
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer


class        a__  :


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      raise NotImplementedError()






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      raise NotImplementedError()




class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       tokenizer
      _lowercase      :				Dict      					=       skip_prompt
      _lowercase      :				Optional[int]      					=       decode_kwargs

      # variables used in the streaming process
      _lowercase      :				Optional[Any]      					=       []
      _lowercase      :				Dict      					=       0
      _lowercase      :				Optional[Any]      					=       True


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if len(value.shape  ) > 1 and value.shape[0] > 1:
         raise ValueError("TextStreamer only supports batch size 1"  )
      elif len(value.shape  ) > 1:
         _lowercase      :				Dict      					=       value[0]

      if self.skip_prompt and self.next_tokens_are_prompt:
         _lowercase      :				List[Any]      					=       False
         return

      # Add the new token to the cache and decodes the entire thing.
      self.token_cache.extend(value.tolist()  )
      _lowercase      :				Optional[int]      					=       self.tokenizer.decode(self.token_cache      ,    **self.decode_kwargs  )

      # After the symbol for a new line, we flush the cache.
      if text.endswith("\n"  ):
         _lowercase      :				Dict      					=       text[self.print_len :]
         _lowercase      :				Tuple      					=       []
         _lowercase      :				Tuple      					=       0
      # If the last token is a CJK character, we print the characters.
      elif len(_UpperCamelCase  ) > 0 and self._is_chinese_char(ord(text[-1]  )  ):
         _lowercase      :				Dict      					=       text[self.print_len :]
         self.print_len += len(_UpperCamelCase  )
      # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
      # which may change with the subsequent token -- there are probably smarter ways to do this!)
      else:
         _lowercase      :				Union[str, Any]      					=       text[self.print_len : text.rfind(" "  ) + 1]
         self.print_len += len(_UpperCamelCase  )

      self.on_finalized_text(_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if len(self.token_cache  ) > 0:
         _lowercase      :				Union[str, Any]      					=       self.tokenizer.decode(self.token_cache      ,    **self.decode_kwargs  )
         _lowercase      :				List[Any]      					=       text[self.print_len :]
         _lowercase      :				Optional[Any]      					=       []
         _lowercase      :				Optional[int]      					=       0
      else:
         _lowercase      :				Any      					=       ""

      _lowercase      :				Any      					=       True
      self.on_finalized_text(_UpperCamelCase      ,    stream_end=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      print(_UpperCamelCase      ,    flush=_UpperCamelCase      ,    end="" if not stream_end else None  )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if (
          (cp >= 0X4E_00 and cp <= 0X9F_FF)
          or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
          or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
          or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
          or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
          or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
          or (cp >= 0XF9_00 and cp <= 0XFA_FF)
          or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
      ):  #
         return True

      return False




class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )
      _lowercase      :				Dict      					=       Queue()
      _lowercase      :				Optional[Any]      					=       None
      _lowercase      :				Any      					=       timeout


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      self.text_queue.put(_UpperCamelCase      ,    timeout=self.timeout  )
      if stream_end:
         self.text_queue.put(self.stop_signal      ,    timeout=self.timeout  )


   def __iter__(			self  ):





      """simple docstring"""





      return self






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       self.text_queue.get(timeout=self.timeout  )
      if value == self.stop_signal:
         raise StopIteration()
      else:
         return value









'''simple docstring'''
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,
)






