


"""simple docstring"""


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


_snake_case          =							logging.get_logger(__name__)



class   _a      :
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str = None							,					SCREAMING_SNAKE_CASE__							:					uuid.UUID = None							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None				):
          if not conversation_id:
                lowerCamelCase__           =		uuid.uuida()
          if past_user_inputs is None:
                lowerCamelCase__           =		[]
          if generated_responses is None:
                lowerCamelCase__           =		[]

          lowerCamelCase__           =		conversation_id
          lowerCamelCase__           =		past_user_inputs
          lowerCamelCase__           =		generated_responses
          lowerCamelCase__           =		text
    def __eq__( self							:					int							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if not isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				):
                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							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					bool = False				):
          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}".'				)
                      lowerCamelCase__           =		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:
                lowerCamelCase__           =		text
    def      _UpperCamelCase       ( self							:					Any				):
          if self.new_user_input:
                self.past_user_inputs.append(self.new_user_input				)
          lowerCamelCase__           =		None
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          self.generated_responses.append(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          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							:					List[str]				):
          lowerCamelCase__           =		F'Conversation id: {self.uuid} \n'
          for is_user, text in self.iter_texts():
                lowerCamelCase__           =		'user' if is_user else 'bot'
                output += F'{name} >> {text} \n'
          return output





@add_end_docstrings(
    SCREAMING_SNAKE_CASE_		,		r'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '		,		)
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Union[str, Any]							,					*SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					**SCREAMING_SNAKE_CASE__							:					str				):
          super().__init__(*SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          if self.tokenizer.pad_token_id is None:
                lowerCamelCase__           =		self.tokenizer.eos_token
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					**SCREAMING_SNAKE_CASE__							:					int				):
          lowerCamelCase__           =		{}
          lowerCamelCase__           =		{}
          lowerCamelCase__           =		{}

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

          if "max_length" in generate_kwargs:
                lowerCamelCase__           =		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:
                lowerCamelCase__           =		clean_up_tokenization_spaces

          if generate_kwargs:
                forward_params.update(SCREAMING_SNAKE_CASE__				)
          return preprocess_params, forward_params, postprocess_params
    def __call__( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Union[Conversation, List[Conversation]]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0							,					**SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		super().__call__(SCREAMING_SNAKE_CASE__							,					num_workers=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) == 1:
                return outputs[0]
          return outputs
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Conversation							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=32				):
          if not isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				):
                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'				):
                lowerCamelCase__           =		self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE__				)
          else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                lowerCamelCase__           =		self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE__				)

          if self.framework == "pt":
                lowerCamelCase__           =		torch.LongTensor([input_ids]				)
          elif self.framework == "tf":
                lowerCamelCase__           =		tf.constant([input_ids]				)
          return {"input_ids": input_ids, "conversation": conversation}
    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					int=10							,					**SCREAMING_SNAKE_CASE__							:					List[str]				):
          lowerCamelCase__           =		generate_kwargs.get('max_length'							,					self.model.config.max_length				)

          lowerCamelCase__           =		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})'				)
                lowerCamelCase__           =		max_length - minimum_tokens
                lowerCamelCase__           =		model_inputs['input_ids'][:, -trim:]
                if "attention_mask" in model_inputs:
                      lowerCamelCase__           =		model_inputs['attention_mask'][:, -trim:]
          lowerCamelCase__           =		model_inputs.pop('conversation'				)
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		self.model.generate(**SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          if self.model.config.is_encoder_decoder:
                lowerCamelCase__           =		1
          else:
                lowerCamelCase__           =		n
          return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					int=True				):
          lowerCamelCase__           =		model_outputs['output_ids']
          lowerCamelCase__           =		self.tokenizer.decode(
              output_ids[0]							,					skip_special_tokens=SCREAMING_SNAKE_CASE__							,					clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							,					)
          lowerCamelCase__           =		model_outputs['conversation']
          conversation.mark_processed()
          conversation.append_response(SCREAMING_SNAKE_CASE__				)
          return conversation


    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Conversation				):
          lowerCamelCase__           =		self.tokenizer.eos_token_id
          lowerCamelCase__           =		[]
          for is_user, text in conversation.iter_texts():
                if eos_token_id is not None:
                      input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__				) + [eos_token_id]				)
                else:
                      input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__				)				)

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







"""simple docstring"""


from __future__ import annotations

from math import gcd



def   snake_case						(				_a:     int		,   _a:     int = 2		,   _a:     int = 1		,   _a:     int = 3		,   )->		int | None:
      '''simple docstring'''


      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(_a:     int		,   _a:     int		,   _a:     int							) -> int:
            return (pow(_a		,   2							) + step) % modulus

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

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

                  # 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``.
                  lowerCamelCase__           =		gcd(hare - tortoise		,   _a							)

                  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.
            lowerCamelCase__           =		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

   _snake_case          =							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",
   )
   _snake_case          =							parser.parse_args()

   _snake_case          =							pollard_rho(args.num, attempts=args.attempts)
   if divisor is None:
      print(f"""{args.num} is probably prime""")
   else:
      _snake_case          =							args.num // divisor
      print(f"""{args.num} = {divisor} * {quotient}""")



