
"""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 			_SCREAMING_SNAKE_CASE					(			enum.Enum							):
			UpperCAmelCase_  :List[Any]				 =						0
			UpperCAmelCase_  :int				 =						1




@add_end_docstrings(A__							)
class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Union[str, Any]				 =						"generated"






			def __init__(					self       ,   *__A       ,   **__A       )			->     int:
						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  __lowerCAmelCase (					self       ,   __A=None       ,   __A=None       ,   __A=None       ,   __A=None       ,   __A=None       ,   __A=None       ,   **__A       ,   )			->     str:
						lowerCAmelCase_    :List[str]			     =  {}
						if truncation is not None:
									lowerCAmelCase_    :List[Any]			     =  truncation

						lowerCAmelCase_    :Optional[int]			     =  generate_kwargs

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

						if clean_up_tokenization_spaces is not None:
									lowerCAmelCase_    :str			     =  clean_up_tokenization_spaces

						if stop_sequence is not None:
									lowerCAmelCase_    :int			     =  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_    :Optional[Any]			     =  stop_sequence_ids[0]

						return preprocess_params, forward_params, postprocess_params






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       )			->     Optional[Any]:
						return True






			def  __lowerCAmelCase (					self       ,   *__A       ,   __A       )			->     List[Any]:
						lowerCAmelCase_    :List[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"""       )
									lowerCAmelCase_    :Dict			     =  ([prefix + arg for arg in args[0]],)
									lowerCAmelCase_    :Optional[int]			     =  True

						elif isinstance(args[0]       ,   __A       ):
									lowerCAmelCase_    :Any			     =  (prefix + args[0],)
									lowerCAmelCase_    :Optional[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_    :List[str]			     =  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       ,   *__A       ,   **__A       )			->     Optional[int]:
						lowerCAmelCase_    :Any			     =  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  __lowerCAmelCase (					self       ,   __A       ,   __A=TruncationStrategy.DO_NOT_TRUNCATE       ,   **__A       )			->     Tuple:
						lowerCAmelCase_    :Union[str, Any]			     =  self._parse_and_tokenize(__A       ,   truncation=__A       ,   **__A       )
						return inputs






			def  __lowerCAmelCase (					self       ,   __A       ,   **__A       )			->     str:
						if self.framework == "pt":
									lowerCAmelCase_     ,       lowerCAmelCase_    :List[str]			     =  model_inputs["""input_ids"""].shape
						elif self.framework == "tf":
									lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[int]			     =  tf.shape(model_inputs["""input_ids"""]       ).numpy()

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






			def  __lowerCAmelCase (					self       ,   __A       ,   __A=ReturnType.TEXT       ,   __A=False       )			->     List[str]:
						lowerCAmelCase_    :int			     =  []
						for output_ids in model_outputs["output_ids"][0]:
									if return_type == ReturnType.TENSORS:
												lowerCAmelCase_    :Optional[Any]			     =  {f"""{self.return_name}_token_ids""": output_ids}
									elif return_type == ReturnType.TEXT:
												lowerCAmelCase_    :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(A__							)
class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Any				 =						"summary"






			def __call__(					self       ,   *__A       ,   **__A       )			->     Tuple:
						return super().__call__(*__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       )			->     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(A__							)
class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :List[str]				 =						"translation"






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       )			->     Dict:
						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       ,   *__A       ,   __A=TruncationStrategy.DO_NOT_TRUNCATE       ,   __A=None       ,   __A=None       )			->     Union[str, 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  __lowerCAmelCase (					self       ,   __A=None       ,   __A=None       ,   **__A       )			->     Dict:
						lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :Union[str, Any]			     =  super()._sanitize_parameters(**__A       )
						if src_lang is not None:
									lowerCAmelCase_    :List[str]			     =  src_lang
						if tgt_lang is not None:
									lowerCAmelCase_    :Tuple			     =  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_    :Any			     =  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       ,   *__A       ,   **__A       )			->     str:
						return super().__call__(*__A       ,   **__A       )










from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase		       =							list[list[float | int]]




def    snake_case_     (  snake_case			,							snake_case     )  ->				Matrix:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(size + 1     )] for _ in range(snake_case     )]
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   float

       for row in range(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[Any]  			=   matrix[row][col]

              lowercase__:   Optional[int]  			=   vector[row][0]

       lowercase__:   str  			=   0
       lowercase__:   Any  			=   0
       while row < size and col < size:
              # pivoting
              lowercase__:   List[Any]  			=   max((abs(augmented[rowa][col]     ), rowa) for rowa in range(snake_case			,							snake_case     )     )[
                  1
              ]
              if augmented[pivot_row][col] == 0:
                     col += 1
                     continue
              else:
                     lowercase__			,						lowercase__:   Optional[int]  			=   augmented[pivot_row], augmented[row]

              for rowa in range(row + 1			,							snake_case     ):
                     lowercase__:   Any  			=   augmented[rowa][col] / augmented[row][col]
                     lowercase__:   int  			=   0
                     for cola in range(col + 1			,							size + 1     ):
                            augmented[rowa][cola] -= augmented[row][cola] * ratio

              row += 1
              col += 1

       # back substitution
       for col in range(1			,							snake_case     ):
              for row in range(snake_case     ):
                     lowercase__:   Union[str, Any]  			=   augmented[row][col] / augmented[col][col]
                     for cola in range(snake_case			,							size + 1     ):
                            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
       return [
           [round(augmented[row][size] / augmented[row][row]			,							10     )] for row in range(snake_case     )
       ]




def    snake_case_     (  snake_case     )  ->				Callable[[int], int]:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(snake_case     )] for _ in range(snake_case     )]
       lowercase__:   Matrix  			=   [[0] for _ in range(snake_case     )]
       lowercase__:   Matrix
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int

       for x_val, y_val in enumerate(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[str]  			=   (x_val + 1) ** (size - col - 1)
              lowercase__:   str  			=   y_val

       lowercase__:   Optional[int]  			=   solve(snake_case			,							snake_case     )

       def interpolated_func(snake_case     ) -> int:
              return sum(
                  round(coeffs[x_val][0]     ) * (var ** (size - x_val - 1))
                  for x_val in range(snake_case     )     )

       return interpolated_func




def    snake_case_     (  snake_case     )  ->				int:
       return (
           1
           - variable
           + variable**2
           - variable**3
           + variable**4
           - variable**5
           + variable**6
           - variable**7
           + variable**8
           - variable**9
           + variable**10
       )




def    snake_case_     (  snake_case = question_function			,							snake_case = 10     )  ->				int:
       lowercase__:   list[int]  			=   [func(snake_case     ) for x_val in range(1			,							order + 1     )]

       lowercase__:   list[Callable[[int], int]]  			=   [
           interpolate(data_points[:max_coeff]     ) for max_coeff in range(1			,							order + 1     )
       ]

       lowercase__:   int  			=   0
       lowercase__:   Callable[[int], int]
       lowercase__:   int

       for poly in polynomials:
              lowercase__:   List[str]  			=   1
              while func(snake_case     ) == poly(snake_case     ):
                     x_val += 1

              ret += poly(snake_case     )

       return ret


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


