



'''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

_UpperCamelCase											=						logging.get_logger(__name__)






class 					lowerCamelCase_    (				enum.Enum     ):
  """simple docstring"""




  a_												=0
  a_												=1






@add_end_docstrings(SCREAMING_SNAKE_CASE_     )
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""generated"""




  def __init__( self      :	str			,	*_a      :	Tuple			,	**_a      :	Optional[Any]   )					->					Dict:
    super().__init__(*_a			,	**_a   )

    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 						_lowercase     ( self      :	Any			,	_a      :	Tuple=None			,	_a      :	List[Any]=None			,	_a      :	Any=None			,	_a      :	str=None			,	_a      :	int=None			,	_a      :	int=None			,	**_a      :	List[Any]			,	)					->					Optional[Any]:
    __lowerCamelCase       :       Dict           = {}
    if truncation is not None:
      __lowerCamelCase       :       List[Any]           = truncation

    __lowerCamelCase       :       Optional[Any]           = generate_kwargs

    __lowerCamelCase       :       str           = {}
    if return_tensors is not None and return_type is None:
      __lowerCamelCase       :       Union[str, Any]           = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
    if return_type is not None:
      __lowerCamelCase       :       Optional[Any]           = return_type

    if clean_up_tokenization_spaces is not None:
      __lowerCamelCase       :       int           = clean_up_tokenization_spaces

    if stop_sequence is not None:
      __lowerCamelCase       :       Union[str, Any]           = self.tokenizer.encode(_a			,	add_special_tokens=_a   )
      if len(_a   ) > 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.'   )
      __lowerCamelCase       :       Dict           = stop_sequence_ids[0]

    return preprocess_params, forward_params, postprocess_params




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	int			,	_a      :	int			,	_a      :	int   )					->					Union[str, Any]:
    return True




  def 						_lowercase     ( self      :	int			,	*_a      :	int			,	_a      :	Tuple   )					->					List[Any]:
    __lowerCamelCase       :       Tuple           = self.model.config.prefix if self.model.config.prefix is not None else ''
    if isinstance(args[0]			,	_a   ):
      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'   )
      __lowerCamelCase       :       int           = ([prefix + arg for arg in args[0]],)
      __lowerCamelCase       :       Tuple           = True

    elif isinstance(args[0]			,	_a   ):
      __lowerCamelCase       :       List[Any]           = (prefix + args[0],)
      __lowerCamelCase       :       Union[str, Any]           = False
    else:
      raise ValueError(
          f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'   )
    __lowerCamelCase       :       Tuple           = self.tokenizer(*_a			,	padding=_a			,	truncation=_a			,	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      :	str			,	*_a      :	int			,	**_a      :	List[str]   )					->					int:
    __lowerCamelCase       :       Optional[int]           = super().__call__(*_a			,	**_a   )
    if (
        isinstance(args[0]			,	_a   )
        and all(isinstance(_a			,	_a   ) for el in args[0]   )
        and all(len(_a   ) == 1 for res in result   )
    ):
      return [res[0] for res in result]
    return result




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Tuple			,	_a      :	int=TruncationStrategy.DO_NOT_TRUNCATE			,	**_a      :	Dict   )					->					List[Any]:
    __lowerCamelCase       :       Union[str, Any]           = self._parse_and_tokenize(_a			,	truncation=_a			,	**_a   )
    return inputs




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Dict			,	**_a      :	Union[str, Any]   )					->					Optional[Any]:
    if self.framework == "pt":
      __lowerCamelCase       ,__lowerCamelCase       :       Any           = model_inputs['input_ids'].shape
    elif self.framework == "tf":
      __lowerCamelCase       ,__lowerCamelCase       :       str           = tf.shape(model_inputs['input_ids']   ).numpy()

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




  def 						_lowercase     ( self      :	List[str]			,	_a      :	List[Any]			,	_a      :	str=ReturnType.TEXT			,	_a      :	Optional[int]=False   )					->					Dict:
    __lowerCamelCase       :       List[str]           = []
    for output_ids in model_outputs["output_ids"][0]:
      if return_type == ReturnType.TENSORS:
        __lowerCamelCase       :       List[str]           = {f'{self.return_name}_token_ids': output_ids}
      elif return_type == ReturnType.TEXT:
        __lowerCamelCase       :       List[str]           = {
            f'{self.return_name}_text': self.tokenizer.decode(
                _a			,	skip_special_tokens=_a			,	clean_up_tokenization_spaces=_a			,	)
        }
      records.append(_a   )
    return records






@add_end_docstrings(SCREAMING_SNAKE_CASE_     )
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""summary"""




  def __call__( self      :	str			,	*_a      :	List[Any]			,	**_a      :	Any   )					->					Dict:
    return super().__call__(*_a			,	**_a   )




  def 						_lowercase     ( self      :	Dict			,	_a      :	int			,	_a      :	int			,	_a      :	int   )					->					bool:
    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(SCREAMING_SNAKE_CASE_     )
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""translation"""




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	int			,	_a      :	int			,	_a      :	int   )					->					int:
    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 						_lowercase     ( self      :	List[str]			,	*_a      :	List[str]			,	_a      :	Any=TruncationStrategy.DO_NOT_TRUNCATE			,	_a      :	int=None			,	_a      :	Tuple=None   )					->					Any:
    if getattr(self.tokenizer			,	'_build_translation_inputs'			,	_a   ):
      return self.tokenizer._build_translation_inputs(
          *_a			,	return_tensors=self.framework			,	truncation=_a			,	src_lang=_a			,	tgt_lang=_a   )
    else:
      return super()._parse_and_tokenize(*_a			,	truncation=_a   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Tuple=None			,	_a      :	int=None			,	**_a      :	Tuple   )					->					List[str]:
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Any           = super()._sanitize_parameters(**_a   )
    if src_lang is not None:
      __lowerCamelCase       :       str           = src_lang
    if tgt_lang is not None:
      __lowerCamelCase       :       int           = tgt_lang
    if src_lang is None and tgt_lang is None:
      # Backward compatibility, direct arguments use is preferred.
      __lowerCamelCase       :       Dict           = kwargs.get('task'			,	self.task   )
      __lowerCamelCase       :       Tuple           = task.split('_'   )
      if task and len(_a   ) == 4:
        # translation, XX, to YY
        __lowerCamelCase       :       Optional[int]           = items[1]
        __lowerCamelCase       :       Optional[Any]           = items[3]
    return preprocess_params, forward_params, postprocess_params




  def __call__( self      :	Dict			,	*_a      :	Tuple			,	**_a      :	Optional[Any]   )					->					Optional[int]:
    return super().__call__(*_a			,	**_a   )











'''simple docstring'''





_UpperCamelCase											=						tuple[float, float, float]
_UpperCamelCase											=						tuple[float, float, float]

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Vectorad:
  __lowerCamelCase       :       Any           = end_pointa[0] - end_pointa[0]
  __lowerCamelCase       :       str           = end_pointa[1] - end_pointa[1]
  __lowerCamelCase       :       Tuple           = end_pointa[2] - end_pointa[2]
  return (x, y, z)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Vectorad:
  __lowerCamelCase       :       List[str]           = ab[1] * ac[2] - ab[2] * ac[1]  # *i
  __lowerCamelCase       :       Dict           = (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
  __lowerCamelCase       :       List[Any]           = ab[0] * ac[1] - ab[1] * ac[0]  # *k
  return (x, y, z)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      bool:
  return tuple(round(_lowerCAmelCase       ,_lowerCAmelCase       ) for x in vector       ) == (0, 0, 0)


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 10       )      ->      bool:
  __lowerCamelCase       :       str           = create_vector(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = create_vector(_lowerCAmelCase       ,_lowerCAmelCase       )
  return is_zero_vector(get_ad_vectors_cross(_lowerCAmelCase       ,_lowerCAmelCase       )       ,_lowerCAmelCase       )






