


"""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    ):
      __magic_name__  :				List[Any] 							=					0
      __magic_name__  :				List[Any] 							=					1

@add_end_docstrings(__A    )
class 			lowercase_	(			__A    ):
      __magic_name__  :				Dict 							=					'generated'







      def __init__(   self			:				str		,	*_lowercase			:				List[Any]		,	**_lowercase			:				Any     ):
        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			:				str		,	_lowercase			:				Tuple=None		,	_lowercase			:				Optional[int]=None		,	_lowercase			:				str=None		,	_lowercase			:				List[str]=None		,	_lowercase			:				List[Any]=None		,	_lowercase			:				str=None		,	**_lowercase			:				Dict		,	):
        lowerCAmelCase__     :     Union[str, Any]										=       {}
        if truncation is not None:
          lowerCAmelCase__     :     Optional[int]										=       truncation

        lowerCAmelCase__     :     Optional[Any]										=       generate_kwargs

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

        if clean_up_tokenization_spaces is not None:
          lowerCAmelCase__     :     Tuple										=       clean_up_tokenization_spaces

        if stop_sequence is not None:
          lowerCAmelCase__     :     List[str]										=       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."     )
          lowerCAmelCase__     :     Dict										=       stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params







      def 					_lowerCAmelCase							(   self			:				Optional[int]		,	_lowercase			:				Optional[int]		,	_lowercase			:				Optional[Any]		,	_lowercase			:				Any     ):
        return True







      def 					_lowerCAmelCase							(   self			:				int		,	*_lowercase			:				Tuple		,	_lowercase			:				Tuple     ):
        lowerCAmelCase__     :     Dict										=       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"     )
          lowerCAmelCase__     :     List[str]										=       ([prefix + arg for arg in args[0]],)
          lowerCAmelCase__     :     Any										=       True

        elif isinstance(args[0]		,	snake_case_     ):
          lowerCAmelCase__     :     Any										=       (prefix + args[0],)
          lowerCAmelCase__     :     Optional[int]										=       False
        else:
          raise ValueError(
              f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"     )
        lowerCAmelCase__     :     int										=       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			:				List[str]		,	*_lowercase			:				Tuple		,	**_lowercase			:				Optional[int]     ):
        lowerCAmelCase__     :     Optional[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			:				Any		,	_lowercase			:				Union[str, Any]		,	_lowercase			:				Dict=TruncationStrategy.DO_NOT_TRUNCATE		,	**_lowercase			:				List[Any]     ):
        lowerCAmelCase__     :     Optional[int]										=       self._parse_and_tokenize(snake_case_		,	truncation=snake_case_		,	**snake_case_     )
        return inputs







      def 					_lowerCAmelCase							(   self			:				int		,	_lowercase			:				List[Any]		,	**_lowercase			:				Any     ):


        if self.framework == "pt":
          lowerCAmelCase__     :     Union[str, Any]										=       model_inputs["""input_ids"""].shape
        elif self.framework == "tf":
          lowerCAmelCase__     :     Any										=       tf.shape(model_inputs["input_ids"]     ).numpy()

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







      def 					_lowerCAmelCase							(   self			:				Dict		,	_lowercase			:				Union[str, Any]		,	_lowercase			:				Any=ReturnType.TEXT		,	_lowercase			:				Optional[Any]=False     ):
        lowerCAmelCase__     :     int										=       []
        for output_ids in model_outputs["output_ids"][0]:
          if return_type == ReturnType.TENSORS:
            lowerCAmelCase__     :     Tuple										=       {f"{self.return_name}_token_ids": output_ids}
          elif return_type == ReturnType.TEXT:
            lowerCAmelCase__     :     int										=       {
                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(__A    )
class 			lowercase_	(			__A    ):
      __magic_name__  :				Union[str, Any] 							=					'summary'







      def __call__(   self			:				Any		,	*_lowercase			:				List[str]		,	**_lowercase			:				Optional[Any]     ):
        return super().__call__(*snake_case_		,	**snake_case_     )







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]		,	_lowercase			:				int		,	_lowercase			:				Dict		,	_lowercase			:				Dict     ):


        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 			lowercase_	(			__A    ):
      __magic_name__  :				Optional[Any] 							=					'translation'







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]		,	_lowercase			:				Tuple		,	_lowercase			:				Optional[int]		,	_lowercase			:				Tuple     ):


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







      def __call__(   self			:				List[str]		,	*_lowercase			:				Optional[Any]		,	**_lowercase			:				Any     ):
        return super().__call__(*snake_case_		,	**snake_case_     )

"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile

def  _A(		lowerCAmelCase							):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5			,  int(math.sqrt(lowerCAmelCase							) + 1							)			,  6							):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def  _A(		):
							A__       :     List[str]								=			2
							while True:
														if is_prime(lowerCAmelCase							):
																					yield num
														num += 1

def  _A(		lowerCAmelCase = 200_0000							):
							return sum(takewhile(lambda lowerCAmelCase							: x < n			,  prime_generator()							)							)


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





