




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     __magic_name__       (				A	:      Tuple,						A	:      Any,						A	:      str,						A	:      List[Any],						A	:      Any=True,						A	:      str="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     __magic_name__       (				A	:      str,						A	:      Union[str, Any],						A	:      Union[str, Any]=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 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Dict="train"      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  str=None      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  Optional[Any]=""      ,   )      ->  Optional[int]:
							super().__init__()
							a					=    Path(__lowerCamelCase							).joinpath(type_path + ".source"							)
							a					=    Path(__lowerCamelCase							).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       :  Dict							)      ->  Dict:
							return len(self.src_lens							)





				def __getitem__(   self       :  List[str]      ,   __lowerCamelCase       :  Any							)      ->  Dict[str, torch.Tensor]:
							a					=    index + 1  # linecache starts at 1
							a					=    self.prefix + linecache.getline(str(self.src_file							)      ,   __lowerCamelCase							).rstrip("\n"							)
							a					=    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
							a					=    (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer      ,   __lowerCamelCase							) else self.tokenizer
							)
							a					=    self.tokenizer.generator if isinstance(self.tokenizer      ,   __lowerCamelCase							) else self.tokenizer

							a					=    encode_line(__lowerCamelCase      ,   __lowerCamelCase      ,   self.max_source_length      ,   "right"							)
							a					=    encode_line(__lowerCamelCase      ,   __lowerCamelCase      ,   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 							__UpperCAmelCase							(   __lowerCamelCase       :  Tuple							)      ->  Dict:
							return [len(__lowerCamelCase							) for x in Path(__lowerCamelCase							).open().readlines()]





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Any							)      ->  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      ,   __lowerCamelCase							)
							    else self.tokenizer.pad_token_id
							)
							a					=    (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer      ,   __lowerCamelCase							)
							    else self.tokenizer.pad_token_id
							)
							a					=    trim_batch(__lowerCamelCase      ,   __lowerCamelCase							)
							a					,	a					=    trim_batch(__lowerCamelCase      ,   __lowerCamelCase      ,   attention_mask=__lowerCamelCase							)
							a					=    {
							    "input_ids": source_ids,
							    "attention_mask": source_mask,
							    "decoder_input_ids": y,
							}
							return batch

__lowerCAmelCase	:       int     =	getLogger(__name__)




def     __magic_name__       (				A	:      List[List]     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      str     ):






			'''simple docstring'''







			a					=    get_git_info()
			save_json(A,						os.path.join(A,						"git_log.json"     )     )




def     __magic_name__       (				A	:      int,						A	:      List[str],						A	:      str=4,						**A	:      Tuple     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







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




def     __magic_name__       (				):






			'''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     __magic_name__       (				A	:      Callable,						A	:      Iterable     ):






			'''simple docstring'''







			return list(map(A,						A     )     )




def     __magic_name__       (				A	:      Optional[int],						A	:      int     ):






			'''simple docstring'''







			with open(A,						"wb"     ) as f:
						return pickle.dump(A,						A     )




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







			def remove_articles(A	:      int     ):
						return re.sub(R"\b(a|an|the)\b",						" ",						A     )

			def white_space_fix(A	:      Union[str, Any]     ):
						return " ".join(text.split()     )

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

			def lower(A	:      Dict     ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A     )     )     )     )




def     __magic_name__       (				A	:      Any,						A	:      int     ):






			'''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     __magic_name__       (				A	:      Any,						A	:      int     ):






			'''simple docstring'''







			return normalize_answer(A     ) == normalize_answer(A     )




def     __magic_name__       (				A	:      List[str],						A	:      List[str]     ):






			'''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     __magic_name__       (				A	:      List[Any]     ):






			'''simple docstring'''







			return model_prefix.startswith("rag"     )






def     __magic_name__       (				A	:      Union[str, Any],						A	:      str,						A	:      Tuple     ):






			'''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












def     __magic_name__       (				A	:      int,						A	:      int,						A	:      int     ):






			'''simple docstring'''







			if exponent == 1:
						return base
			if exponent % 2 == 0:
						a					=    _modexpt(A,						exponent // 2,						A     ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(A,						exponent - 1,						A     )) % modulo_value




def     __magic_name__       (				A	:      int = 1777,						A	:      int = 1855,						A	:      int = 8     ):






			'''simple docstring'''







			a					=    base
			for _ in range(1,						A     ):
						a					=    _modexpt(A,						A,						10**digits     )

			return result


if __name__ == "__main__":
		print(F'''{solution() = }''')






