





import uuid
from typing import Any, Dict, List, Optional, Union

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

if is_torch_available():
  import torch


__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)






class        UpperCAmelCase     :
 def __init__(    self				:Dict						,	lowercase_				:str = None						,	lowercase_				:uuid.UUID = None						,	lowercase_				:List[str]=None						,	lowercase_				:Optional[int]=None							)->							List[Any]:
     if not conversation_id:
         A__    =      uuid.uuida()
     if past_user_inputs is None:
         A__    =      []
     if generated_responses is None:
         A__    =      []

     A__    =      conversation_id
     A__    =      past_user_inputs
     A__    =      generated_responses
     A__    =      text
 def __eq__(    self				:Union[str, Any]						,	lowercase_				:Tuple							)->							Tuple:
     if not isinstance(lowercase_						,	lowercase_							):
         return False
     if self.uuid == other.uuid:
         return True
     return (
         self.new_user_input == other.new_user_input
         and self.past_user_inputs == other.past_user_inputs
         and self.generated_responses == other.generated_responses
     )
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:str						,	lowercase_				:bool = False							)->							Optional[int]:
     if self.new_user_input:
         if overwrite:
             logger.warning(
                 F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
                 F"with: \"{text}\"."							)
             A__    =      text
         else:
             logger.warning(
                 F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
                 F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"							)
     else:
         A__    =      text
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[Any]:
     if self.new_user_input:
         self.past_user_inputs.append(self.new_user_input							)
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str							)->							List[Any]:
     self.generated_responses.append(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Dict:
     for user_input, generated_response in zip(self.past_user_inputs						,	self.generated_responses							):
         yield True, user_input
         yield False, generated_response
     if self.new_user_input:
         yield True, self.new_user_input



 def __repr__(    self				:Union[str, Any]							)->							Optional[Any]:
     A__    =      F"Conversation id: {self.uuid} \n"
     for is_user, text in self.iter_texts():
         A__    =      "user" if is_user else "bot"
         output += F"{name} >> {text} \n"
     return output






@add_end_docstrings(
    UpperCamelCase__      ,	r"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """      ,	)
class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Tuple						,	*lowercase_				:Dict						,	**lowercase_				:Optional[Any]							)->							Dict:
     super().__init__(*lowercase_						,	**lowercase_							)
     if self.tokenizer.pad_token_id is None:
         A__    =      self.tokenizer.eos_token
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Optional[int]=None						,	lowercase_				:List[str]=None						,	lowercase_				:List[str]=None						,	**lowercase_				:str							)->							str:
     A__    =      {}
     A__    =      {}
     A__    =      {}

     if min_length_for_response is not None:
         A__    =      min_length_for_response
     if minimum_tokens is not None:
         A__    =      minimum_tokens

     if "max_length" in generate_kwargs:
         A__    =      generate_kwargs["max_length"]
         # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
     if clean_up_tokenization_spaces is not None:
         A__    =      clean_up_tokenization_spaces

     if generate_kwargs:
         forward_params.update(lowercase_							)
     return preprocess_params, forward_params, postprocess_params
 def __call__(    self				:Optional[int]						,	lowercase_				:Union[Conversation, List[Conversation]]						,	lowercase_				:List[Any]=0						,	**lowercase_				:Union[str, Any]							)->							Dict:
     A__    =      super().__call__(lowercase_						,	num_workers=lowercase_						,	**lowercase_							)
     if isinstance(lowercase_						,	lowercase_							) and len(lowercase_							) == 1:
         return outputs[0]
     return outputs
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Conversation						,	lowercase_				:Optional[int]=32							)->							Dict[str, Any]:
     if not isinstance(lowercase_						,	lowercase_							):
         raise ValueError("ConversationalPipeline, expects Conversation as inputs"							)
     if conversation.new_user_input is None:
         raise ValueError(
             F"Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. "
             "Add user inputs with the conversation's `add_user_input` method"							)
     if hasattr(self.tokenizer						,	"_build_conversation_input_ids"							):
         A__    =      self.tokenizer._build_conversation_input_ids(lowercase_							)
     else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         A__    =      self._legacy_parse_and_tokenize(lowercase_							)

     if self.framework == "pt":
         A__    =      torch.LongTensor([input_ids]							)
     elif self.framework == "tf":
         A__    =      tf.constant([input_ids]							)
     return {"input_ids": input_ids, "conversation": conversation}
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Union[str, Any]=10						,	**lowercase_				:Dict							)->							str:
     A__    =      generate_kwargs.get("max_length"						,	self.model.config.max_length							)

     A__    =      model_inputs["input_ids"].shape[1]
     if max_length - minimum_tokens < n:
         logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"							)
         A__    =      max_length - minimum_tokens
         A__    =      model_inputs["input_ids"][:, -trim:]
         if "attention_mask" in model_inputs:
             A__    =      model_inputs["attention_mask"][:, -trim:]
     A__    =      model_inputs.pop("conversation"							)
     A__    =      max_length
     A__    =      self.model.generate(**lowercase_						,	**lowercase_							)
     if self.model.config.is_encoder_decoder:
         A__    =      1
     else:
         A__    =      n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Any						,	lowercase_				:str=True							)->							Tuple:
     A__    =      model_outputs["output_ids"]
     A__    =      self.tokenizer.decode(
         output_ids[0]						,	skip_special_tokens=lowercase_						,	clean_up_tokenization_spaces=lowercase_						,	)
     A__    =      model_outputs["conversation"]
     conversation.mark_processed()
     conversation.append_response(lowercase_							)
     return conversation



 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Conversation							)->							Dict:
     A__    =      self.tokenizer.eos_token_id
     A__    =      []
     for is_user, text in conversation.iter_texts():
         if eos_token_id is not None:
             input_ids.extend(self.tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							) + [eos_token_id]							)
         else:
             input_ids.extend(self.tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     if len(lowercase_							) > self.tokenizer.model_max_length:
         A__    =      input_ids[-self.tokenizer.model_max_length :]
     return input_ids










'''simple docstring'''

from __future__ import annotations

from math import gcd





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int = 2						,	_lowerCamelCase : int = 1						,	_lowerCamelCase : int = 3						,	):
    # A value less than 2 can cause an infinite loop in the algorithm.
    if num < 2:
        raise ValueError("The input value cannot be less than 2"	)

    # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
    # algorithm struggles to find factors that are divisible by two.
    # As a workaround, we specifically check for two and even inputs.
    #   See: https://math.stackexchange.com/a/2856214/165820
    if num > 2 and num % 2 == 0:
        return 2

    # Pollard's Rho algorithm requires a function that returns pseudorandom
    # values between 0 <= X < ``num``.  It doesn't need to be random in the
    # sense that the output value is cryptographically secure or difficult
    # to calculate, it only needs to be random in the sense that all output
    # values should be equally likely to appear.
    # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
    # However, the success of Pollard's algorithm isn't guaranteed and is
    # determined in part by the initial seed and the chosen random function.
    # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
    # where ``C`` is a value that we can modify between each attempt.
    def rand_fn(_lowerCamelCase : int						,	_lowerCamelCase : int						,	_lowerCamelCase : int	) -> int:
        return (pow(_lowerCamelCase						,	2	) + step) % modulus

    for _ in range(_lowerCamelCase	):
        # These track the position within the cycle detection logic.
        A__    =      seed
        A__    =      seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

            # At some point both the tortoise and the hare will enter a cycle whose
            # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
            # the tortoise and hare will end up on the same value modulo ``p``.
            # We can detect when this happens because the position difference between
            # the tortoise and the hare will share a common divisor with ``num``.
            A__    =      gcd(hare - tortoise						,	_lowerCamelCase	)

            if divisor == 1:
                # No common divisor yet, just keep searching.
                continue
            else:
                # We found a common divisor!
                if divisor == num:
                    # Unfortunately, the divisor is ``num`` itself and is useless.
                    break
                else:
                    # The divisor is a nontrivial factor of ``num``!
                    return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
        A__    =      hare

        # The new step value for the random function can just be incremented.
        # At first the results will be similar to what the old function would
        # have produced, but the value will quickly diverge after a bit.
        step += 1

    # We haven't found a divisor within the requested number of attempts.
    # We were unlucky or ``num`` itself is actually prime.
    return None


if __name__ == "__main__":
  import argparse

  __lowerCAmelCase							:     Optional[int]		     =argparse.ArgumentParser()
  parser.add_argument(
      "num",
      type=int,
      help="The value to find a divisor of",
  )
  parser.add_argument(
      "--attempts",
      type=int,
      default=3,
      help="The number of attempts before giving up",
  )
  __lowerCAmelCase							:     Optional[int]		     =parser.parse_args()

  __lowerCAmelCase							:     Dict		     =pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(f"""{args.num} is probably prime""")
  else:
    __lowerCAmelCase							:     Optional[Any]		     =args.num // divisor
    print(f"""{args.num} = {divisor} * {quotient}""")



