





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class    _UpperCAmelCase							(		_lowerCamelCase			,       unittest.TestCase     ):
      a						       =							CTRLTokenizer
      a						       =							False
      a						       =							False
      def 		_lowerCamelCase      (		self       ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         A_     :				Optional[Any]			      =	["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
         A_     :				List[str]			      =	dict(zip(a__      ,       range(len(a__       )       )       )       )
         A_     :				Tuple			      =	["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
         A_     :				str			      =	{"""unk_token""": """<unk>"""}

         A_     :				Tuple			      =	os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         A_     :				Optional[Any]			      =	os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""merges_file"""]       )
         with open(self.vocab_file      ,       """w"""      ,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(a__       ) + """\n"""       )
         with open(self.merges_file      ,       """w"""      ,       encoding="""utf-8"""       ) as fp:
            fp.write("""\n""".join(a__       )       )
      def 		_lowerCamelCase      (		self      ,       **a__       ):
         kwargs.update(self.special_tokens_map       )
         return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Tuple			      =	"""adapt react readapt apt"""
         A_     :				Optional[Any]			      =	"""adapt react readapt apt"""
         return input_text, output_text






      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	CTRLTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map       )
         A_     :				Optional[Any]			      =	"""adapt react readapt apt"""
         A_     :				Optional[int]			      =	"""adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
         A_     :				Optional[int]			      =	tokenizer.tokenize(a__       )
         self.assertListEqual(a__      ,       a__       )

         A_     :				Tuple			      =	tokens + [tokenizer.unk_token]

         A_     :				List[str]			      =	[0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__       )      ,       a__       )













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






