



import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


A__								=						trt.Logger(trt.Logger.WARNING)
A__								=						absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A__								=						logging.getLogger(__name__)

A__								=						argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

A__								=						parser.parse_args()

if args.tokenizer_name:
				A__								=						AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
				raise ValueError(
				    '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
				    '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
				)

logger.info('''Training/evaluation parameters %s''', args)

A__								=						args.per_device_eval_batch_size

A__								=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A__								=						True

A__								=						'''temp_engine/bert-fp32.engine'''
if args.fpaa:
				A__								=						'''temp_engine/bert-fp16.engine'''
if args.inta:
				A__								=						'''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
				os.makedirs('''temp_engine''')

A__								=						1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
				with open(args.onnx_model_path, '''rb''') as model:
								if not parser.parse(model.read()):
												for error in range(parser.num_errors):
																print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
				A__								=						[network.get_input(i) for i in range(network.num_inputs)]
				A__								=						[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								A__								=						1 << 50
								if STRICT_TYPES:
												config.set_flag(trt.BuilderFlag.STRICT_TYPES)
								if args.fpaa:
												config.set_flag(trt.BuilderFlag.FPaa)
								if args.inta:
												config.set_flag(trt.BuilderFlag.INTa)
								A__								=						builder.create_optimization_profile()
								config.add_optimization_profile(profile)
								for i in range(len(input_names)):
												profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
								A__								=						builder.build_engine(network, config)

								# serialize_engine and store in file (can be directly loaded and deserialized):
								with open(engine_name, '''wb''') as f:
												f.write(engine.serialize())



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	np.asarray(inputs['''input_ids''']  ,  dtype=np.intaa      )
				snake_case__	:      List[str]     		=	np.asarray(inputs['''attention_mask''']  ,  dtype=np.intaa      )
				snake_case__	:      int     		=	np.asarray(inputs['''token_type_ids''']  ,  dtype=np.intaa      )

				# Copy inputs
				cuda.memcpy_htod_async(d_inputs[0]  ,  input_ids.ravel()  ,  __lowerCAmelCase      )
				cuda.memcpy_htod_async(d_inputs[1]  ,  attention_mask.ravel()  ,  __lowerCAmelCase      )
				cuda.memcpy_htod_async(d_inputs[2]  ,  token_type_ids.ravel()  ,  __lowerCAmelCase      )
				# start time
				snake_case__	:      Tuple     		=	time.time()
				# Run inference
				context.execute_async(
				    bindings=[int(__lowerCAmelCase      ) for d_inp in d_inputs] + [int(__lowerCAmelCase      ), int(__lowerCAmelCase      )]  ,  stream_handle=stream.handle      )
				# Transfer predictions back from GPU
				cuda.memcpy_dtoh_async(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
				cuda.memcpy_dtoh_async(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
				# Synchronize the stream and take time
				stream.synchronize()
				# end time
				snake_case__	:      Dict     		=	time.time()
				snake_case__	:      Tuple     		=	end_time - start_time
				snake_case__	:      List[Any]     		=	(h_outputa, h_outputa)
				# print(outputs)
				return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
A__								=						Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
				set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				A__								=						load_dataset(args.dataset_name, args.dataset_config_name)
else:
				raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

A__								=						raw_datasets['''validation'''].column_names

A__								=						'''question''' if '''question''' in column_names else column_names[0]
A__								=						'''context''' if '''context''' in column_names else column_names[1]
A__								=						'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
A__								=						tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
				logger.warning(
				    f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
				    f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
				)

A__								=						min(args.max_seq_length, tokenizer.model_max_length)



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	[q.lstrip() for q in examples[question_column_name]]

				# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
				# in one example possible giving several features when a context is long, each of those features having a
				# context that overlaps a bit the context of the previous feature.
				snake_case__	:      Any     		=	tokenizer(
				    examples[question_column_name if pad_on_right else context_column_name]  ,  examples[context_column_name if pad_on_right else question_column_name]  ,  truncation='''only_second''' if pad_on_right else '''only_first'''  ,  max_length=__lowerCAmelCase  ,  stride=args.doc_stride  ,  return_overflowing_tokens=__lowerCAmelCase  ,  return_offsets_mapping=__lowerCAmelCase  ,  padding='''max_length'''  ,  )

				# Since one example might give us several features if it has a long context, we need a map from a feature to
				# its corresponding example. This key gives us just that.
				snake_case__	:      Dict     		=	tokenized_examples.pop('''overflow_to_sample_mapping'''      )

				# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
				# corresponding example_id and we will store the offset mappings.
				snake_case__	:      Optional[Any]     		=	[]

				for i in range(len(tokenized_examples['''input_ids''']      )      ):
								# Grab the sequence corresponding to that example (to know what is the context and what is the question).
								snake_case__	:      Dict     		=	tokenized_examples.sequence_ids(__lowerCAmelCase      )
								snake_case__	:      Optional[Any]     		=	1 if pad_on_right else 0

								# One example can give several spans, this is the index of the example containing this span of text.
								snake_case__	:      str     		=	sample_mapping[i]
								tokenized_examples["example_id"].append(examples['''id'''][sample_index]      )

								# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
								# position is part of the context or not.
								snake_case__	:      Union[str, Any]     		=	[
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]      )
								]

				return tokenized_examples


A__								=						raw_datasets['''validation''']
# Validation Feature Creation
A__								=						eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

A__								=						default_data_collator

A__								=						eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
A__								=						DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase="eval"      )   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	postprocess_qa_predictions(
				    examples=__lowerCAmelCase  ,  features=__lowerCAmelCase  ,  predictions=__lowerCAmelCase  ,  version_2_with_negative=args.version_2_with_negative  ,  n_best_size=args.n_best_size  ,  max_answer_length=args.max_answer_length  ,  null_score_diff_threshold=args.null_score_diff_threshold  ,  output_dir=args.output_dir  ,  prefix=__lowerCAmelCase  ,  )
				# Format the result to the format the metric expects.
				if args.version_2_with_negative:
								snake_case__	:      Tuple     		=	[
								    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
								]
				else:
								snake_case__	:      Optional[Any]     		=	[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

				snake_case__	:      str     		=	[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
				return EvalPrediction(predictions=__lowerCAmelCase  ,  label_ids=__lowerCAmelCase      )


A__								=						load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
				# setup for TRT inferrence
				for i in range(len(input_names)):
								context.set_binding_shape(i, INPUT_SHAPE)
				assert context.all_binding_shapes_specified



				def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Optional[int]:


								"""simple docstring"""

								return trt.volume(engine.get_binding_shape(__lowerCAmelCase      )      ) * engine.get_binding_dtype(__lowerCAmelCase      ).itemsize

				# Allocate device memory for inputs and outputs.
				A__								=						[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

				# Allocate output buffer
				A__								=						cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				A__								=						cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				A__								=						cuda.mem_alloc(h_outputa.nbytes)
				A__								=						cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				A__								=						cuda.Stream()

				# Evaluation
				logger.info('''***** Running Evaluation *****''')
				logger.info(f"""  Num examples = {len(eval_dataset)}""")
				logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

				A__								=						0.0
				A__								=						0
				A__								=						timeit.default_timer()

				A__								=						None
				for step, batch in enumerate(eval_dataloader):
								A__						,  A__								=						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								A__						,  A__								=						outputs
								A__								=						torch.tensor(start_logits)
								A__								=						torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								A__								=						accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
								A__								=						accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

								A__								=						(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								A__								=						logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

				if all_preds is not None:
								A__								=						nested_truncate(all_preds, len(eval_dataset))

				A__								=						timeit.default_timer() - start_time
				logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
				# Inference time from TRT
				logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
				logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))





				logger.info('''Total Number of Inference =  %d''', niter)

A__								=						post_processing_function(eval_examples, eval_dataset, all_preds)
A__								=						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





logger.info(f"""Evaluation metrics: {eval_metric}""")






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


A__								=						logging.get_logger(__name__)

A__								=						{'''vocab_file''': '''spiece.model'''}

A__								=						{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}


class       a							(			__lowerCamelCase				):



		def __init__(     self     :Union[str, Any]   ,__lowercase     :Optional[Any]   ,__lowercase     :int=False   ,__lowercase     :int=True   ,__lowercase     :Optional[int]=False   ,__lowercase     :List[str]="<s>"   ,__lowercase     :str="</s>"   ,__lowercase     :Dict="<unk>"   ,__lowercase     :Optional[int]="<sep>"   ,__lowercase     :Tuple="<pad>"   ,__lowercase     :Union[str, Any]="<cls>"   ,__lowercase     :Dict="<mask>"   ,__lowercase     :List[Any]=["<eop>", "<eod>"]   ,__lowercase     :Optional[Dict[str, Any]] = None   ,**__lowercase     :int   ,):
						snake_case__	:      Any     		=	AddedToken(__lowercase   ,lstrip=__lowercase   ,rstrip=__lowercase  ) if isinstance(__lowercase   ,__lowercase  ) else mask_token

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

						super().__init__(
						    do_lower_case=__lowercase   ,remove_space=__lowercase   ,keep_accents=__lowercase   ,bos_token=__lowercase   ,eos_token=__lowercase   ,unk_token=__lowercase   ,sep_token=__lowercase   ,pad_token=__lowercase   ,cls_token=__lowercase   ,mask_token=__lowercase   ,additional_special_tokens=__lowercase   ,sp_model_kwargs=self.sp_model_kwargs   ,**__lowercase   ,)

						snake_case__	:      Optional[Any]     		=	3

						snake_case__	:      List[str]     		=	do_lower_case
						snake_case__	:      Union[str, Any]     		=	remove_space
						snake_case__	:      Tuple     		=	keep_accents
						snake_case__	:      List[str]     		=	vocab_file

						snake_case__	:      Optional[Any]     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(__lowercase  )

						try:
										import jieba
						except ModuleNotFoundError as error:
										raise error.__class__(
										    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
										    '''See https://pypi.org/project/jieba/ for installation.'''  )
						snake_case__	:      List[Any]     		=	jieba
						snake_case__	:      Union[str, Any]     		=	str.maketrans(''' \n'''   ,'''\u2582\u2583'''  )



		@property
		# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						return len(self.sp_model  )



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Optional[Any]     		=	{self.convert_ids_to_tokens(__lowercase  ): i for i in range(self.vocab_size  )}
						vocab.update(self.added_tokens_encoder  )
						return vocab



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



		def __setstate__(     self     :int   ,__lowercase     :str  ):
						snake_case__	:      Tuple     		=	d

						# for backward compatibility
						if not hasattr(self   ,'''sp_model_kwargs'''  ):
										snake_case__	:      List[Any]     		=	{}

						snake_case__	:      List[Any]     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(self.vocab_file  )



		def 	__lowerCamelCase			(     self     :Dict   ,__lowercase     :Optional[int]  ):
						if self.remove_space:
										snake_case__	:      int     		=	''' '''.join(inputs.strip().split()  )
						else:
										snake_case__	:      Tuple     		=	inputs
						snake_case__	:      List[Any]     		=	outputs.replace('''``'''   ,'''"'''  ).replace('''\'\''''   ,'''"'''  )

						if not self.keep_accents:
										snake_case__	:      Any     		=	unicodedata.normalize('''NFKD'''   ,__lowercase  )
										snake_case__	:      Dict     		=	''''''.join([c for c in outputs if not unicodedata.combining(__lowercase  )]  )
						if self.do_lower_case:
										snake_case__	:      str     		=	outputs.lower()

						return outputs



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str  ):
						snake_case__	:      Dict     		=	self.preprocess_text(__lowercase  )
						snake_case__	:      Any     		=	self.sp_model.encode(__lowercase   ,out_type=__lowercase  )
						snake_case__	:      List[str]     		=	[]
						for piece in pieces:
										if len(__lowercase  ) > 1 and piece[-1] == str(''','''  ) and piece[-2].isdigit():
														snake_case__	:      Union[str, Any]     		=	self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowercase   ,''''''  )  )
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																		if len(cur_pieces[0]  ) == 1:
																						snake_case__	:      Optional[Any]     		=	cur_pieces[1:]
																		else:
																						snake_case__	:      Optional[Any]     		=	cur_pieces[0][1:]
														cur_pieces.append(piece[-1]  )
														new_pieces.extend(__lowercase  )
										else:
														new_pieces.append(__lowercase  )

						return new_pieces



		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :Dict  ):
						return self.sp_model.PieceToId(__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :Optional[int]  ):
						return self.sp_model.IdToPiece(__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :List[Any]  ):
						snake_case__	:      Union[str, Any]     		=	''''''.join(__lowercase  ).replace(__lowercase   ,''' '''  ).strip()
						return out_string



		def 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      Optional[Any]     		=	[self.sep_token_id]
						snake_case__	:      str     		=	[self.cls_token_id]
						if token_ids_a is None:
										return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls



		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None   ,__lowercase     :bool = False  ):

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

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



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      List[Any]     		=	[self.sep_token_id]
						snake_case__	:      Optional[Any]     		=	[2]

						if token_ids_a is None:
										return len(token_ids_a + sep  ) * [0] + cls_segment_id
						return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :str   ,__lowercase     :Optional[str] = None  ):
						if not os.path.isdir(__lowercase  ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
						snake_case__	:      Union[str, Any]     		=	os.path.join(
						    __lowercase   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

						return (out_vocab_file,)






		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,*__lowercase     :Optional[int]   ,**__lowercase     :Dict  ):
						snake_case__	:      Dict     		=	super()._decode(*__lowercase   ,**__lowercase  )
						snake_case__	:      List[Any]     		=	text.replace(''' '''   ,''''''  ).replace('''\u2582'''   ,''' '''  ).replace('''\u2583'''   ,'''\n'''  )
						return text

