
'''simple docstring'''




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowercase			:				Dict    		=			os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
      from run_translation import main  # noqa


set_seed(42)
lowercase			:				Dict    		=			'sshleifer/student_marian_en_ro_6_1'
lowercase			:				Optional[int]    		=			'sshleifer/tiny-mbart'





@require_torch
class  A				(							__snake_case						):





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			) ->				str:






         """simple docstring"""






         A       :   str					=						self.run_trainer(
             eval_steps=1						,			max_len=12						,			model_name=SCREAMING_SNAKE_CASE						,			num_train_epochs=1						,			distributed=SCREAMING_SNAKE_CASE						,			extra_args_str=SCREAMING_SNAKE_CASE						,			predict_with_generate=SCREAMING_SNAKE_CASE						,			do_train=SCREAMING_SNAKE_CASE						,			do_eval=SCREAMING_SNAKE_CASE						,			do_predict=SCREAMING_SNAKE_CASE						,			)
         A       :   List[Any]					=						TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE						,			'''trainer_state.json'''	)	).log_history

         if not do_eval:
             return

         A       :   Dict					=						[log for log in logs if '''eval_loss''' in log.keys()]

         A       :   List[str]					=						eval_metrics[0]
         if predict_with_generate:
             assert "eval_bleu" in first_step_stats

             A       :   Tuple					=						eval_metrics[-1]
             assert isinstance(last_step_stats['''eval_bleu''']						,			SCREAMING_SNAKE_CASE	)
             assert not math.isnan(float(last_step_stats['''eval_loss''']	)	), "eval_loss must not be `nan`"





     @require_torch_non_multi_gpu
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         self.run_seqaseq_quick()





     @require_torch_multi_gpu
     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE	)





     @require_torch_multi_gpu
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE	)





     @unittest.skip('''Requires an update of the env running those tests'''	)
     @require_torch_multi_gpu
     @require_fairscale
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE						,			extra_args_str='''--sharded_ddp simple'''	)





     @unittest.skip('''Requires an update of the env running those tests'''	)
     @require_torch_multi_gpu
     @require_fairscale
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE						,			extra_args_str='''--sharded_ddp simple --fp16'''	)





     @unittest.skip('''Requires an update of the env running those tests'''	)
     @require_torch_multi_gpu
     @require_fairscale
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE						,			extra_args_str='''--sharded_ddp zero_dp_2'''						,			predict_with_generate=SCREAMING_SNAKE_CASE	)





     @unittest.skip('''Requires an update of the env running those tests'''	)
     @require_torch_multi_gpu
     @require_fairscale
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         self.run_seqaseq_quick(
             distributed=SCREAMING_SNAKE_CASE						,			extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''						,			predict_with_generate=SCREAMING_SNAKE_CASE	)





     @require_apex
     @require_torch_gpu
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE						,			extra_args_str='''--fp16 --fp16_backend=apex'''	)
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE						,			extra_args_str='''--fp16 --fp16_backend=apex'''	)





     @parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']	)
     @require_torch_multi_gpu
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   Any					=						{
             # test with the default log_level - should be info and thus log info once
             '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
         }

         A       :   Any					=						experiments[experiment_id]
         A       :   Any					=						{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
         A       :   Union[str, Any]					=						'''Running training'''
         with CaptureStderr() as cl:
             self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE						,			extra_args_str=data['''extra_args_str''']	)
         A       :   int					=						len(re.findall(SCREAMING_SNAKE_CASE						,			cl.err	)	)
         self.assertEqual(SCREAMING_SNAKE_CASE						,			data['''n_matches''']	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Tuple					=						self.run_trainer(
             eval_steps=2						,			max_len=128						,			model_name=SCREAMING_SNAKE_CASE						,			learning_rate=3e-4						,			num_train_epochs=10						,			distributed=SCREAMING_SNAKE_CASE						,			)

         # Check metrics
         A       :   Union[str, Any]					=						TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE						,			'''trainer_state.json'''	)	).log_history
         A       :   Union[str, Any]					=						[log for log in logs if '''eval_loss''' in log.keys()]
         A       :   List[str]					=						eval_metrics[0]
         A       :   List[Any]					=						eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats['''eval_bleu''']						,			SCREAMING_SNAKE_CASE	)

         # test if do_predict saves generations and metrics
         A       :   int					=						os.listdir(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						{os.path.basename(SCREAMING_SNAKE_CASE	) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents





     @slow
     @require_bitsandbytes
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(SCREAMING_SNAKE_CASE	) -> Tuple[int, float]:
             A       :   Optional[int]					=						'''--skip_memory_metrics 0'''

             A       :   List[Any]					=						self.run_trainer(
                 max_len=128						,			model_name=SCREAMING_SNAKE_CASE						,			learning_rate=3e-4						,			num_train_epochs=1						,			optim=SCREAMING_SNAKE_CASE						,			distributed=SCREAMING_SNAKE_CASE						,			extra_args_str=SCREAMING_SNAKE_CASE						,			do_eval=SCREAMING_SNAKE_CASE						,			do_predict=SCREAMING_SNAKE_CASE						,			n_gpus_to_use=1						,			)

             # Check metrics
             A       :   str					=						TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE						,			'''trainer_state.json'''	)	).log_history
             A       :   Union[str, Any]					=						int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20	)
             A       :   int					=						int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20	)

             A       :   List[Any]					=						logs[0]['''train_loss''']
             return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         A,  A,  A       :   List[Any]					=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value	)
         A,  A,  A       :   Optional[Any]					=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value	)

         A       :   Dict					=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         A       :   Union[str, Any]					=						gpu_peak_mem_orig + gpu_alloc_mem_orig
         A       :   Optional[Any]					=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         A       :   str					=						gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         A       :   List[str]					=						120

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
             F' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'
             F' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'						,			)

         self.assertGreater(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
             F' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'
             F' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'						,			)

         self.assertEqual(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			F'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 3e-3						,			SCREAMING_SNAKE_CASE = "adafactor"						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			) ->				Tuple:






         """simple docstring"""






         A       :   Tuple					=						self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         A       :   Dict					=						self.get_auto_remove_tmp_dir()
         A       :   int					=						F'\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(SCREAMING_SNAKE_CASE	)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(SCREAMING_SNAKE_CASE	)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

         A       :   Any					=						F'\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(SCREAMING_SNAKE_CASE	)}\n        '.split()

         A       :   Optional[Any]					=						'''
            --do_predict
        '''.split()

         A       :   Union[str, Any]					=						[]
         if do_train:
             args += args_train

         if do_eval:
             args += args_eval

         if do_predict:
             args += args_predict

         if predict_with_generate:
             args += "--predict_with_generate".split()

         if do_train:
             if optim == "adafactor":
                 args += "--adafactor".split()
             else:
                 args += F'--optim {optim}'.split()

         if extra_args_str is not None:
             args += extra_args_str.split()

         if distributed:
             if n_gpus_to_use is None:
                 A       :   Dict					=						get_gpu_count()
             A       :   Tuple					=						get_torch_dist_unique_port()
             A       :   str					=						F'\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            '.split()
             A       :   str					=						[sys.executable] + distributed_args + args
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             execute_subprocess_async(SCREAMING_SNAKE_CASE						,			env=self.get_env()	)
         else:
             A       :   List[str]					=						['''run_translation.py'''] + args
             with patch.object(SCREAMING_SNAKE_CASE						,			'''argv'''						,			SCREAMING_SNAKE_CASE	):
                 main()

         return output_dir











"""simple docstring"""





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

import sentencepiece as spm

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


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__					=   {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__					=   {
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

SCREAMING_SNAKE_CASE__					=   {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']





      def __init__(       self      ,						lowercase      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            lowerCAmelCase  						=       {} if sp_model_kwargs is None else sp_model_kwargs

            lowerCAmelCase  						=       kwargs.get("""name_or_path""" )
            if name_or_path is None:
                  logger.warning(
                      """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                      """ you are testing the model, this can safely be ignored""" )
                  lowerCAmelCase  						=       """None"""

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            lowerCAmelCase  						=       """<|endoftext|>""" if eos_token is None else eos_token
            lowerCAmelCase  						=       """<unk>""" if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                  lowerCAmelCase  						=       unk_token if pad_token is None else pad_token
                  lowerCAmelCase  						=       eos_token if bos_token is None else bos_token
            else:
                  lowerCAmelCase  						=       """<pad>""" if pad_token is None else pad_token
                  lowerCAmelCase  						=       """<s>""" if bos_token is None else bos_token

            super().__init__(
                do_lower_case=lowercase      ,						remove_space=lowercase      ,						keep_accents=lowercase      ,						bos_token=lowercase      ,						eos_token=lowercase      ,						unk_token=lowercase      ,						pad_token=lowercase      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**lowercase      ,						)

            lowerCAmelCase  						=       do_lower_case
            lowerCAmelCase  						=       remove_space
            lowerCAmelCase  						=       keep_accents
            lowerCAmelCase  						=       vocab_file

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )

            # Used for whitespace normalization in input texts
            # fmt : off
            lowerCAmelCase  						=       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            lowerCAmelCase  						=       re.compile(
                f'[{"".join(map(lowercase      ,						list(range(0      ,						9 ) ) + list(range(11      ,						32 ) ) + list(range(127      ,						160 ) ) + [160, 173, 8_203] ) )}]' )





      def __getstate__(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            return state





      def __setstate__(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       d

            # for backward compatibility
            if not hasattr(self      ,						"""sp_model_kwargs""" ):
                  lowerCAmelCase  						=       {}

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(self.vocab_file )





      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def     _snake_case			(       self )		->     int:
            return len(self.sp_model )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       self.non_printing_characters_re.sub(""""""      ,						lowercase )

            # Normalize whitespaces
            lowerCAmelCase  						=       """""".join([char if char not in self.whitespaces else """ """ for char in text] )

            # NFC Unicode normalization
            lowerCAmelCase  						=       unicodedata.normalize("""NFC"""      ,						lowercase )
            return text





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     List[str]:
            lowerCAmelCase  						=       self.preprocess_text(lowercase )
            return self.sp_model.encode(lowercase      ,						out_type=lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            return self.sp_model.PieceToId(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            return self.sp_model.IdToPiece(lowercase )





      @staticmethod
      def     _snake_case			(       lowercase )		->     str:
            return out_string





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       """"""
            lowerCAmelCase  						=       False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                        if not prev_is_special:
                              out_string += " "

                        out_string += self.sp_model.decode(lowercase ) + token
                        lowerCAmelCase  						=       True
                        lowerCAmelCase  						=       []
                  else:
                        current_sub_tokens.append(lowercase )
                        lowerCAmelCase  						=       False
            out_string += self.sp_model.decode(lowercase )

            return out_string





      def     _snake_case			(       self )		->     Dict[str, int]:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            if not os.path.isdir(lowercase ):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                  return
            lowerCAmelCase  						=       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:
                        lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
                        fi.write(lowercase )

            return (out_vocab_file,)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False )		->     Union[List[int], List[List[int]], "torch.Tensor"]:

            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       self.preprocess_text(lowercase )
                  lowerCAmelCase  						=       self.sp_model.encode(lowercase )
            else:
                  lowerCAmelCase  						=       [self.preprocess_text(lowercase ) for t in text]
                  lowerCAmelCase  						=       self.sp_model.encode(lowercase )

            if return_tensors is True or return_tensors == "pt":
                  lowerCAmelCase  						=       torch.tensor(lowercase )

            return token_ids





      def     _snake_case			(       self      ,						lowercase )		->     str:
            return self.sp_model.decode(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     List[int]:
            lowerCAmelCase  						=       [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
            lowerCAmelCase  						=       (
                f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowercase ) + f'{self.bos_token}Bot:'
            )
            return self.encode(text=lowercase )






