





'''simple docstring'''






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
     from tests.trainer.test_trainer import (  # noqa
         RegressionModelConfig,
         RegressionPreTrainedModel,
         get_regression_trainer,
     )


set_seed(42)

snake_case_     			=						get_tests_dir("""fixtures""")
snake_case_     			=						os.path.join(dirname(get_tests_dir()))
snake_case_     			=						dirname(os.path.abspath(__file__))

# default torch.distributed port
snake_case_     			=						"""10999"""

snake_case_     			=						"""t5-small"""

# *** Working Models ***
snake_case_     			=						"""hf-internal-testing/tiny-albert"""
snake_case_     			=						"""sshleifer/bart-tiny-random"""
snake_case_     			=						"""hf-internal-testing/tiny-bert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-bigbird_pegasus"""
snake_case_     			=						"""hf-internal-testing/tiny-random-big_bird"""
snake_case_     			=						"""hf-internal-testing/tiny-random-blenderbot"""
snake_case_     			=						"""bigscience/bigscience-small-testing"""
snake_case_     			=						"""hf-internal-testing/tiny-random-deberta"""
snake_case_     			=						"""hf-internal-testing/tiny-random-deberta-v2"""
snake_case_     			=						"""sshleifer/tiny-distilbert-base-cased"""
snake_case_     			=						"""hf-internal-testing/tiny-electra"""
snake_case_     			=						"""hf-internal-testing/tiny-random-flaubert"""
snake_case_     			=						"""stas/tiny-wmt19-en-de"""
snake_case_     			=						"""hf-internal-testing/tiny-random-funnel"""
snake_case_     			=						"""sshleifer/tiny-gpt2"""
snake_case_     			=						"""hf-internal-testing/tiny-random-gptj"""
snake_case_     			=						"""hf-internal-testing/tiny-random-gpt_neo"""
snake_case_     			=						"""hf-internal-testing/tiny-layoutlm"""
snake_case_     			=						"""hf-internal-testing/tiny-random-led"""
snake_case_     			=						"""hf-internal-testing/tiny-random-longformer"""
snake_case_     			=						"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
snake_case_     			=						"""sshleifer/tiny-marian-en-de"""
snake_case_     			=						"""sshleifer/tiny-mbart"""
snake_case_     			=						"""hf-internal-testing/tiny-random-mobilebert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-mpnet"""
snake_case_     			=						"""stas/pegasus-cnn_dailymail-tiny-random"""
snake_case_     			=						"""hf-internal-testing/tiny-random-prophetnet"""
snake_case_     			=						"""sshleifer/tiny-distilroberta-base"""
snake_case_     			=						"""hf-internal-testing/tiny-random-squeezebert"""
snake_case_     			=						"""patrickvonplaten/t5-tiny-random"""
snake_case_     			=						"""hf-internal-testing/tiny-random-t5-v1.1"""
snake_case_     			=						"""hf-internal-testing/tiny-random-vit"""
snake_case_     			=						"""hf-internal-testing/tiny-xlm-roberta"""
snake_case_     			=						"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
snake_case_     			=						"""hf-internal-testing/tiny-random-mt5"""
snake_case_     			=						"""hf-internal-testing/tiny-random-camembert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
snake_case_     			=						"""hf-internal-testing/tiny-random-convbert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-layoutlmv2"""
snake_case_     			=						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
snake_case_     			=						"""hf-internal-testing/tiny-random-ctrl"""
snake_case_     			=						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
snake_case_     			=						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
snake_case_     			=						"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
snake_case_     			=						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
snake_case_     			=						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
snake_case_     			=						""""""
# - "luke" Entities classes - no plan to make such example
snake_case_     			=						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
snake_case_     			=						"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
snake_case_     			=						"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
snake_case_     			=						"""hf-internal-testing/tiny-random-tapas"""
def 				_lowerCamelCase(	UpperCamelCase__     :			str=False		)		->	List[str]:
    # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
    # - it won't be able to handle that
    # 2. for now testing with just 2 gpus max (since some quality tests may give different
    # results with mode gpus because we use very little data)
    A							:							int				=    min(2     ,							get_gpu_count()		) if distributed else 1
    A							:							Dict				=    os.environ.get('''DS_TEST_PORT'''     ,							UpperCamelCase__		)
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def 				_lowerCamelCase(	)		->	Any:
    A							:							Tuple				=    F'''{FIXTURE_DIRECTORY}/tests_samples'''
    A							:							int				=    F'''{data_dir_samples}/wmt_en_ro'''
    A							:							List[str]				=    F'''{data_dir_samples}/xsum'''
    A							:							int				=    '''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.split()

    # try to cover as many models as possible once (it's enough to run on one task per model)
    # but need a tiny model for each
    #
    # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
    A							:							List[str]				=    {
        '''trans''': [
            '''bart''',
            '''fsmt''',
            '''m2m_100''',
            '''marian''',
            '''mbart''',
            '''t5''',
            '''t5_v1''',
            # "mt5", missing model files
        ],
        '''sum''': [
            '''pegasus''',
        ],
        '''clm''': [
            '''big_bird''',
            '''bigbird_pegasus''',
            '''blenderbot''',
            '''bloom''',
            '''gpt2''',
            '''gpt_neo''',
            '''gptj''',
            '''xlm-roberta''',
            '''prophetnet''',
            # "camembert", missing model files
        ],
        '''mlm''': [
            '''albert''',
            '''deberta''',
            '''deberta-v2''',
            '''distilbert''',
            '''electra''',
            '''flaubert''',
            '''funnel''',
            '''layoutlm''',
            # "reformer", # multiple issues with either mlm/qa/clas
        ],
        '''qa''': [
            '''led''',
            '''longformer''',
            '''mobilebert''',
            '''mpnet''',
            '''roberta''',
            '''squeezebert''',
            # "convbert", # missing tokenizer files
            # "layoutlmv2", missing model files
        ],
        '''clas''': [
            '''bert''',
            '''xlnet''',
            # "hubert", # missing tokenizer files
            # "ibert", # multiple issues with either mlm/qa/clas
            # "transfo-xl", # tokenizer issues as ctrl
            # "ctrl", # tokenizer issues
            # "openai-gpt", missing model files
            # "tapas", multiple issues
        ],
        '''img_clas''': [
            '''vit''',
        ],
    }

    A							:							int				=    F'''{ROOT_DIRECTORY}/examples/pytorch'''

    A							:							List[str]				=    {
        '''trans''': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
        '''sum''': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
        '''clm''': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
        '''mlm''': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
        '''qa''': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
        '''clas''': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
        '''img_clas''': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
    }

    A							:							int				=    get_launcher(distributed=UpperCamelCase__		)

    A							:							Optional[Any]				=    {}
    for task, args in tasks.items():
        A							:							Dict				=    args.split()
        for model in tasksamodels[task]:
            A							:							Dict				=    globals()[F'''{model.upper().replace('-'     ,							'_'		)}_TINY''']
            A							:							List[str]				=    F'''--model_name_or_path {model_name}'''.split()
            A							:							List[Any]				=    launcher + args + args_model + args_main

            # # generation special case
            # if task == "gen":
            #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
            #     args_model += f"--model_type {model}".split()
            #     cmds[f"{task}_{model}"] = launcher + args + args_model
            # else:

    return cmds


snake_case_     			=						make_task_cmds()

snake_case_     			=						"""zero2"""
snake_case_     			=						"""zero3"""

snake_case_     			=						[ZEROa, ZEROa]
def 				_lowerCamelCase(	UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Optional[int]		)		->	Any:
    # customize the test name generator function as we want both params to appear in the sub-test
    # name, as by default it shows only the first param
    A							:							Union[str, Any]				=    parameterized.to_safe_name('''_'''.join(str(UpperCamelCase__		) for x in param.args		)		)
    return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
snake_case_     			=						list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 	_lowercase    (				a					):


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # return a ready to run train cmd
       if task not in task_cmds:
           raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''       )

       A							:							List[str]				=    task_cmds[task]
       A							:							Dict				=    f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       A							:							Optional[Any]				=    self.get_auto_remove_tmp_dir()
       A							:							Union[str, Any]				=    f'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir


   @parameterized.expand(_UpperCAmelCase     , name_func=_UpperCAmelCase       )
   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # testing the ability to do a run followed by recovery of full fp32 weights

       A,						A							:							List[str]				=    self.get_task_cmd(_UpperCAmelCase     , _UpperCAmelCase       )

       # 1. generate the checkpoint
       cmd += "--save_steps 1".split()
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       # 2. test that the fp32 weights get reconsolidated
       A							:							Dict				=    f'''{output_dir}/checkpoint-1'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/out.bin'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       subprocess.check_call(_UpperCAmelCase     , shell=_UpperCAmelCase       )
       assert os.path.exists(_UpperCAmelCase       ), f'''{recovered_model_path} was not found'''

       # possibly could also test that the resulting saved model is usable but given that we use
       # random models we won't know if it's any good







'''simple docstring'''






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

import regex as re

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


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_     			=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

snake_case_     			=						{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Tuple				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							List[Any]				=    bs[:]
    A							:							Tuple				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Optional[int]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	str:
    A							:							int				=    set()
    A							:							Any				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							Optional[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       A							:							Optional[int]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else mask_token

       super().__init__(
           errors=_UpperCAmelCase     , bos_token=_UpperCAmelCase     , eos_token=_UpperCAmelCase     , unk_token=_UpperCAmelCase     , sep_token=_UpperCAmelCase     , cls_token=_UpperCAmelCase     , pad_token=_UpperCAmelCase     , mask_token=_UpperCAmelCase     , add_prefix_space=_UpperCAmelCase     , **_UpperCAmelCase     , )

       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as vocab_handle:
           A							:							int				=    json.load(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    {v: k for k, v in self.encoder.items()}
       A							:							Tuple				=    errors  # how to handle errors in decoding
       A							:							List[Any]				=    bytes_to_unicode()
       A							:							List[Any]				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Optional[int]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							Union[str, Any]				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							List[str]				=    {}
       A							:							List[Any]				=    add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       A							:							Any				=    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       ):
       return len(self.encoder       )


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Union[str, Any]				=    tuple(_UpperCAmelCase       )
       A							:							Tuple				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

       while True:
           A							:							Any				=    min(_UpperCAmelCase     , key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase     , float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
               break
           A,						A							:							Optional[int]				=    bigram
           A							:							str				=    []
           A							:							List[Any]				=    0
           while i < len(_UpperCAmelCase       ):
               try:
                   A							:							Any				=    word.index(_UpperCAmelCase     , _UpperCAmelCase       )
               except ValueError:
                   new_word.extend(word[i:]       )
                   break
               else:
                   new_word.extend(word[i:j]       )
                   A							:							Tuple				=    j

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							Dict				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							int				=    get_pairs(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    ''' '''.join(_UpperCAmelCase       )
       A							:							Tuple				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Tuple				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Tuple				=    ''''''.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(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Any				=    ''''''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

       A							:							Tuple				=    0
       with open(_UpperCAmelCase     , '''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 _UpperCAmelCase       : 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!'''       )
                   A							:							List[Any]				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Dict				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase     , token_ids_a=_UpperCAmelCase     , already_has_special_tokens=_UpperCAmelCase       )

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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Dict				=    [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]

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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							str				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							List[Any]				=    ''' ''' + text
       return (text, kwargs)
