



"""simple docstring"""







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCAmelCase:  str			     =				logging.get_logger(__name__)





@dataclass
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str
	SCREAMING_SNAKE_CASE_							:       str
	SCREAMING_SNAKE_CASE_							:       Optional[str]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[str]						       =  None






	def    lowerCamelCase__					(     self   ):
			return json.dumps(dataclasses.asdict(self   )				,indent=2   ) + "\n"







@dataclass(frozen=snake_case       )
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[int]
	SCREAMING_SNAKE_CASE_							:       Optional[List[int]]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[List[int]]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[Union[int, float]]						       =  None






	def    lowerCamelCase__					(     self   ):
			return json.dumps(dataclasses.asdict(self   )   ) + "\n"







class   UpperCamelCase	:




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if len(self.get_labels()   ) > 1:
					_lowercase				: Optional[int]     =   self.get_labels()[int(example.label   )]
			return example






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			with open(UpperCAmelCase_				,"""r"""				,encoding="""utf-8-sig"""   ) as f:
					return list(csv.reader(UpperCAmelCase_				,delimiter="""\t"""				,quotechar=UpperCAmelCase_   )   )







class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_="classification"				,UpperCAmelCase_=False   ):
			_lowercase				: int     =   [] if labels is None else labels
			_lowercase				: int     =   [] if examples is None else examples
			_lowercase				: Union[str, Any]     =   mode
			_lowercase				: Optional[Any]     =   verbose






	def __len__(     self   ):
			return len(self.examples   )






	def __getitem__(     self				,UpperCAmelCase_   ):
			if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
					return SingleSentenceClassificationProcessor(labels=self.labels				,examples=self.examples[idx]   )
			return self.examples[idx]






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=""				,UpperCAmelCase_=0				,UpperCAmelCase_=1				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,**UpperCAmelCase_   ):
			_lowercase				: int     =   cls(**UpperCAmelCase_   )
			processor.add_examples_from_csv(
			    UpperCAmelCase_				,split_name=UpperCAmelCase_				,column_label=UpperCAmelCase_				,column_text=UpperCAmelCase_				,column_id=UpperCAmelCase_				,skip_first_row=UpperCAmelCase_				,overwrite_labels=UpperCAmelCase_				,overwrite_examples=UpperCAmelCase_				,)
			return processor






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=None				,**UpperCAmelCase_   ):
			_lowercase				: int     =   cls(**UpperCAmelCase_   )
			processor.add_examples(UpperCAmelCase_				,labels=UpperCAmelCase_   )
			return processor






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=""				,UpperCAmelCase_=0				,UpperCAmelCase_=1				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,):
			_lowercase				: Dict     =   self._read_tsv(UpperCAmelCase_   )
			if skip_first_row:
					_lowercase				: Union[str, Any]     =   lines[1:]
			_lowercase				: int     =   []
			_lowercase				: Optional[int]     =   []
			_lowercase				: int     =   []
			for i, line in enumerate(UpperCAmelCase_   ):
					texts.append(line[column_text]   )
					labels.append(line[column_label]   )
					if column_id is not None:
							ids.append(line[column_id]   )
					else:
							_lowercase				: Tuple     =   f"""{split_name}-{i}""" if split_name else str(UpperCAmelCase_   )
							ids.append(UpperCAmelCase_   )

			return self.add_examples(
			    UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,overwrite_labels=UpperCAmelCase_				,overwrite_examples=UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=False   ):
			if labels is not None and len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
					raise ValueError(
					    f"""Text and labels have mismatched lengths {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )
			if ids is not None and len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
					raise ValueError(f"""Text and ids have mismatched lengths {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )
			if ids is None:
					_lowercase				: Any     =   [None] * len(UpperCAmelCase_   )
			if labels is None:
					_lowercase				: List[str]     =   [None] * len(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   []
			_lowercase				: int     =   set()
			for text_or_text_and_label, label, guid in zip(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
					if isinstance(UpperCAmelCase_				,(tuple, list)   ) and label is None:
							_lowercase     ,  _lowercase				: Dict     =   text_or_text_and_label
					else:
							_lowercase				: Optional[int]     =   text_or_text_and_label
					added_labels.add(UpperCAmelCase_   )
					examples.append(InputExample(guid=UpperCAmelCase_				,text_a=UpperCAmelCase_				,text_b=UpperCAmelCase_				,label=UpperCAmelCase_   )   )

			# Update examples
			if overwrite_examples:
					_lowercase				: Any     =   examples
			else:
					self.examples.extend(UpperCAmelCase_   )

			# Update labels
			if overwrite_labels:
					_lowercase				: List[str]     =   list(UpperCAmelCase_   )
			else:
					_lowercase				: Optional[int]     =   list(set(self.labels   ).union(UpperCAmelCase_   )   )

			return self.examples






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=0				,UpperCAmelCase_=True				,UpperCAmelCase_=None				,):
			if max_length is None:
					_lowercase				: Any     =   tokenizer.max_len

			_lowercase				: List[Any]     =   {label: i for i, label in enumerate(self.labels   )}

			_lowercase				: Union[str, Any]     =   []
			for ex_index, example in enumerate(self.examples   ):
					if ex_index % 1_00_00 == 0:
							logger.info(f"""Tokenizing example {ex_index}"""   )

					_lowercase				: Optional[int]     =   tokenizer.encode(
					    example.text_a				,add_special_tokens=UpperCAmelCase_				,max_length=min(UpperCAmelCase_				,tokenizer.max_len   )				,)
					all_input_ids.append(UpperCAmelCase_   )

			_lowercase				: str     =   max(len(UpperCAmelCase_   ) for input_ids in all_input_ids   )

			_lowercase				: Optional[Any]     =   []
			for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase_				,self.examples   )   ):
					if ex_index % 1_00_00 == 0:
							logger.info(f"""Writing example {ex_index}/{len(self.examples   )}"""   )
					# The mask has 1 for real tokens and 0 for padding tokens. Only real
					# tokens are attended to.
					_lowercase				: Any     =   [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase_   )

					# Zero-pad up to the sequence length.
					_lowercase				: Dict     =   batch_length - len(UpperCAmelCase_   )
					if pad_on_left:
							_lowercase				: Dict     =   ([pad_token] * padding_length) + input_ids
							_lowercase				: Any     =   ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
					else:
							_lowercase				: Optional[Any]     =   input_ids + ([pad_token] * padding_length)
							_lowercase				: int     =   attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

					if len(UpperCAmelCase_   ) != batch_length:
							raise ValueError(f"""Error with input length {len(UpperCAmelCase_   )} vs {batch_length}"""   )
					if len(UpperCAmelCase_   ) != batch_length:
							raise ValueError(f"""Error with input length {len(UpperCAmelCase_   )} vs {batch_length}"""   )

					if self.mode == "classification":
							_lowercase				: Dict     =   label_map[example.label]
					elif self.mode == "regression":
							_lowercase				: Optional[int]     =   float(example.label   )
					else:
							raise ValueError(self.mode   )

					if ex_index < 5 and self.verbose:
							logger.info("""*** Example ***"""   )
							logger.info(f"""guid: {example.guid}"""   )
							logger.info(f"""input_ids: {" ".join([str(UpperCAmelCase_   ) for x in input_ids]   )}"""   )
							logger.info(f"""attention_mask: {" ".join([str(UpperCAmelCase_   ) for x in attention_mask]   )}"""   )
							logger.info(f"""label: {example.label} (id = {label})"""   )

					features.append(InputFeatures(input_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,label=UpperCAmelCase_   )   )

			if return_tensors is None:
					return features
			elif return_tensors == "tf":
					if not is_tf_available():
							raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""   )
					import tensorflow as tf

					def gen():
							for ex in features:
									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

					_lowercase				: Tuple     =   tf.data.Dataset.from_generator(
					    UpperCAmelCase_				,({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)				,({"""input_ids""": tf.TensorShape([None]   ), """attention_mask""": tf.TensorShape([None]   )}, tf.TensorShape([]   ))				,)
					return dataset
			elif return_tensors == "pt":
					if not is_torch_available():
							raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""   )
					import torch
					from torch.utils.data import TensorDataset

					_lowercase				: int     =   torch.tensor([f.input_ids for f in features]				,dtype=torch.long   )
					_lowercase				: str     =   torch.tensor([f.attention_mask for f in features]				,dtype=torch.long   )
					if self.mode == "classification":
							_lowercase				: Tuple     =   torch.tensor([f.label for f in features]				,dtype=torch.long   )
					elif self.mode == "regression":
							_lowercase				: Any     =   torch.tensor([f.label for f in features]				,dtype=torch.float   )

					_lowercase				: Any     =   TensorDataset(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
					return dataset
			else:
					raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""   )








"""simple docstring"""







import math
from collections.abc import Generator


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Generator     =   (i for i in range(1   ,				(max_n + 1)      ))
		for i in (n for n in numbers if n > 1):
				for j in range(2   ,				__UpperCAmelCase      ):
						if (i % j) == 0:
								break
				else:
						yield i


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Generator     =   (i for i in range(1   ,				(max_n + 1)      ))
		for i in (n for n in numbers if n > 1):
				# only need to check for factors up to sqrt(i)
				_lowercase				: Union[str, Any]     =   int(math.sqrt(__UpperCAmelCase      )      ) + 1
				for j in range(2   ,				__UpperCAmelCase      ):
						if (i % j) == 0:
								break
				else:
						yield i


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: Generator     =   (i for i in range(1   ,				(max_n + 1)   ,				2      ))
		# It's useless to test even numbers as they will not be prime
		if max_n > 2:
				yield 2  # Because 2 will not be tested, it's necessary to yield it now
		for i in (n for n in numbers if n > 1):
				_lowercase				: str     =   int(math.sqrt(__UpperCAmelCase      )      ) + 1
				for j in range(3   ,				__UpperCAmelCase   ,				2      ):
						# As we removed the even numbers, we don't need them now
						if (i % j) == 0:
								break
				else:
						yield i


def  __SCREAMING_SNAKE_CASE (  ):
		from timeit import timeit

		_lowercase				: Tuple     =   """from __main__ import slow_primes, primes, fast_primes"""
		print(timeit("""slow_primes(1_000_000_000_000)"""   ,				setup=__UpperCAmelCase   ,				number=1000000      )      )
		print(timeit("""primes(1_000_000_000_000)"""   ,				setup=__UpperCAmelCase   ,				number=1000000      )      )
		print(timeit("""fast_primes(1_000_000_000_000)"""   ,				setup=__UpperCAmelCase   ,				number=1000000      )      )


if __name__ == "__main__":
		UpperCAmelCase:  int			     =				int(input("""Calculate primes up to:\n>> """).strip())
		for ret in primes(number):
				print(ret)
		benchmark()



