `test_hidden_states_output` tests
					__lowercase       :   Optional[Any]        =    (
					    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
					)
		def      _lowerCamelCase				(		self    )		->    str:
					__lowercase       :   str        =    ids_tensor([self.batch_size, self.seq_length - 1]							,		self.vocab_size    )
					__lowercase       :   Optional[Any]        =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,		1    )
					__lowercase       :   Optional[int]        =    tf.concat([input_ids, eos_tensor]							,		axis=1    )

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

					__lowercase       :   Dict        =    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							,		attention_window=self.attention_window							,		**self.config_updates							,		)
					__lowercase       :   int        =    prepare_led_inputs_dict(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    tf.concat(
					    [tf.zeros_like(UpperCamelCase_    )[:, :-1], tf.ones_like(UpperCamelCase_    )[:, -1:]]							,		axis=-1							,		)
					__lowercase       :   str        =    global_attention_mask
					return config, inputs_dict
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Optional[Any]:
					__lowercase       :   Union[str, Any]        =    TFLEDModel(config=UpperCamelCase_    ).get_decoder()
					__lowercase       :   str        =    inputs_dict['''input_ids''']

					__lowercase       :   str        =    input_ids[:1, :]
					__lowercase       :   str        =    inputs_dict['''attention_mask'''][:1, :]
					__lowercase       :   Optional[int]        =    1

					# first forward pass
					__lowercase       :   Optional[Any]        =    model(UpperCamelCase_							,		attention_mask=UpperCamelCase_							,		use_cache=UpperCamelCase_    )

					__lowercase       :   Union[str, Any]        =    outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__lowercase       :   int        =    ids_tensor((self.batch_size, 3)							,		config.vocab_size    )
					__lowercase       :   Dict        =    tf.cast(ids_tensor((self.batch_size, 3)							,		2    )							,		tf.inta    )

					# append to next input_ids and
					__lowercase       :   Optional[Any]        =    tf.concat([input_ids, next_tokens]							,		axis=-1    )
					__lowercase       :   List[str]        =    tf.concat([attention_mask, next_attn_mask]							,		axis=-1    )

					__lowercase       :   Union[str, Any]        =    model(UpperCamelCase_							,		attention_mask=UpperCamelCase_    )[0]
					__lowercase       :   Optional[Any]        =    model(UpperCamelCase_							,		attention_mask=UpperCamelCase_							,		past_key_values=UpperCamelCase_    )[0]

					self.parent.assertEqual(next_tokens.shape[1]							,		output_from_past.shape[1]    )

					# select random slice
					__lowercase       :   Optional[Any]        =    int(ids_tensor((1,)							,		output_from_past.shape[-1]    )    )
					__lowercase       :   int        =    output_from_no_past[:, -3:, random_slice_idx]
					__lowercase       :   Any        =    output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(UpperCamelCase_							,		UpperCamelCase_							,		rtol=1E-3    )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							):
			if attention_mask is None:
						__lowercase       :   List[str]        =    tf.cast(tf.math.not_equal(__lowerCamelCase     ,							config.pad_token_id     )     ,							tf.inta     )
			if decoder_attention_mask is None:
						__lowercase       :   Tuple        =    tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape     ,							dtype=tf.inta     ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,							config.pad_token_id     )     ,							tf.inta     ),
						    ]     ,							axis=-1     ,							)
			if head_mask is None:
						__lowercase       :   Optional[Any]        =    tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						__lowercase       :   Any        =    tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			    "decoder_input_ids": decoder_input_ids,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			}

@require_tf
class    UpperCAmelCase_  (       lowerCAmelCase_					, lowerCAmelCase_					, unittest.TestCase						):
		UpperCamelCase						 =(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
		UpperCamelCase						 =(TFLEDForConditionalGeneration,) if is_tf_available() else ()
		UpperCamelCase						 =(
		    {
		        "conversational": TFLEDForConditionalGeneration,
		        "feature-extraction": TFLEDModel,
		        "summarization": TFLEDForConditionalGeneration,
		        "text2text-generation": TFLEDForConditionalGeneration,
		        "translation": TFLEDForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		UpperCamelCase						 =True
		UpperCamelCase						 =False
		UpperCamelCase						 =False
		UpperCamelCase						 =False
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   int        =    TFLEDModelTester(self    )
					__lowercase       :   Tuple        =    ConfigTester(self							,		config_class=UpperCamelCase_    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					self.config_tester.run_common_tests()
		def      _lowerCamelCase				(		self    )		->    Optional[int]:
					__lowercase       :   Dict        =    self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_    )
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					__lowercase       :   List[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()
					__lowercase       :   Dict        =    tf.zeros_like(inputs_dict['''attention_mask''']    )
					__lowercase       :   Optional[int]        =    2
					__lowercase       :   Optional[int]        =    tf.where(
					    tf.range(self.model_tester.seq_length    )[None, :] < num_global_attn_indices							,		1							,		inputs_dict['''global_attention_mask''']							,		)

					__lowercase       :   str        =    True
					__lowercase       :   int        =    self.model_tester.seq_length
					__lowercase       :   Tuple        =    self.model_tester.encoder_seq_length

					def check_decoder_attentions_output(UpperCamelCase_    ):
								__lowercase       :   int        =    outputs.decoder_attentions
								self.assertEqual(len(UpperCamelCase_    )							,		self.model_tester.num_hidden_layers    )
								self.assertListEqual(
								    list(decoder_attentions[0].shape[-3:]    )							,		[self.model_tester.num_attention_heads, seq_length, seq_length]							,		)

					def check_encoder_attentions_output(UpperCamelCase_    ):
								__lowercase       :   int        =    [t.numpy() for t in outputs.encoder_attentions]
								__lowercase       :   Union[str, Any]        =    [t.numpy() for t in outputs.encoder_global_attentions]
								self.assertEqual(len(UpperCamelCase_    )							,		self.model_tester.num_hidden_layers    )
								self.assertEqual(len(UpperCamelCase_    )							,		self.model_tester.num_hidden_layers    )
								self.assertListEqual(
								    list(attentions[0].shape[-3:]    )							,		[self.model_tester.num_attention_heads, seq_length, seq_length]							,		)
								self.assertListEqual(
								    list(global_attentions[0].shape[-3:]    )							,		[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]							,		)

					for model_class in self.all_model_classes:
								__lowercase       :   Union[str, Any]        =    True
								__lowercase       :   Dict        =    False
								__lowercase       :   Dict        =    False
								__lowercase       :   List[str]        =    model_class(UpperCamelCase_    )
								__lowercase       :   Optional[Any]        =    model(self._prepare_for_class(UpperCamelCase_							,		UpperCamelCase_    )    )
								__lowercase       :   Union[str, Any]        =    len(UpperCamelCase_    )
								self.assertEqual(config.output_hidden_states							,		UpperCamelCase_    )
								check_encoder_attentions_output(UpperCamelCase_    )

								if self.is_encoder_decoder:
											__lowercase       :   Any        =    model_class(UpperCamelCase_    )
											__lowercase       :   str        =    model(self._prepare_for_class(UpperCamelCase_							,		UpperCamelCase_    )    )
											self.assertEqual(config.output_hidden_states							,		UpperCamelCase_    )
											check_decoder_attentions_output(UpperCamelCase_    )

								# Check that output attentions can also be changed via the config
								del inputs_dict["output_attentions"]
								__lowercase       :   Optional[Any]        =    True
								__lowercase       :   Optional[int]        =    model_class(UpperCamelCase_    )
								__lowercase       :   List[Any]        =    model(self._prepare_for_class(UpperCamelCase_							,		UpperCamelCase_    )    )
								self.assertEqual(config.output_hidden_states							,		UpperCamelCase_    )
								check_encoder_attentions_output(UpperCamelCase_    )

								# Check attention is always last and order is fine
								__lowercase       :   List[Any]        =    True
								__lowercase       :   Tuple        =    True
								__lowercase       :   Optional[Any]        =    model_class(UpperCamelCase_    )
								__lowercase       :   List[Any]        =    model(self._prepare_for_class(UpperCamelCase_							,		UpperCamelCase_    )    )

								self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)							,		len(UpperCamelCase_    )    )
								self.assertEqual(model.config.output_hidden_states							,		UpperCamelCase_    )
								check_encoder_attentions_output(UpperCamelCase_    )
		@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''    )
		def      _lowerCamelCase				(		self    )		->    str:
					pass
		def      _lowerCamelCase				(		self    )		->    Tuple:
					# TODO: Head-masking not yet implement
					pass







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			return tf.constant(__lowerCamelCase     ,							dtype=tf.intaa     )


a_	   =    1e-4


@slow
@require_tf
class    UpperCAmelCase_  (       unittest.TestCase						):
		def      _lowerCamelCase				(		self    )		->    int:
					__lowercase       :   Tuple        =    TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''    ).led

					# change to intended input here
					__lowercase       :   Tuple        =    _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]    )
					__lowercase       :   Optional[Any]        =    _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]    )
					__lowercase       :   Optional[int]        =    prepare_led_inputs_dict(model.config							,		UpperCamelCase_							,		UpperCamelCase_    )
					__lowercase       :   List[str]        =    model(**UpperCamelCase_    )[0]
					__lowercase       :   List[Any]        =    (1, 10_24, 7_68)
					self.assertEqual(output.shape							,		UpperCamelCase_    )
					# change to expected output here
					__lowercase       :   List[Any]        =    tf.convert_to_tensor(
					    [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]							,		)
					tf.debugging.assert_near(output[:, :3, :3]							,		UpperCamelCase_							,		atol=1E-3    )
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   List[str]        =    TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''    )

					# change to intended input here
					__lowercase       :   str        =    _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]    )
					__lowercase       :   Tuple        =    _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]    )
					__lowercase       :   Dict        =    prepare_led_inputs_dict(model.config							,		UpperCamelCase_							,		UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    model(**UpperCamelCase_    )[0]
					__lowercase       :   List[Any]        =    (1, 10_24, model.config.vocab_size)
					self.assertEqual(output.shape							,		UpperCamelCase_    )
					# change to expected output here
					__lowercase       :   Tuple        =    tf.convert_to_tensor(
					    [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]							,		)
					tf.debugging.assert_near(output[:, :3, :3]							,		UpperCamelCase_							,		atol=1E-3							,		rtol=1E-3    )








"""simple docstring"""






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

import sentencepiece as spm

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


_lowercase : Optional[int]      						=						"▁"

_lowercase : Optional[Any]      						=						{"vocab_file": "spiece.model"}

_lowercase : Optional[Any]      						=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_lowercase : Tuple      						=						{
    "google/pegasus-xsum": 5_1_2,
}


_lowercase : Optional[int]      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES

					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any="<pad>",  lowerCamelCase							:  Optional[Any]="</s>",  lowerCamelCase							:  Any="<unk>",  lowerCamelCase							:  Tuple="<mask_2>",  lowerCamelCase							:  int="<mask_1>",  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Dict=103,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Optional[int],  )->					None:
								lowerCamelCase__		:		Union[str, Any]			=offset
								if additional_special_tokens is not None:
											if not isinstance(lowerCamelCase,  lowerCamelCase     ):
														raise TypeError(
														    F'''additional_special_tokens should be of type {type(lowerCamelCase     )}, but is'''
														    F''' {type(lowerCamelCase     )}'''     )

											lowerCamelCase__		:		Any			=(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    F'''<unk_{i}>''' for i in range(len(lowerCamelCase     ),  self.offset - 1     )
											]

											if len(set(lowerCamelCase     )     ) != len(lowerCamelCase     ):
														raise ValueError(
														    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
														    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''     )
											lowerCamelCase__		:		Optional[Any]			=additional_special_tokens_extended
								else:
											lowerCamelCase__		:		Tuple			=[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'''<unk_{i}>''' for i in range(2,  self.offset     )]

								lowerCamelCase__		:		Tuple			={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  mask_token=lowerCamelCase,  pad_token=lowerCamelCase,  mask_token_sent=lowerCamelCase,  offset=lowerCamelCase,  additional_special_tokens=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )
								lowerCamelCase__		:		Optional[int]			=mask_token_sent
								lowerCamelCase__		:		Optional[Any]			=vocab_file
								lowerCamelCase__		:		List[str]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(lowerCamelCase     )

								# add special tokens to encoder dict
								lowerCamelCase__		:		Dict[int, str]			={
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }     )

								if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,  self.offset - 1     )}     )

								lowerCamelCase__		:		Dict[str, int]			={v: k for k, v in self.encoder.items()}



					@property
					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								return len(self.sp_model     ) + self.offset



					def 		snake_case       (       self							:  Optional[Any]     )->					Dict[str, int]:
								lowerCamelCase__		:		List[Any]			={self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



					def __getstate__(       self							:  str     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=self.__dict__.copy()
								lowerCamelCase__		:		Optional[int]			=None
								return state



					def __setstate__(       self							:  Dict,  lowerCamelCase							:  int     )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''     ):
											lowerCamelCase__		:		str			={}

								lowerCamelCase__		:		Union[str, Any]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase     )



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str     )->					int:
								if token in self.decoder:
											return self.decoder[token]
								elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
								lowerCamelCase__		:		Any			=self.sp_model.piece_to_id(lowerCamelCase     )
								return sp_id + self.offset



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int     )->					str:
								if index in self.encoder:
											return self.encoder[index]
								elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
								else:
											lowerCamelCase__		:		Any			=self.sp_model.IdToPiece(index - self.offset     )
								return token



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		Optional[int]			=[]
								lowerCamelCase__		:		Tuple			=''''''
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														out_string += self.sp_model.decode(lowerCamelCase     ) + token
														lowerCamelCase__		:		str			=[]
											else:
														current_sub_tokens.append(lowerCamelCase     )
								out_string += self.sp_model.decode(lowerCamelCase     )
								return out_string.strip()



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any]=False     )->					List[str]:
								return 1



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		Tuple			=set(self.all_special_ids     )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

								return [1 if x in all_special_ids else 0 for x in seq]



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  List,  lowerCamelCase							:  Optional[List] = None,  lowerCamelCase							:  bool = False     )->					List[int]:
								if already_has_special_tokens:
											return self._special_token_mask(lowerCamelCase     )
								elif token_ids_a is None:
											return self._special_token_mask(lowerCamelCase     ) + [1]
								else:
											return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[int]=None     )->					List[int]:
								if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




					def 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None     )->					Tuple[str]:
								if not os.path.isdir(lowerCamelCase     ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								lowerCamelCase__		:		List[str]			=os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCamelCase     ) and os.path.isfile(self.vocab_file     ):
											copyfile(self.vocab_file,  lowerCamelCase     )
								elif not os.path.isfile(self.vocab_file     ):
											with open(lowerCamelCase,  '''wb'''     ) as fi:
														lowerCamelCase__		:		int			=self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase     )

								return (out_vocab_file,)




