





from __future__ import annotations





def lowerCAmelCase__(__snake_case			,__snake_case			,__snake_case			,__snake_case				)							-> Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__														=						[]
	lowerCamelCase__														=						input_list[low:mid], input_list[mid : high + 1]
	while left and right:
		result.append((left if left[0] <= right[0] else right).pop(0				)				)
	lowerCamelCase__														=						result + left + right
	return input_list





def lowerCAmelCase__(__snake_case				)							-> List[Any]:







	'''simple docstring'''







	if len(A__				) <= 1:
		return input_list
	lowerCamelCase__														=						list(A__				)

	# iteration for two-way merging
	lowerCamelCase__														=						2
	while p <= len(A__				):
		# getting low, high and middle value for merge-sort of single list
		for i in range(0			,len(A__				)			,A__				):
			lowerCamelCase__														=						i
			lowerCamelCase__														=						i + p - 1
			lowerCamelCase__														=						(low + high + 1) // 2
			lowerCamelCase__														=						merge(A__			,A__			,A__			,A__				)
		# final merge of last two parts
		if p * 2 >= len(A__				):
			lowerCamelCase__														=						i
			lowerCamelCase__														=						merge(A__			,0			,A__			,len(A__				) - 1				)
			break
		p *= 2

	return input_list


if __name__ == "__main__":
			_a							= input("Enter numbers separated by a comma:\n").strip()
			if user_input == "":
						_a							= []
			else:
						_a							= [int(item.strip()) for item in user_input.split(",")]
			print(iter_merge_sort(unsorted))







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__			(				A__,      A__,      A__,      A__,      A__=True,      A__="pt"				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					{'add_prefix_space': True} if isinstance(A__,      A__				) and not line.startswith(' '				) else {}
			SCREAMING_SNAKE_CASE_		:     Dict					=					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__			(				A__,      A__,      A__=None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__="train"			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=""			,						):





										"""simple docstring"""




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






							def __len__(      self       ):





										"""simple docstring"""




										return len(self.src_lens       )






							def __getitem__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					index + 1  # linecache starts at 1
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prefix + linecache.getline(str(self.src_file       )			,						lowerCAmelCase__       ).rstrip('\n'       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					linecache.getline(str(self.tgt_file       )			,						lowerCAmelCase__       ).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			,						lowerCAmelCase__       ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										SCREAMING_SNAKE_CASE_		:     Any					=					(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.tokenizer.generator if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer

										SCREAMING_SNAKE_CASE_		:     Dict					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_source_length			,						'right'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_target_length			,						'right'       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					source_inputs['input_ids'].squeeze()
										SCREAMING_SNAKE_CASE_		:     Dict					=					target_inputs['input_ids'].squeeze()
										SCREAMING_SNAKE_CASE_		:     str					=					source_inputs['attention_mask'].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}






							@staticmethod
							def 					UpperCamelCase__     (      lowerCAmelCase__       ):





										"""simple docstring"""




										return [len(lowerCAmelCase__       ) for x in Path(lowerCAmelCase__       ).open().readlines()]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					torch.stack([x['input_ids'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     int					=					torch.stack([x['attention_mask'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.stack([x['decoder_input_ids'] for x in batch]       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					(
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer			,						lowerCAmelCase__       )
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					(
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer			,						lowerCAmelCase__       )
										    else self.tokenizer.pad_token_id
										)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					trim_batch(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					trim_batch(lowerCAmelCase__			,						lowerCAmelCase__			,						attention_mask=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{
										    'input_ids': source_ids,
										    'attention_mask': source_mask,
										    'decoder_input_ids': y,
										}
										return batch







lowerCAmelCase__			:      Tuple								=getLogger(__name__)






def 						a__			(				A__				):
			return list(itertools.chain.from_iterable(A__				)				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_git_info()
			save_json(A__,      os.path.join(A__,      'git_log.json'				)				)






def 						a__			(				A__,      A__,      A__=4,      **A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__,      indent=A__,      **A__				)






def 						a__			(				A__				):
			with open(A__				) as f:
						return json.load(A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					git.Repo(search_parent_directories=A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					{
			    '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__			(				A__,      A__				):
			return list(map(A__,      A__				)				)






def 						a__			(				A__,      A__				):
			with open(A__,      'wb'				) as f:
						return pickle.dump(A__,      A__				)






def 						a__			(				A__				):

			def remove_articles(A__				):
						return re.sub(r'\b(a|an|the)\b',      ' ',      A__				)

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

			def remove_punc(A__				):
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					set(string.punctuation				)
						return "".join(ch for ch in text if ch not in exclude				)

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

			return white_space_fix(remove_articles(remove_punc(lower(A__				)				)				)				)






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					normalize_answer(A__				).split()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					normalize_answer(A__				).split()
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					Counter(A__				) & Counter(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					sum(common.values()				)
			if num_same == 0:
						return 0
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1.0 * num_same / len(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					1.0 * num_same / len(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					(2 * precision * recall) / (precision + recall)
			return fa






def 						a__			(				A__,      A__				):
			return normalize_answer(A__				) == normalize_answer(A__				)






def 						a__			(				A__,      A__				):
			assert len(A__				) == len(A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					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__			(				A__				):
			return model_prefix.startswith('rag'				)






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					'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
									SCREAMING_SNAKE_CASE_		:     Any					=					p if hasattr(A__,      A__				) else equivalent_param[p]
									setattr(A__,      A__,      getattr(A__,      A__				)				)
									delattr(A__,      A__				)
			return hparams, config





