



"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel

class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Optional[Any]=13,  lowerCamelCase							:  Optional[int]=7,  lowerCamelCase							:  Optional[int]=True,  lowerCamelCase							:  int=True,  lowerCamelCase							:  str=False,  lowerCamelCase							:  int=True,  lowerCamelCase							:  Optional[Any]=99,  lowerCamelCase							:  Union[str, Any]=32,  lowerCamelCase							:  Optional[int]=5,  lowerCamelCase							:  List[Any]=4,  lowerCamelCase							:  List[Any]=37,  lowerCamelCase							:  Any="gelu",  lowerCamelCase							:  Any=0.1,  lowerCamelCase							:  List[Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[int]=16,  lowerCamelCase							:  Optional[int]=2,  lowerCamelCase							:  Optional[int]=0.02,  lowerCamelCase							:  List[str]=3,  lowerCamelCase							:  str=4,  lowerCamelCase							:  Any=None,  )->					Optional[Any]:
								lowerCamelCase__		:		Optional[Any]			=parent
								lowerCamelCase__		:		Dict			=batch_size
								lowerCamelCase__		:		int			=seq_length
								lowerCamelCase__		:		str			=is_training
								lowerCamelCase__		:		str			=use_input_mask
								lowerCamelCase__		:		List[Any]			=use_token_type_ids
								lowerCamelCase__		:		Union[str, Any]			=use_labels
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Tuple			=num_attention_heads
								lowerCamelCase__		:		Dict			=intermediate_size
								lowerCamelCase__		:		Dict			=hidden_act
								lowerCamelCase__		:		Optional[int]			=hidden_dropout_prob
								lowerCamelCase__		:		List[Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		List[Any]			=max_position_embeddings
								lowerCamelCase__		:		str			=type_vocab_size
								lowerCamelCase__		:		List[str]			=type_sequence_label_size
								lowerCamelCase__		:		int			=initializer_range
								lowerCamelCase__		:		Union[str, Any]			=num_labels
								lowerCamelCase__		:		str			=num_choices
								lowerCamelCase__		:		List[str]			=scope



					def 		snake_case       (       self							:  Optional[Any]     )->					Optional[int]:
								lowerCamelCase__		:		str			=ids_tensor([self.batch_size, self.seq_length],  self.vocab_size     )

								lowerCamelCase__		:		Union[str, Any]			=None
								if self.use_input_mask:
											lowerCamelCase__		:		str			=random_attention_mask([self.batch_size, self.seq_length]     )

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

								lowerCamelCase__		:		Dict			=None
								lowerCamelCase__		:		Optional[int]			=None
								lowerCamelCase__		:		Any			=None
								if self.use_labels:
											lowerCamelCase__		:		List[Any]			=ids_tensor([self.batch_size],  self.type_sequence_label_size     )
											lowerCamelCase__		:		Optional[Any]			=ids_tensor([self.batch_size, self.seq_length],  self.num_labels     )
											lowerCamelCase__		:		Optional[int]			=ids_tensor([self.batch_size],  self.num_choices     )

								lowerCamelCase__		:		List[str]			=self.get_config()

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



					def 		snake_case       (       self							:  Optional[int]     )->					int:
								return OpenLlamaConfig(
								    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=lowerCamelCase,  initializer_range=self.initializer_range,  use_stable_embedding=lowerCamelCase,  )



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  List[Any]     )->					Optional[Any]:
								lowerCamelCase__		:		List[str]			=OpenLlamaModel(config=lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		List[Any]			=model(lowerCamelCase,  attention_mask=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=model(lowerCamelCase     )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[str],  )->					str:
								lowerCamelCase__		:		Tuple			=True
								lowerCamelCase__		:		List[Any]			=OpenLlamaModel(lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		Any			=model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  )
								lowerCamelCase__		:		Union[str, Any]			=model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  )
								lowerCamelCase__		:		Any			=model(lowerCamelCase,  attention_mask=lowerCamelCase     )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Union[str, Any],  )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=OpenLlamaForCausalLM(config=lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		str			=model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase     )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[str],  )->					Any:
								lowerCamelCase__		:		Tuple			=True
								lowerCamelCase__		:		Any			=True
								lowerCamelCase__		:		Dict			=OpenLlamaForCausalLM(config=lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()

								# first forward pass
								lowerCamelCase__		:		Union[str, Any]			=model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  use_cache=lowerCamelCase,  )
								lowerCamelCase__		:		int			=outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowerCamelCase__		:		Optional[Any]			=ids_tensor((self.batch_size, 3),  config.vocab_size     )
								lowerCamelCase__		:		Union[str, Any]			=ids_tensor((self.batch_size, 3),  vocab_size=2     )

								# append to next input_ids and
								lowerCamelCase__		:		List[Any]			=torch.cat([input_ids, next_tokens],  dim=-1     )
								lowerCamelCase__		:		List[Any]			=torch.cat([input_mask, next_mask],  dim=-1     )

								lowerCamelCase__		:		Dict			=model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  output_hidden_states=lowerCamelCase,  )['''hidden_states'''][0]
								lowerCamelCase__		:		Optional[int]			=model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  output_hidden_states=lowerCamelCase,  )['''hidden_states'''][0]

								# select random slice
								lowerCamelCase__		:		List[str]			=ids_tensor((1,),  output_from_past.shape[-1]     ).item()
								lowerCamelCase__		:		Optional[Any]			=output_from_no_past[:, -3:, random_slice_idx].detach()
								lowerCamelCase__		:		int			=output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-3     )     )




					def 		snake_case       (       self							:  Optional[Any]     )->					Any:
								lowerCamelCase__		:		str			=self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   (
								    lowerCamelCase__
								)	,   
								)		:		List[Any]			=config_and_inputs
								lowerCamelCase__		:		Optional[int]			={'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict





@require_torch
class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   lowerCAmelCase_		,   lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							(
					    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
					)
					_a   			=							(OpenLlamaForCausalLM,) if is_torch_available() else ()
					_a   			=							(
					    {
					        'feature-extraction': OpenLlamaModel,
					        'text-classification': OpenLlamaForSequenceClassification,
					        'text-generation': OpenLlamaForCausalLM,
					        'zero-shot': OpenLlamaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					_a   			=							False
					_a   			=							False



					def 		snake_case       (       self							:  Dict     )->					List[Any]:
								lowerCamelCase__		:		List[Any]			=OpenLlamaModelTester(self     )
								lowerCamelCase__		:		List[Any]			=ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37     )



					def 		snake_case       (       self							:  Optional[int]     )->					Optional[Any]:
								self.config_tester.run_common_tests()



					def 		snake_case       (       self							:  Tuple     )->					Union[str, Any]:
								lowerCamelCase__		:		int			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase     )



					def 		snake_case       (       self							:  int     )->					Optional[Any]:
								lowerCamelCase__		:		List[str]			=self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
											lowerCamelCase__		:		Dict			=type
											self.model_tester.create_and_check_model(*lowerCamelCase     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					Optional[int]:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__		:		Tuple			=3
								lowerCamelCase__		:		Dict			=input_dict['''input_ids''']
								lowerCamelCase__		:		Dict			=input_ids.ne(1     ).to(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=ids_tensor([self.model_tester.batch_size],  self.model_tester.type_sequence_label_size     )
								lowerCamelCase__		:		List[str]			=OpenLlamaForSequenceClassification(lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		Union[str, Any]			=model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase     )
								self.assertEqual(result.logits.shape,  (self.model_tester.batch_size, self.model_tester.num_labels)     )



					def 		snake_case       (       self							:  Optional[int]     )->					int:
								lowerCamelCase__	,   lowerCamelCase__		:		str			=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__		:		Any			=3
								lowerCamelCase__		:		List[Any]			='''single_label_classification'''
								lowerCamelCase__		:		Any			=input_dict['''input_ids''']
								lowerCamelCase__		:		Optional[Any]			=input_ids.ne(1     ).to(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=ids_tensor([self.model_tester.batch_size],  self.model_tester.type_sequence_label_size     )
								lowerCamelCase__		:		Optional[Any]			=OpenLlamaForSequenceClassification(lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		str			=model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase     )
								self.assertEqual(result.logits.shape,  (self.model_tester.batch_size, self.model_tester.num_labels)     )



					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__	,   lowerCamelCase__		:		Tuple			=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__		:		Optional[int]			=3
								lowerCamelCase__		:		Optional[int]			='''multi_label_classification'''
								lowerCamelCase__		:		Optional[Any]			=input_dict['''input_ids''']
								lowerCamelCase__		:		str			=input_ids.ne(1     ).to(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=ids_tensor(
								    [self.model_tester.batch_size, config.num_labels],  self.model_tester.type_sequence_label_size     ).to(torch.float     )
								lowerCamelCase__		:		str			=OpenLlamaForSequenceClassification(lowerCamelCase     )
								model.to(lowerCamelCase     )
								model.eval()
								lowerCamelCase__		:		Union[str, Any]			=model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase     )
								self.assertEqual(result.logits.shape,  (self.model_tester.batch_size, self.model_tester.num_labels)     )



					@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test'''     )
					def 		snake_case       (       self							:  Optional[Any]     )->					List[str]:
								pass




					@parameterized.expand([('''linear''',), ('''dynamic''',)]     )
					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Optional[int]     )->					str:
								lowerCamelCase__	,   lowerCamelCase__		:		str			=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__		:		List[Any]			=ids_tensor([1, 10],  config.vocab_size     )
								lowerCamelCase__		:		str			=ids_tensor([1, int(config.max_position_embeddings * 1.5     )],  config.vocab_size     )

								set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
								lowerCamelCase__		:		Union[str, Any]			=OpenLlamaModel(lowerCamelCase     )
								original_model.to(lowerCamelCase     )
								original_model.eval()
								lowerCamelCase__		:		List[str]			=original_model(lowerCamelCase     ).last_hidden_state
								lowerCamelCase__		:		str			=original_model(lowerCamelCase     ).last_hidden_state

								set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
								lowerCamelCase__		:		Tuple			={'''type''': scaling_type, '''factor''': 10.0}
								lowerCamelCase__		:		Any			=OpenLlamaModel(lowerCamelCase     )
								scaled_model.to(lowerCamelCase     )
								scaled_model.eval()
								lowerCamelCase__		:		int			=scaled_model(lowerCamelCase     ).last_hidden_state
								lowerCamelCase__		:		Optional[int]			=scaled_model(lowerCamelCase     ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
											self.assertTrue(torch.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-5     )     )
								else:
											self.assertFalse(torch.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-5     )     )

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-5     )     )









"""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,)




