






'''simple docstring'''





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase=True					,			_lowercase="pt" ):

					'''simple docstring'''







					_A								= {"""add_prefix_space""": True} if isinstance(__A					,			__A ) and not line.startswith(''' ''' ) else {}
					_A								= padding_side
					return tokenizer(
					    [line]					,			max_length=__A					,			padding='''max_length''' if pad_to_max_length else None					,			truncation=__A					,			return_tensors=__A					,			add_special_tokens=__A					,			**__A					,			)




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=None					,			):

					'''simple docstring'''







					_A								= input_ids.ne(__A ).any(dim=0 )
					if attention_mask is None:
										return input_ids[:, keep_column_mask]
					else:
										return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class 						SCREAMING_SNAKE_CASE				( __lowerCAmelCase					):





							"""simple docstring"""

							def __init__(  self:   List[Any]		,     __A:   Optional[int]		,     __A:   List[str]		,     __A:   int		,     __A:   Optional[int]		,     __A:   Any="train"		,     __A:   Union[str, Any]=None		,     __A:   int=None		,     __A:   Dict=None		,     __A:   Any=""		,     )    ->				str:
												super().__init__()
												_A								= Path(_UpperCamelCase   ).joinpath(type_path + '''.source'''   )
												_A								= Path(_UpperCamelCase   ).joinpath(type_path + '''.target'''   )
												_A								= self.get_char_lens(self.src_file   )
												_A								= max_source_length
												_A								= max_target_length
												assert min(self.src_lens   ) > 0, f"""found empty line in {self.src_file}"""
												_A								= tokenizer
												_A								= prefix
												if n_obs is not None:
																	_A								= self.src_lens[:n_obs]
												_A								= src_lang
												_A								= tgt_lang

							def __len__(  self:   List[str]   )    ->				Union[str, Any]:
												return len(self.src_lens   )

							def __getitem__(  self:   Optional[int]		,     __A:   str   )    ->				Dict[str, torch.Tensor]:
												_A								= index + 1  # linecache starts at 1
												_A								= self.prefix + linecache.getline(str(self.src_file   )		,     _UpperCamelCase   ).rstrip('''\n'''   )
												_A								= linecache.getline(str(self.tgt_file   )		,     _UpperCamelCase   ).rstrip('''\n'''   )
												assert source_line, f"""empty source line for index {index}"""
												assert tgt_line, f"""empty tgt line for index {index}"""

												# Need to add eos token manually for T5
												if isinstance(self.tokenizer		,     _UpperCamelCase   ):
																	source_line += self.tokenizer.eos_token
																	tgt_line += self.tokenizer.eos_token

												# Pad source and target to the right
												_A								= (
												    self.tokenizer.question_encoder if isinstance(self.tokenizer		,     _UpperCamelCase   ) else self.tokenizer
												)
												_A								= self.tokenizer.generator if isinstance(self.tokenizer		,     _UpperCamelCase   ) else self.tokenizer

												_A								= encode_line(_UpperCamelCase		,     _UpperCamelCase		,     self.max_source_length		,     '''right'''   )
												_A								= encode_line(_UpperCamelCase		,     _UpperCamelCase		,     self.max_target_length		,     '''right'''   )

												_A								= source_inputs["""input_ids"""].squeeze()
												_A								= target_inputs["""input_ids"""].squeeze()
												_A								= source_inputs["""attention_mask"""].squeeze()
												return {
												    "input_ids": source_ids,
												    "attention_mask": src_mask,
												    "decoder_input_ids": target_ids,
												}

							@staticmethod
							def       __A (  __A:   int   )    ->				Optional[Any]:
												return [len(_UpperCamelCase   ) for x in Path(_UpperCamelCase   ).open().readlines()]



							def       __A (  self:   int		,     __A:   int   )    ->				Dict[str, torch.Tensor]:
												_A								= torch.stack([x['''input_ids'''] for x in batch]   )
												_A								= torch.stack([x['''attention_mask'''] for x in batch]   )
												_A								= torch.stack([x['''decoder_input_ids'''] for x in batch]   )
												_A								= (
												    self.tokenizer.generator.pad_token_id
												    if isinstance(self.tokenizer		,     _UpperCamelCase   )
												    else self.tokenizer.pad_token_id
												)
												_A								= (
												    self.tokenizer.question_encoder.pad_token_id
												    if isinstance(self.tokenizer		,     _UpperCamelCase   )
												    else self.tokenizer.pad_token_id
												)
												_A								= trim_batch(_UpperCamelCase		,     _UpperCamelCase   )
												_A								= trim_batch(_UpperCamelCase		,     _UpperCamelCase		,     attention_mask=_UpperCamelCase   )
												_A								= {
												    """input_ids""": source_ids,
												    """attention_mask""": source_mask,
												    """decoder_input_ids""": y,
												}
												return batch



__A      =				getLogger(__name__)




def      __A					(     _lowercase ):

					'''simple docstring'''







					return list(itertools.chain.from_iterable(__A ) )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= get_git_info()
					save_json(__A					,			os.path.join(__A					,			'''git_log.json''' ) )




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=4					,			**_lowercase ):

					'''simple docstring'''







					with open(__A					,			'''w''' ) as f:
										json.dump(__A					,			__A					,			indent=__A					,			**__A )




def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(__A ) as f:
										return json.load(__A )




def      __A					(     ):

					'''simple docstring'''







					_A								= git.Repo(search_parent_directories=__A )
					_A								= {
					    """repo_id""": str(__A ),
					    """repo_sha""": str(repo.head.object.hexsha ),
					    """repo_branch""": str(repo.active_branch ),
					    """hostname""": str(socket.gethostname() ),
					}
					return repo_infos




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					return list(map(__A					,			__A ) )




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					with open(__A					,			'''wb''' ) as f:
										return pickle.dump(__A					,			__A )




def      __A					(     _lowercase ):

					'''simple docstring'''







					def remove_articles(_lowercase ):
										return re.sub(R'''\b(a|an|the)\b'''					,			''' '''					,			__A )

					def white_space_fix(_lowercase ):
										return " ".join(text.split() )

					def remove_punc(_lowercase ):
										_A								= set(string.punctuation )
										return "".join(ch for ch in text if ch not in exclude )

					def lower(_lowercase ):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A ) ) ) )




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= normalize_answer(__A ).split()
					_A								= normalize_answer(__A ).split()
					_A								= Counter(__A ) & Counter(__A )
					_A								= sum(common.values() )
					if num_same == 0:
										return 0
					_A								= 1.0 * num_same / len(__A )
					_A								= 1.0 * num_same / len(__A )
					_A								= (2 * precision * recall) / (precision + recall)
					return fa




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					return normalize_answer(__A ) == normalize_answer(__A )




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					assert len(__A ) == len(__A )
					_A								= 0
					for hypo, pred in zip(__A					,			__A ):
										em += exact_match_score(__A					,			__A )
					if len(__A ) > 0:
										em /= len(__A )
					return {"em": em}




def      __A					(     _lowercase ):

					'''simple docstring'''







					return model_prefix.startswith('''rag''' )




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= {p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					_A								= """dropout_rate"""
					for p in extra_params:
										if getattr(__A					,			__A					,			__A ):
															if not hasattr(__A					,			__A ) and not hasattr(__A					,			equivalent_param[p] ):
																				logger.info('''config doesn\'t have a `{}` attribute'''.format(__A ) )
																				delattr(__A					,			__A )
																				continue
															_A								= p if hasattr(__A					,			__A ) else equivalent_param[p]
															setattr(__A					,			__A					,			getattr(__A					,			__A ) )
															delattr(__A					,			__A )
					return hparams, config











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



