
"""simple docstring"""
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCamelCase_      :			str					=							logging.get_logger(__name__)






class      _UpperCAmelCase				(					enum.Enum):
    __lowercase							: int  =					0
    __lowercase							: Optional[int]  =					1






@add_end_docstrings(_snake_case)
class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: List[Any]  =					"generated"
    def __init__(    self						,    *snake_case_						,    **snake_case_     ):
      super().__init__(*snake_case_						,    **snake_case_     )

      self.check_model_type(
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
          if self.framework == "tf"
          else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING     )
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=None						,    snake_case_=None						,    snake_case_=None						,    snake_case_=None						,    **snake_case_						,    ):
      _snake_case     :						Dict						=   {}
      if truncation is not None:
        _snake_case     :						Tuple						=   truncation

      _snake_case     :						Tuple						=   generate_kwargs

      _snake_case     :						Tuple						=   {}
      if return_tensors is not None and return_type is None:
        _snake_case     :						str						=   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
      if return_type is not None:
        _snake_case     :						Dict						=   return_type

      if clean_up_tokenization_spaces is not None:
        _snake_case     :						Optional[int]						=   clean_up_tokenization_spaces

      if stop_sequence is not None:
        _snake_case     :						Tuple						=   self.tokenizer.encode(snake_case_						,    add_special_tokens=snake_case_     )
        if len(snake_case_     ) > 1:
          warnings.warn(
              "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
              " the stop sequence will be used as the stop sequence string in the interim."     )
        _snake_case     :						str						=   stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      return True
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    snake_case_     ):
      _snake_case     :						Optional[Any]						=   self.model.config.prefix if self.model.config.prefix is not None else ""
      if isinstance(args[0]						,    snake_case_     ):
        if self.tokenizer.pad_token_id is None:
          raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"     )
        _snake_case     :						Dict						=   ([prefix + arg for arg in args[0]],)
        _snake_case     :						List[str]						=   True

      elif isinstance(args[0]						,    snake_case_     ):
        _snake_case     :						Union[str, Any]						=   (prefix + args[0],)
        _snake_case     :						str						=   False
      else:
        raise ValueError(
            F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'     )
      _snake_case     :						Any						=   self.tokenizer(*snake_case_						,    padding=snake_case_						,    truncation=snake_case_						,    return_tensors=self.framework     )
      # This is produced by tokenizers but is an invalid generate kwargs
      if "token_type_ids" in inputs:
        del inputs["token_type_ids"]
      return inputs
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      _snake_case     :						int						=   super().__call__(*snake_case_						,    **snake_case_     )
      if (
          isinstance(args[0]						,    snake_case_     )
          and all(isinstance(snake_case_						,    snake_case_     ) for el in args[0]     )
          and all(len(snake_case_     ) == 1 for res in result     )
      ):
        return [res[0] for res in result]
      return result
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=TruncationStrategy.DO_NOT_TRUNCATE						,    **snake_case_     ):
      _snake_case     :						Optional[Any]						=   self._parse_and_tokenize(snake_case_						,    truncation=snake_case_						,    **snake_case_     )
      return inputs
    def 					lowerCamelCase__	(    self						,    snake_case_						,    **snake_case_     ):




      if self.framework == "pt":
        _snake_case			,					_snake_case     :						Tuple						=   model_inputs["input_ids"].shape
      elif self.framework == "tf":
        _snake_case			,					_snake_case     :						List[str]						=   tf.shape(model_inputs["input_ids"]     ).numpy()

      _snake_case     :						Tuple						=   generate_kwargs.get("min_length"						,    self.model.config.min_length     )
      _snake_case     :						Union[str, Any]						=   generate_kwargs.get("max_length"						,    self.model.config.max_length     )
      self.check_inputs(snake_case_						,    generate_kwargs["min_length"]						,    generate_kwargs["max_length"]     )
      _snake_case     :						List[Any]						=   self.model.generate(**snake_case_						,    **snake_case_     )
      _snake_case     :						List[str]						=   output_ids.shape[0]
      if self.framework == "pt":
        _snake_case     :						List[Any]						=   output_ids.reshape(snake_case_						,    out_b // in_b						,    *output_ids.shape[1:]     )
      elif self.framework == "tf":
        _snake_case     :						str						=   tf.reshape(snake_case_						,    (in_b, out_b // in_b, *output_ids.shape[1:])     )
      return {"output_ids": output_ids}


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=ReturnType.TEXT						,    snake_case_=False     ):
      _snake_case     :						Any						=   []
      for output_ids in model_outputs["output_ids"][0]:
        if return_type == ReturnType.TENSORS:
          _snake_case     :						Dict						=   {F'{self.return_name}_token_ids': output_ids}
        elif return_type == ReturnType.TEXT:
          _snake_case     :						str						=   {
              F'{self.return_name}_text': self.tokenizer.decode(
                  snake_case_						,    skip_special_tokens=snake_case_						,    clean_up_tokenization_spaces=snake_case_						,    )
          }
        records.append(snake_case_     )
      return records






@add_end_docstrings(_snake_case)
class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: List[str]  =					"summary"
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      return super().__call__(*snake_case_						,    **snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):




      if max_length < min_length:
        logger.warning(F'Your min_length={min_length} must be inferior than your max_length={max_length}.'     )

      if input_length < max_length:
        logger.warning(
            F'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
            "a summarization task, where outputs shorter than the input are typically wanted, you might "
            F'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'     )






@add_end_docstrings(_snake_case)
class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Any  =					"translation"
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):




      if input_length > 0.9 * max_length:
        logger.warning(
            F'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
            "increasing your max_length manually, e.g. translator(\'...\', max_length=400)"     )
      return True
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    snake_case_=TruncationStrategy.DO_NOT_TRUNCATE						,    snake_case_=None						,    snake_case_=None     ):




      if getattr(self.tokenizer						,    "_build_translation_inputs"						,    snake_case_     ):
        return self.tokenizer._build_translation_inputs(
            *snake_case_						,    return_tensors=self.framework						,    truncation=snake_case_						,    src_lang=snake_case_						,    tgt_lang=snake_case_     )
      else:
        return super()._parse_and_tokenize(*snake_case_						,    truncation=snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    **snake_case_     ):
      _snake_case			,					_snake_case			,					_snake_case     :						str						=   super()._sanitize_parameters(**snake_case_     )
      if src_lang is not None:
        _snake_case     :						Tuple						=   src_lang
      if tgt_lang is not None:
        _snake_case     :						List[Any]						=   tgt_lang
      if src_lang is None and tgt_lang is None:
        # Backward compatibility, direct arguments use is preferred.
        _snake_case     :						Any						=   kwargs.get("task"						,    self.task     )
        _snake_case     :						Dict						=   task.split("_"     )
        if task and len(snake_case_     ) == 4:
          # translation, XX, to YY
          _snake_case     :						List[str]						=   items[1]
          _snake_case     :						Optional[int]						=   items[3]
      return preprocess_params, forward_params, postprocess_params


    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      return super().__call__(*snake_case_						,    **snake_case_     )



"""simple docstring"""
def 				a__   (   a   :				int		):

  """simple docstring"""







  if not isinstance(a       ,     a		):
    raise TypeError("Input value must be an 'int' type"		)

  _snake_case     :						Union[str, Any]						=   0
  while number:
    position += 1
    number >>= 1

  return position


if __name__ == "__main__":
    import doctest

    doctest.testmod()

