






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"      )				->						Tuple:



					"""simple docstring"""
					__a					:						str      =					{"add_prefix_space": True} if isinstance(__A				,       __A      ) and not line.startswith(" "      ) else {}
					__a					:						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				,       )				->						Union[str, Any]:



					"""simple docstring"""
					__a					:						Tuple      =					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 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					List[Any] ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Union[str, Any]="train" ,					__UpperCAmelCase:					List[Any]=None ,					__UpperCAmelCase:					Any=None ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					Any="" ,					)    ->     List[str]:




											'''simple docstring'''



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



						def __len__(self:					Dict   )    ->     List[Any]:




											'''simple docstring'''



											return len(self.src_lens   )



						def __getitem__(self:					List[Any] ,					__UpperCAmelCase:					List[str]   )    ->     Dict[str, torch.Tensor]:




											'''simple docstring'''



											__a					:						int      =					index + 1  # linecache starts at 1
											__a					:						Union[str, Any]      =					self.prefix + linecache.getline(str(self.src_file   ) ,					__UpperCAmelCase   ).rstrip("\n"   )
											__a					:						Tuple      =					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					:						Union[str, Any]      =					(
											    self.tokenizer.question_encoder if isinstance(self.tokenizer ,					__UpperCAmelCase   ) else self.tokenizer
											)
											__a					:						str      =					self.tokenizer.generator if isinstance(self.tokenizer ,					__UpperCAmelCase   ) else self.tokenizer

											__a					:						Any      =					encode_line(__UpperCAmelCase ,					__UpperCAmelCase ,					self.max_source_length ,					"right"   )
											__a					:						Optional[int]      =					encode_line(__UpperCAmelCase ,					__UpperCAmelCase ,					self.max_target_length ,					"right"   )

											__a					:						List[str]      =					source_inputs["input_ids"].squeeze()
											__a					:						Optional[Any]      =					target_inputs["input_ids"].squeeze()
											__a					:						Optional[Any]      =					source_inputs["attention_mask"].squeeze()
											return {
											    "input_ids": source_ids,
											    "attention_mask": src_mask,
											    "decoder_input_ids": target_ids,
											}



						@staticmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					Union[str, Any]   )    ->     Dict[str, torch.Tensor]:




											'''simple docstring'''



											__a					:						int      =					torch.stack([x["input_ids"] for x in batch]   )
											__a					:						Any      =					torch.stack([x["attention_mask"] for x in batch]   )
											__a					:						Tuple      =					torch.stack([x["decoder_input_ids"] for x in batch]   )
											__a					:						Dict      =					(
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer ,					__UpperCAmelCase   )
											    else self.tokenizer.pad_token_id
											)
											__a					:						Optional[Any]      =					(
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer ,					__UpperCAmelCase   )
											    else self.tokenizer.pad_token_id
											)
											__a					:						Any      =					trim_batch(__UpperCAmelCase ,					__UpperCAmelCase   )
											__a      ,			__a					:						Optional[Any]      =					trim_batch(__UpperCAmelCase ,					__UpperCAmelCase ,					attention_mask=__UpperCAmelCase   )
											__a					:						Optional[Any]      =					{
											    "input_ids": source_ids,
											    "attention_mask": source_mask,
											    "decoder_input_ids": y,
											}
											return batch


UpperCAmelCase__      			=						getLogger(__name__)







def     a_   (__A      )				->						str:



					"""simple docstring"""
					return list(itertools.chain.from_iterable(__A      )      )







def     a_   (__A      )				->						None:



					"""simple docstring"""
					__a					:						int      =					get_git_info()
					save_json(__A				,       os.path.join(__A				,       "git_log.json"      )      )







def     a_   (__A				,       __A				,       __A=4				,       **__A      )				->						Union[str, Any]:



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







def     a_   (__A      )				->						Union[str, Any]:



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







def     a_   ()				->						Any:



					"""simple docstring"""
					__a					:						Dict      =					git.Repo(search_parent_directories=__A      )
					__a					:						Union[str, 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      )				->						List:



					"""simple docstring"""
					return list(map(__A				,       __A      )      )







def     a_   (__A				,       __A      )				->						List[Any]:



					"""simple docstring"""
					with open(__A				,       "wb"      ) as f:
										return pickle.dump(__A				,       __A      )







def     a_   (__A      )				->						str:



					"""simple docstring"""

					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      ):
										__a					:						Tuple      =					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      )				->						str:



					"""simple docstring"""
					__a					:						Optional[Any]      =					normalize_answer(__A      ).split()
					__a					:						List[str]      =					normalize_answer(__A      ).split()
					__a					:						Dict      =					Counter(__A      ) & Counter(__A      )
					__a					:						List[Any]      =					sum(common.values()      )
					if num_same == 0:
										return 0
					__a					:						Union[str, Any]      =					1.0 * num_same / len(__A      )
					__a					:						Optional[Any]      =					1.0 * num_same / len(__A      )
					__a					:						Tuple      =					(2 * precision * recall) / (precision + recall)
					return fa







def     a_   (__A				,       __A      )				->						List[Any]:



					"""simple docstring"""
					return normalize_answer(__A      ) == normalize_answer(__A      )







def     a_   (__A				,       __A      )				->						Dict:



					"""simple docstring"""
					assert len(__A      ) == len(__A      )
					__a					:						Dict      =					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      )				->						Union[str, Any]:



					"""simple docstring"""
					return model_prefix.startswith("rag"      )







def     a_   (__A				,       __A				,       __A      )				->						List[Any]:



					"""simple docstring"""
					__a					:						Tuple      =					{p: p for p in extra_params}
					# T5 models don't have `dropout` param, they have `dropout_rate` instead
					__a					:						int      =					"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					:						List[Any]      =					p if hasattr(__A				,       __A      ) else equivalent_param[p]
															setattr(__A				,       __A				,       getattr(__A				,       __A      )      )
															delattr(__A				,       __A      )
					return hparams, config













def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if not head:
										return True
					# split the list to two parts
					__a      ,			__a					:						Any      =					head.next, head
					while fast and fast.next:
										__a					:						Optional[int]      =					fast.next.next
										__a					:						Optional[int]      =					slow.next
					__a					:						Optional[int]      =					slow.next
					__a					:						Tuple      =					None  # Don't forget here! But forget still works!
					# reverse the second part
					__a					:						Any      =					None
					while second:
										__a					:						int      =					second.next
										__a					:						int      =					node
										__a					:						Union[str, Any]      =					second
										__a					:						Union[str, Any]      =					nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										__a					:						int      =					node.next
										__a					:						Dict      =					head.next
					return True







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					if not head or not head.next:
										return True
					__a					:						Optional[int]      =					{}
					__a					:						Optional[int]      =					0
					while head:
										if head.val in d:
															d[head.val].append(__A      )
										else:
															__a					:						Dict      =					[pos]
										__a					:						Dict      =					head.next
										pos += 1
					__a					:						Dict      =					pos - 1
					__a					:						Optional[int]      =					0
					for v in d.values():
										if len(__A      ) % 2 != 0:
															middle += 1
										else:
															__a					:						Dict      =					0
															for i in range(0				,       len(__A      )      ):
																				if v[i] + v[len(__A      ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True





