




"""simple docstring"""

import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


_lowerCamelCase  :			int        =       logging.get_logger(__name__)


_lowerCamelCase  :			int        =       r'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'

class        lowercase      (    __UpperCAmelCase):







				@add_start_docstrings(__lowerCAmelCase       )
				def __call__(							self   :					str      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Dict      ,     **_lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

					raise NotImplementedError('''StoppingCriteria needs to be subclassed'''       )

class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					Dict      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					List[str] = None       ):



					"""simple docstring"""

					A_  :				int	   =		max_length
					A_  :				List[Any]	   =		max_position_embeddings







				@add_start_docstrings(__lowerCAmelCase       )
				def __call__(							self   :					Optional[Any]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					List[Any]      ,     **_lowerCamelCase   :					List[str]       ):



					"""simple docstring"""

					A_  :				str	   =		input_ids.shape[-1]
					A_  :				List[str]	   =		cur_len >= self.max_length
					if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
						logger.warning_once(
						    '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
						    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
						    '''exceptions, performance degradation, or nothing at all.'''       )
					return is_done

class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					Tuple      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

					warnings.warn(
					    '''The class `MaxNewTokensCriteria` is deprecated. '''
					    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
					    '''with `max_length = start_length + max_new_tokens` instead.'''      ,     __lowerCAmelCase      ,     )
					A_  :				Optional[Any]	   =		start_length
					A_  :				Any	   =		max_new_tokens
					A_  :				List[str]	   =		start_length + max_new_tokens







				@add_start_docstrings(__lowerCAmelCase       )
				def __call__(							self   :					List[str]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[Any]      ,     **_lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					return input_ids.shape[-1] >= self.max_length

class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					Optional[int]      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Union[str, Any] = None       ):



					"""simple docstring"""

					A_  :				List[str]	   =		max_time
					A_  :				Union[str, Any]	   =		time.time() if initial_timestamp is None else initial_timestamp







				@add_start_docstrings(__lowerCAmelCase       )
				def __call__(							self   :					Union[str, Any]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					Any      ,     **_lowerCamelCase   :					Union[str, Any]       ):



					"""simple docstring"""

					return time.time() - self.initial_timestamp > self.max_time

class        lowercase      (    __UpperCAmelCase):







				@add_start_docstrings(__lowerCAmelCase       )
				def __call__(							self   :					Optional[Any]      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					str      ,     **_lowerCamelCase   :					List[Any]       ):



					"""simple docstring"""

					return any(criteria(__lowerCAmelCase      ,     __lowerCAmelCase       ) for criteria in self       )







				@property
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					for stopping_criterium in self:
						if isinstance(__lowerCAmelCase      ,     __lowerCAmelCase       ):
							return stopping_criterium.max_length
						elif isinstance(__lowerCAmelCase      ,     __lowerCAmelCase       ):
							return stopping_criterium.max_length
					return None

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Any	   =		stopping_criteria.max_length
	A_  :				Tuple	   =		deepcopy(__snake_case	)
	if stopping_max_length is not None and stopping_max_length != max_length:
		warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter'''  ,      __snake_case	)
	elif stopping_max_length is None:
		new_stopping_criteria.append(MaxLengthCriteria(max_length=__snake_case	)	)
	return new_stopping_criteria












"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

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

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

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


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       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)))





