






"""simple docstring"""



import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_UpperCamelCase						:    List[str]		=						logging.getLogger()
_UpperCamelCase						:    Tuple		=						torch.cuda.is_available()
_UpperCamelCase						:    Tuple		=						{
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 4_2,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1e-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Path						,   __snake_case     :    list       ):





  '''simple docstring'''





  lowercase				       =			'\n'.join(__snake_case       )
  Path(__snake_case       ).open('w'       ).writelines(__snake_case       )


_UpperCamelCase						:    int		=						[' Sam ate lunch today.', 'Sams lunch ingredients.']
_UpperCamelCase						:    Optional[Any]		=						['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_UpperCamelCase						:    Any		=						'patrickvonplaten/t5-tiny-random'
_UpperCamelCase						:    Union[str, Any]		=						'sshleifer/t5-tinier-random'
_UpperCamelCase						:    str		=						'sshleifer/bart-tiny-random'
_UpperCamelCase						:    int		=						'sshleifer/tiny-mbart'
_UpperCamelCase						:    List[str]		=						'sshleifer/tiny-marian-en-de'
_UpperCamelCase						:    Any		=						'stas/tiny-wmt19-en-de'


_UpperCamelCase						:    Union[str, Any]		=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]       ):





  '''simple docstring'''





  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(__snake_case						,   f'{split}.source'       )						,   __snake_case       )
    _dump_articles(os.path.join(__snake_case						,   f'{split}.target'       )						,   __snake_case       )
  return tmp_dir

class 			a	(  a_      ):







    @classmethod
    def      UpperCamelCase_ (     cls			):
      logging.disable(logging.CRITICAL			)  # remove noisy download output from tracebacks
      return cls







    @slow
    @require_torch_gpu
    def      UpperCamelCase_ (     self			):
      lowercase				       =			list_models()
      lowercase				       =			'sshleifer'
      lowercase				       =			[x.modelId for x in model_list if x.modelId.startswith(_lowerCamelCase			)]
      lowercase				       =			['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
      lowercase				       =			[]
      for m in model_ids:
        if m in allowed_to_be_broken:
          continue
        try:
          AutoConfig.from_pretrained(_lowerCamelCase			)
        except Exception:
          failures.append(_lowerCamelCase			)
      assert not failures, F'The following models could not be loaded through AutoConfig: {failures}'







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
      self._test_distiller_cli(_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'max_epochs': 4,
          'val_check_interval': 0.2_5,
          'alpha_hid': 2.0,
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
      }
      lowercase				       =			self._test_distiller_cli(_lowerCamelCase				,						check_contents=_lowerCamelCase			)

      lowercase				       =			list(Path(model.output_dir			).glob('*.ckpt'			)			)
      self.assertEqual(1				,						len(_lowerCamelCase			)			)
      lowercase				       =			list(Path(model.output_dir			).glob('**/*.bin'			)			)
      self.assertEqual(len(_lowerCamelCase			)				,						2			)
      lowercase				       =			lmap(str.strip				,						Path(model.hparams.data_dir			).joinpath('test.source'			).open().readlines()			)
      lowercase				       =			tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(_lowerCamelCase				,						_lowerCamelCase				,						str(model.output_dir / 'best_tfmr'			)			)
      self.assertTrue(Path(_lowerCamelCase			).exists()			)

      lowercase				       =			self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]				,						transformer_ckpts[0].parent				,						_lowerCamelCase			)
      assert os.path.exists(os.path.join(_lowerCamelCase				,						'pytorch_model.bin'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase			)
      lowercase       ,	lowercase				       =			model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
      lowercase				       =			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,						dtype=torch.long				,						device=model.device			)
      lowercase				       =			target_ids[:, :-1].contiguous()  # Why this line?
      lowercase				       =			target_ids[:, 1:].clone()  # why clone?
      lowercase				       =			model(
          _lowerCamelCase				,						attention_mask=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase				,						labels=_lowerCamelCase				,						use_cache=_lowerCamelCase			).loss

      lowercase				       =			model(_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase				,						use_cache=_lowerCamelCase			).logits

      lowercase				       =			nn.functional.log_softmax(_lowerCamelCase				,						dim=-1			)
      lowercase       ,	lowercase				       =			label_smoothed_nll_loss(
          _lowerCamelCase				,						_lowerCamelCase				,						0.1				,						ignore_index=model.config.pad_token_id			)
      with self.assertRaises(_lowerCamelCase			):
        # TODO: understand why this breaks
        self.assertEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'student_encoder_layers': 2,
          'student_decoder_layers': 1,
          'num_train_epochs': 4,
          'val_check_interval': 0.2_5,
          'alpha_hid': 2.0,
          'task': 'translation',
          'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
          'tokenizer_name': MBART_TINY,
          'teacher': MBART_TINY,
          'src_lang': 'en_XX',
          'tgt_lang': 'ro_RO',
      }
      lowercase				       =			self._test_distiller_cli(_lowerCamelCase				,						check_contents=_lowerCamelCase			)
      assert model.model.config.model_type == "mbart"

      lowercase				       =			list(Path(model.output_dir			).glob('*.ckpt'			)			)
      self.assertEqual(1				,						len(_lowerCamelCase			)			)
      lowercase				       =			list(Path(model.output_dir			).glob('**/*.bin'			)			)
      lowercase				       =			list(Path(model.output_dir			).glob('best_tfmr/*'			)			)
      assert len(_lowerCamelCase			) > 2
      self.assertEqual(len(_lowerCamelCase			)				,						2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'student_encoder_layers': 1,
          'student_decoder_layers': 1,
          'alpha_hid': 2.0,
          'teacher': T5_TINY,
          'model_name_or_path': T5_TINY,
          'tokenizer_name': T5_TINY,
      }
      self._test_distiller_cli(_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			{
          'teacher': T5_TINY,
          'student': T5_TINIER,
          'model_name_or_path': T5_TINIER,
          'tokenizer_name': T5_TINIER,
      }
      self._test_distiller_cli(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase=True			):
      lowercase				       =			{
          'label_smoothing': 0.0,
          'early_stopping_patience': -1,
          'train_batch_size': 1,
          'eval_batch_size': 2,
          'max_epochs': 2,
          'alpha_mlm': 0.2,
          'alpha_ce': 0.8,
          'do_predict': True,
          'model_name_or_path': 'sshleifer/tinier_bart',
          'teacher': CHEAP_ARGS['model_name_or_path'],
          'val_check_interval': 0.5,
      }
      default_updates.update(_lowerCamelCase			)
      lowercase				       =			CHEAP_ARGS.copy()
      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      lowercase				       =			self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						**_lowerCamelCase			)
      lowercase				       =			distill_main(argparse.Namespace(**_lowerCamelCase			)			)
      if not check_contents:
        return model
      lowercase				       =			os.listdir(_lowerCamelCase			)
      lowercase				       =			{os.path.basename(_lowerCamelCase			) for p in contents}
      lowercase				       =			[p for p in contents if p.endswith('ckpt'			)]
      assert len(_lowerCamelCase			) > 0

      self.assertIn('test_generations.txt'				,						_lowerCamelCase			)
      self.assertIn('test_results.txt'				,						_lowerCamelCase			)

      lowercase				       =			load_json(model.metrics_save_path			)
      lowercase				       =			metrics['val'][-1]
      self.assertGreaterEqual(last_step_stats['val_avg_gen_time']				,						0.0_1			)
      self.assertGreaterEqual(1.0				,						last_step_stats['val_avg_gen_time']			)
      self.assertIsInstance(last_step_stats[F'val_avg_{model.val_metric}']				,						_lowerCamelCase			)
      lowercase				       =			int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1			)
      self.assertEqual(len(metrics['val']			)				,						_lowerCamelCase			)
      self.assertEqual(len(metrics['test']			)				,						1			)
      return model


class 			a	(  a_      ):







    @parameterized.expand(
        [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				,						)
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			CHEAP_ARGS.copy()
      lowercase				       =			'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
      lowercase				       =			0.1 if task == 'translation' else 0

      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
      lowercase				       =			self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=_lowerCamelCase				,						model_name_or_path=_lowerCamelCase				,						tokenizer_name=_lowerCamelCase				,						train_batch_size=2				,						eval_batch_size=2				,						output_dir=_lowerCamelCase				,						do_predict=_lowerCamelCase				,						task=_lowerCamelCase				,						src_lang='en_XX'				,						tgt_lang='ro_RO'				,						freeze_encoder=_lowerCamelCase				,						freeze_embeds=_lowerCamelCase				,						)
      assert "n_train" in args_d
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      lowercase				       =			main(_lowerCamelCase			)

      lowercase				       =			module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
        lowercase				       =			module.model.lm_head
        assert not lm_head.weight.requires_grad
        assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
        lowercase				       =			module.model.model
        lowercase				       =			fsmt.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not fsmt.decoder.embed_tokens.weight.requires_grad
        # check that embeds are not the same
        assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
        lowercase				       =			module.model.model
        lowercase				       =			bart.decoder.embed_positions
        assert not embed_pos.weight.requires_grad
        assert not bart.shared.weight.requires_grad
        # check that embeds are the same
        assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
        assert bart.decoder.embed_tokens == bart.shared

      lowercase				       =			load_json(module.output_dir / 'text_batch.json'			)
      assert isinstance(_lowerCamelCase				,						_lowerCamelCase			)
      assert len(_lowerCamelCase			) >= 4







    def      UpperCamelCase_ (     self			):
      lowercase				       =			CHEAP_ARGS.copy()

      lowercase				       =			'summarization'
      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

      args_d.update(
          data_dir=_lowerCamelCase				,						tokenizer_name=_lowerCamelCase				,						train_batch_size=2				,						eval_batch_size=2				,						do_predict=_lowerCamelCase				,						task=_lowerCamelCase				,						src_lang='en_XX'				,						tgt_lang='ro_RO'				,						freeze_encoder=_lowerCamelCase				,						freeze_embeds=_lowerCamelCase				,						)

      # test models whose config includes the extra_model_args
      lowercase				       =			BART_TINY
      lowercase				       =			self.get_auto_remove_tmp_dir()
      lowercase				       =			args_d.copy()
      args_da.update(
          model_name_or_path=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						)
      lowercase				       =			('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
      for p in extra_model_params:
        lowercase				       =			0.5
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      lowercase				       =			main(_lowerCamelCase			)
      for p in extra_model_params:
        assert getattr(model.config				,						_lowerCamelCase			) == 0.5, F'failed to override the model config for param {p}'

      # test models whose config doesn't include the extra_model_args
      lowercase				       =			T5_TINY
      lowercase				       =			self.get_auto_remove_tmp_dir()
      lowercase				       =			args_d.copy()
      args_da.update(
          model_name_or_path=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						)
      lowercase				       =			'encoder_layerdrop'
      lowercase				       =			0.5
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      with pytest.raises(_lowerCamelCase			) as excinfo:
        lowercase				       =			main(_lowerCamelCase			)
      assert str(excinfo.value			) == F'model config doesn\'t have a `{unsupported_param}` attribute'







    def      UpperCamelCase_ (     self			):
      lowercase				       =			CHEAP_ARGS.copy()

      lowercase				       =			'summarization'
      lowercase				       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)

      lowercase				       =			BART_TINY
      lowercase				       =			self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=_lowerCamelCase				,						model_name_or_path=_lowerCamelCase				,						output_dir=_lowerCamelCase				,						tokenizer_name=_lowerCamelCase				,						train_batch_size=2				,						eval_batch_size=2				,						do_predict=_lowerCamelCase				,						task=_lowerCamelCase				,						src_lang='en_XX'				,						tgt_lang='ro_RO'				,						freeze_encoder=_lowerCamelCase				,						freeze_embeds=_lowerCamelCase				,						)

      # emulate finetune.py
      lowercase				       =			argparse.ArgumentParser()
      lowercase				       =			pl.Trainer.add_argparse_args(_lowerCamelCase			)
      lowercase				       =			SummarizationModule.add_model_specific_args(_lowerCamelCase				,						os.getcwd()			)
      lowercase				       =			{'--help': True}

      # --help test
      with pytest.raises(_lowerCamelCase			) as excinfo:
        with CaptureStdout() as cs:
          lowercase				       =			parser.parse_args(_lowerCamelCase			)
        assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      lowercase				       =			lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      lowercase				       =			'non_existing_scheduler'
      lowercase				       =			{F'--lr_scheduler={unsupported_param}'}
      with pytest.raises(_lowerCamelCase			) as excinfo:
        with CaptureStderr() as cs:
          lowercase				       =			parser.parse_args(_lowerCamelCase			)
        assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      lowercase				       =			F'invalid choice: \'{unsupported_param}\''
      assert expected in cs.err, F'should have bailed on invalid choice of scheduler {unsupported_param}'

      # --lr_scheduler=existing_scheduler test
      lowercase				       =			'cosine'
      lowercase				       =			args_d.copy()
      lowercase				       =			supported_param
      lowercase				       =			argparse.Namespace(**_lowerCamelCase			)
      lowercase				       =			main(_lowerCamelCase			)
      assert (
          getattr(model.hparams				,						'lr_scheduler'			) == supported_param
      ), F'lr_scheduler={supported_param} shouldn\'t fail'





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__A	:   Any	=					logging.get_logger(__name__)

__A	:   Union[str, Any]	=					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A	:   Optional[int]	=					{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

__A	:   Optional[Any]	=					{
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}




@lru_cache()
def        __UpperCamelCase     (      )				->int:



							"""simple docstring"""
							lowerCamelCase_								=(
							    list(range(ord("""!"""		)      ,  ord("""~"""		) + 1		)		) + list(range(ord("""¡"""		)      ,  ord("""¬"""		) + 1		)		) + list(range(ord("""®"""		)      ,  ord("""ÿ"""		) + 1		)		)
							)
							lowerCamelCase_								=bs[:]
							lowerCamelCase_								=0
							for b in range(2**8		):
														if b not in bs:
																					bs.append(_A		)
																					cs.append(2**8 + n		)
																					n += 1
							lowerCamelCase_								=[chr(_A		) for n in cs]
							return dict(zip(_A      ,  _A		)		)




def        __UpperCamelCase     (      _A					:							Optional[int]		)				->Optional[int]:



							"""simple docstring"""
							lowerCamelCase_								=set()
							lowerCamelCase_								=word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)		)
														lowerCamelCase_								=char
							return pairs






class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Any	=		VOCAB_FILES_NAMES
						_UpperCamelCase:Any	=		PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase:Optional[int]	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase:List[str]	=		["input_ids", "attention_mask"]







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE="replace"     ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"     ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"     ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=False     ,						_SCREAMING_SNAKE_CASE=False     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[Any]:
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else bos_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else eos_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else unk_token
													lowerCamelCase_								=AddedToken(_SCREAMING_SNAKE_CASE     ,						lstrip=_SCREAMING_SNAKE_CASE     ,						rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							) else pad_token
													super().__init__(
													    errors=_SCREAMING_SNAKE_CASE     ,						unk_token=_SCREAMING_SNAKE_CASE     ,						bos_token=_SCREAMING_SNAKE_CASE     ,						eos_token=_SCREAMING_SNAKE_CASE     ,						pad_token=_SCREAMING_SNAKE_CASE     ,						add_prefix_space=_SCREAMING_SNAKE_CASE     ,						add_bos_token=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE     ,						)
													lowerCamelCase_								=add_bos_token

													with open(_SCREAMING_SNAKE_CASE     ,						encoding="""utf-8"""							) as vocab_handle:
																				lowerCamelCase_								=json.load(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								={v: k for k, v in self.encoder.items()}
													lowerCamelCase_								=errors  # how to handle errors in decoding
													lowerCamelCase_								=bytes_to_unicode()
													lowerCamelCase_								={v: k for k, v in self.byte_encoder.items()}
													with open(_SCREAMING_SNAKE_CASE     ,						encoding="""utf-8"""							) as merges_handle:
																				lowerCamelCase_								=merges_handle.read().split("""\n"""							)[1:-1]
													lowerCamelCase_								=[tuple(merge.split()							) for merge in bpe_merges]
													lowerCamelCase_								=dict(zip(_SCREAMING_SNAKE_CASE     ,						range(len(_SCREAMING_SNAKE_CASE							)							)							)							)
													lowerCamelCase_								={}
													lowerCamelCase_								=add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													lowerCamelCase_								=re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)







						@property
						def        _snake_case				(			self							)->							str:
													return len(self.encoder							)







						def        _snake_case				(			self							)->							Optional[Any]:
													return dict(self.encoder     ,						**self.added_tokens_encoder							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													if token in self.cache:
																				return self.cache[token]
													lowerCamelCase_								=tuple(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=get_pairs(_SCREAMING_SNAKE_CASE							)

													if not pairs:
																				return token

													while True:
																				lowerCamelCase_								=min(_SCREAMING_SNAKE_CASE     ,						key=lambda _SCREAMING_SNAKE_CASE							: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE     ,						float("""inf"""							)							)							)
																				if bigram not in self.bpe_ranks:
																											break
																				lowerCamelCase_	,   lowerCamelCase_								=bigram
																				lowerCamelCase_								=[]
																				lowerCamelCase_								=0
																				while i < len(_SCREAMING_SNAKE_CASE							):
																											try:
																																		lowerCamelCase_								=word.index(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
																											except ValueError:
																																		new_word.extend(word[i:]							)
																																		break
																											else:
																																		new_word.extend(word[i:j]							)
																																		lowerCamelCase_								=j

																											if word[i] == first and i < len(_SCREAMING_SNAKE_CASE							) - 1 and word[i + 1] == second:
																																		new_word.append(first + second							)
																																		i += 2
																											else:
																																		new_word.append(word[i]							)
																																		i += 1
																				lowerCamelCase_								=tuple(_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=new_word
																				if len(_SCREAMING_SNAKE_CASE							) == 1:
																											break
																				else:
																											lowerCamelCase_								=get_pairs(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=""" """.join(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=word
													return word







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=None							)->							Any:
													if self.add_bos_token:
																				lowerCamelCase_								=[self.bos_token_id]
													else:
																				lowerCamelCase_								=[]

													lowerCamelCase_								=bos_token_ids + token_ids_a

													if token_ids_a is None:
																				return output

													return output + bos_token_ids + token_ids_a







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = False							)->							List[int]:
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_SCREAMING_SNAKE_CASE     ,						token_ids_a=_SCREAMING_SNAKE_CASE     ,						already_has_special_tokens=_SCREAMING_SNAKE_CASE							)

													if not self.add_bos_token:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_SCREAMING_SNAKE_CASE     ,						token_ids_a=_SCREAMING_SNAKE_CASE     ,						already_has_special_tokens=_SCREAMING_SNAKE_CASE							)

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







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Any:
													lowerCamelCase_								=[]
													for token in re.findall(self.pat     ,						_SCREAMING_SNAKE_CASE							):
																				lowerCamelCase_								="""""".join(
																				    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																				bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE							).split(""" """							)							)
													return bpe_tokens







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[str]:
													return self.encoder.get(_SCREAMING_SNAKE_CASE     ,						self.encoder.get(self.unk_token							)							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													return self.decoder.get(_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							int:
													lowerCamelCase_								="""""".join(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""     ,						errors=self.errors							)
													return text







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							Tuple[str]:
													if not os.path.isdir(_SCREAMING_SNAKE_CASE							):
																				logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
																				return
													lowerCamelCase_								=os.path.join(
													    _SCREAMING_SNAKE_CASE     ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													lowerCamelCase_								=os.path.join(
													    _SCREAMING_SNAKE_CASE     ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

													with open(_SCREAMING_SNAKE_CASE     ,						"""w"""     ,						encoding="""utf-8"""							) as f:
																				f.write(json.dumps(self.encoder     ,						indent=2     ,						sort_keys=_SCREAMING_SNAKE_CASE     ,						ensure_ascii=_SCREAMING_SNAKE_CASE							) + """\n"""							)

													lowerCamelCase_								=0
													with open(_SCREAMING_SNAKE_CASE     ,						"""w"""     ,						encoding="""utf-8"""							) as writer:
																				writer.write("""#version: 0.2\n"""							)
																				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,						key=lambda _SCREAMING_SNAKE_CASE							: kv[1]							):
																											if index != token_index:
																																		logger.warning(
																																		    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																																		    """ Please check that the tokenizer is not corrupted!"""							)
																																		lowerCamelCase_								=token_index
																											writer.write(""" """.join(_SCREAMING_SNAKE_CASE							) + """\n"""							)
																											index += 1

													return vocab_file, merge_file







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=False     ,						**_SCREAMING_SNAKE_CASE							)->							Optional[Any]:
													lowerCamelCase_								=kwargs.pop("""add_prefix_space"""     ,						self.add_prefix_space							)
													if is_split_into_words or add_prefix_space:
																				lowerCamelCase_								=""" """ + text
													return (text, kwargs)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[int]:
													lowerCamelCase_								=[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE     ,						add_special_tokens=_SCREAMING_SNAKE_CASE							) + [self.eos_token_id]							)
													if len(_SCREAMING_SNAKE_CASE							) > self.model_max_length:
																				lowerCamelCase_								=input_ids[-self.model_max_length :]
													return input_ids
