





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						=		logging.get_logger(__name__)





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str = None		, _SCREAMING_SNAKE_CASE						:		uuid.UUID = None		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		Any=None		)->	Union[str, Any]:
			if not conversation_id:
					lowerCAmelCase__       :      Optional[Any]   					= uuid.uuida()
			if past_user_inputs is None:
					lowerCAmelCase__       :      int   					= []
			if generated_responses is None:
					lowerCAmelCase__       :      int   					= []

			lowerCAmelCase__       :      uuid.UUID   					= conversation_id
			lowerCAmelCase__       :      List[str]   					= past_user_inputs
			lowerCAmelCase__       :      List[str]   					= generated_responses
			lowerCAmelCase__       :      Optional[str]   					= text




	def __eq__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Optional[int]:
			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		)->	Tuple:
			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__       :      int   					= 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__       :      Union[str, Any]   					= text




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Any:
			if self.new_user_input:
					self.past_user_inputs.append(self.new_user_input		)
			lowerCAmelCase__       :      Optional[int]   					= None




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	Optional[int]:
			self.generated_responses.append(_SCREAMING_SNAKE_CASE		)




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





@add_end_docstrings(
    _lowercase					,						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  _a							(    _lowercase):




	def __init__( self						:		List[str]		, *_SCREAMING_SNAKE_CASE						:		Dict		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	Tuple:
			super().__init__(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			if self.tokenizer.pad_token_id is None:
					lowerCAmelCase__       :      Optional[int]   					= self.tokenizer.eos_token




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int=None		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		)->	Optional[int]:
			lowerCAmelCase__       :      List[str]   					= {}
			lowerCAmelCase__       :      Dict   					= {}
			lowerCAmelCase__       :      List[Any]   					= {}

			if min_length_for_response is not None:
					lowerCAmelCase__       :      int   					= min_length_for_response
			if minimum_tokens is not None:
					lowerCAmelCase__       :      List[str]   					= minimum_tokens

			if "max_length" in generate_kwargs:
					lowerCAmelCase__       :      Optional[int]   					= 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__       :      Dict   					= clean_up_tokenization_spaces

			if generate_kwargs:
					forward_params.update(_SCREAMING_SNAKE_CASE		)
			return preprocess_params, forward_params, postprocess_params




	def __call__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Union[Conversation, List[Conversation]]		, _SCREAMING_SNAKE_CASE						:		Any=0		, **_SCREAMING_SNAKE_CASE						:		Any		)->	Dict:
			lowerCAmelCase__       :      Dict   					= 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						:		Union[str, Any]=32		)->	Dict[str, Any]:
			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__       :      int   					= self.tokenizer._build_conversation_input_ids(_SCREAMING_SNAKE_CASE		)
			else:
					# If the tokenizer cannot handle conversations, we default to only the old version
					lowerCAmelCase__       :      List[Any]   					= self._legacy_parse_and_tokenize(_SCREAMING_SNAKE_CASE		)

			if self.framework == "pt":
					lowerCAmelCase__       :      str   					= torch.LongTensor([input_ids]		)
			elif self.framework == "tf":
					lowerCAmelCase__       :      str   					= tf.constant([input_ids]		)
			return {"input_ids": input_ids, "conversation": conversation}




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		str=10		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	List[Any]:
			lowerCAmelCase__       :      Optional[Any]   					= generate_kwargs.get('''max_length'''		, self.model.config.max_length		)

			lowerCAmelCase__       :      int   					= 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__       :      Optional[Any]   					= max_length - minimum_tokens
					lowerCAmelCase__       :      Optional[Any]   					= model_inputs['''input_ids'''][:, -trim:]
					if "attention_mask" in model_inputs:
							lowerCAmelCase__       :      str   					= model_inputs['''attention_mask'''][:, -trim:]
			lowerCAmelCase__       :      Optional[Any]   					= model_inputs.pop('''conversation'''		)
			lowerCAmelCase__       :      Any   					= max_length
			lowerCAmelCase__       :      Optional[int]   					= self.model.generate(**_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			if self.model.config.is_encoder_decoder:
					lowerCAmelCase__       :      Any   					= 1
			else:
					lowerCAmelCase__       :      Dict   					= n
			return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any=True		)->	List[Any]:
			lowerCAmelCase__       :      List[Any]   					= model_outputs['''output_ids''']
			lowerCAmelCase__       :      List[str]   					= self.tokenizer.decode(
			    output_ids[0]		, skip_special_tokens=_SCREAMING_SNAKE_CASE		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		, )
			lowerCAmelCase__       :      Union[str, Any]   					= model_outputs['''conversation''']
			conversation.mark_processed()
			conversation.append_response(_SCREAMING_SNAKE_CASE		)
			return conversation




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Conversation		)->	Dict:
			lowerCAmelCase__       :      List[Any]   					= self.tokenizer.eos_token_id
			lowerCAmelCase__       :      Dict   					= []
			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__       :      Union[str, Any]   					= input_ids[-self.tokenizer.model_max_length :]
			return input_ids








import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_   (	_a     ,       _a=1_000	):




		"""simple docstring"""






		if n < 2:
				return False

		if n % 2 == 0:
				return n == 2

		# this means n is odd
		lowerCAmelCase__       :      int   					= n - 1
		lowerCAmelCase__       :      Any   					= 0
		while d % 2 == 0:
				d /= 2
				exp += 1

		# n - 1=d*(2**exp)
		lowerCAmelCase__       :      Optional[Any]   					= 0
		while count < prec:
				lowerCAmelCase__       :      Optional[Any]   					= random.randint(2     ,       n - 1	)
				lowerCAmelCase__       :      List[Any]   					= bin_exp_mod(_a     ,       _a     ,       _a	)
				if b != 1:
						lowerCAmelCase__       :      Dict   					= True
						for _ in range(_a	):
								if b == n - 1:
										lowerCAmelCase__       :      Union[str, Any]   					= False
										break
								lowerCAmelCase__       :      Tuple   					= b * b
								b %= n
						if flag:
								return False
						count += 1
		return True


if __name__ == "__main__":
		lowerCamelCase						=		abs(int(input('''Enter bound : ''').strip()))
		print('''Here\'s the list of primes:''')
		print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))




