

"""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   __lowercase      (     enum.Enum       ):
  snake_case_												=							0
  snake_case_												=							1







@add_end_docstrings(__lowerCamelCase       )
class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""generated"""

  def __init__(	self		:      List[str]       ,*A		:      Tuple       ,**A		:      str     ):






   '''simple docstring'''
   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		:      Optional[Any]       ,A		:      List[Any]=None       ,A		:      Any=None       ,A		:      Optional[int]=None       ,A		:      Union[str, Any]=None       ,A		:      Optional[Any]=None       ,A		:      Optional[Any]=None       ,**A		:      str       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       {}
   if truncation is not None:
    UpperCAmelCase__       :	Any        =       truncation

   UpperCAmelCase__       :	Union[str, Any]        =       generate_kwargs

   UpperCAmelCase__       :	Dict        =       {}
   if return_tensors is not None and return_type is None:
    UpperCAmelCase__       :	Optional[Any]        =       ReturnType.TENSORS if return_tensors else ReturnType.TEXT
   if return_type is not None:
    UpperCAmelCase__       :	Optional[int]        =       return_type

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

   if stop_sequence is not None:
    UpperCAmelCase__       :	List[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."""     )
    UpperCAmelCase__       :	Union[str, Any]        =       stop_sequence_ids[0]

   return preprocess_params, forward_params, postprocess_params

  def  __lowercase							(	self		:      Dict       ,A		:      int       ,A		:      int       ,A		:      int     ):






   '''simple docstring'''
   return True

  def  __lowercase							(	self		:      List[Any]       ,*A		:      Dict       ,A		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       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"""     )
    UpperCAmelCase__       :	Union[str, Any]        =       ([prefix + arg for arg in args[0]],)
    UpperCAmelCase__       :	int        =       True

   elif isinstance(args[0]       ,A     ):
    UpperCAmelCase__       :	Optional[Any]        =       (prefix + args[0],)
    UpperCAmelCase__       :	List[Any]        =       False
   else:
    raise ValueError(
        f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"     )
   UpperCAmelCase__       :	Dict        =       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		:      Dict       ,*A		:      str       ,**A		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       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		:      Dict       ,A		:      Any       ,A		:      str=TruncationStrategy.DO_NOT_TRUNCATE       ,**A		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       self._parse_and_tokenize(A       ,truncation=A       ,**A     )
   return inputs

  def  __lowercase							(	self		:      Any       ,A		:      List[str]       ,**A		:      Optional[Any]     ):






   '''simple docstring'''
   if self.framework == "pt":
    UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       model_inputs["""input_ids"""].shape
   elif self.framework == "tf":
    UpperCAmelCase__ ,   UpperCAmelCase__       :	Dict        =       tf.shape(model_inputs["""input_ids"""]     ).numpy()

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




  def  __lowercase							(	self		:      List[Any]       ,A		:      Any       ,A		:      str=ReturnType.TEXT       ,A		:      Any=False     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       []
   for output_ids in model_outputs["output_ids"][0]:
    if return_type == ReturnType.TENSORS:
     UpperCAmelCase__       :	Optional[Any]        =       {f"{self.return_name}_token_ids": output_ids}
    elif return_type == ReturnType.TEXT:
     UpperCAmelCase__       :	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(__lowerCamelCase       )
class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""summary"""

  def __call__(	self		:      Optional[Any]       ,*A		:      Any       ,**A		:      Optional[Any]     ):






   '''simple docstring'''
   return super().__call__(*A       ,**A     )




  def  __lowercase							(	self		:      Union[str, Any]       ,A		:      int       ,A		:      int       ,A		:      int     ):






   '''simple docstring'''
   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(__lowerCamelCase       )
class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""translation"""

  def  __lowercase							(	self		:      List[str]       ,A		:      int       ,A		:      int       ,A		:      int     ):






   '''simple docstring'''
   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		:      int       ,*A		:      Any       ,A		:      Tuple=TruncationStrategy.DO_NOT_TRUNCATE       ,A		:      str=None       ,A		:      Optional[int]=None     ):






   '''simple docstring'''
   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		:      Union[str, Any]       ,A		:      Dict=None       ,A		:      Union[str, Any]=None       ,**A		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       super()._sanitize_parameters(**A     )
   if src_lang is not None:
    UpperCAmelCase__       :	List[str]        =       src_lang
   if tgt_lang is not None:
    UpperCAmelCase__       :	List[Any]        =       tgt_lang
   if src_lang is None and tgt_lang is None:
    # Backward compatibility, direct arguments use is preferred.
    UpperCAmelCase__       :	Optional[Any]        =       kwargs.get("""task"""       ,self.task     )
    UpperCAmelCase__       :	Any        =       task.split("""_"""     )
    if task and len(A     ) == 4:
     # translation, XX, to YY
     UpperCAmelCase__       :	str        =       items[1]
     UpperCAmelCase__       :	Union[str, Any]        =       items[3]
   return preprocess_params, forward_params, postprocess_params




  def __call__(	self		:      List[str]       ,*A		:      int       ,**A		:      Union[str, Any]     ):






   '''simple docstring'''
   return super().__call__(*A       ,**A     )








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 100   ):
      '''simple docstring'''







      A_							       =		0
      A_							       =		0
      for i in range(1		,				n + 1   ):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
      print(f'{solution() = }')





