


"""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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=True				, _SCREAMING_SNAKE_CASE="pt"				)    ->				str:
							snake_case_           =		{"""add_prefix_space""": True} if isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				) and not line.startswith(""" """				) else {}
							snake_case_           =		padding_side
							return tokenizer(
							    [line]				, max_length=_SCREAMING_SNAKE_CASE				, padding="""max_length""" if pad_to_max_length else None				, truncation=_SCREAMING_SNAKE_CASE				, return_tensors=_SCREAMING_SNAKE_CASE				, add_special_tokens=_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				, )







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				, )    ->				Any:
							snake_case_           =		input_ids.ne(_SCREAMING_SNAKE_CASE				).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      __A     (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



														super().__init__()
														snake_case_           =		Path(UpperCAmelCase_       ).joinpath(type_path + """.source"""       )
														snake_case_           =		Path(UpperCAmelCase_       ).joinpath(type_path + """.target"""       )
														snake_case_           =		self.get_char_lens(self.src_file       )
														snake_case_           =		max_source_length
														snake_case_           =		max_target_length
														assert min(self.src_lens       ) > 0, F"""found empty line in {self.src_file}"""
														snake_case_           =		tokenizer
														snake_case_           =		prefix
														if n_obs is not None:
																					snake_case_           =		self.src_lens[:n_obs]
														snake_case_           =		src_lang
														snake_case_           =		tgt_lang



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



														"""simple docstring"""



														return len(self.src_lens       )



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



														"""simple docstring"""



														snake_case_           =		index + 1  # linecache starts at 1
														snake_case_           =		self.prefix + linecache.getline(str(self.src_file       )		,       UpperCAmelCase_       ).rstrip("""\n"""       )
														snake_case_           =		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
														snake_case_           =		(
														    self.tokenizer.question_encoder if isinstance(self.tokenizer		,       UpperCAmelCase_       ) else self.tokenizer
														)
														snake_case_           =		self.tokenizer.generator if isinstance(self.tokenizer		,       UpperCAmelCase_       ) else self.tokenizer

														snake_case_           =		encode_line(UpperCAmelCase_		,       UpperCAmelCase_		,       self.max_source_length		,       """right"""       )
														snake_case_           =		encode_line(UpperCAmelCase_		,       UpperCAmelCase_		,       self.max_target_length		,       """right"""       )

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



							@staticmethod
							def 		lowerCAmelCase       (				UpperCAmelCase_							:      Union[str, Any]       )					->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      List[Any]       )					->Dict[str, torch.Tensor]:



														"""simple docstring"""



														snake_case_           =		torch.stack([x["""input_ids"""] for x in batch]       )
														snake_case_           =		torch.stack([x["""attention_mask"""] for x in batch]       )
														snake_case_           =		torch.stack([x["""decoder_input_ids"""] for x in batch]       )
														snake_case_           =		(
														    self.tokenizer.generator.pad_token_id
														    if isinstance(self.tokenizer		,       UpperCAmelCase_       )
														    else self.tokenizer.pad_token_id
														)
														snake_case_           =		(
														    self.tokenizer.question_encoder.pad_token_id
														    if isinstance(self.tokenizer		,       UpperCAmelCase_       )
														    else self.tokenizer.pad_token_id
														)
														snake_case_           =		trim_batch(UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_       ,						snake_case_           =		trim_batch(UpperCAmelCase_		,       UpperCAmelCase_		,       attention_mask=UpperCAmelCase_       )
														snake_case_           =		{
														    """input_ids""": source_ids,
														    """attention_mask""": source_mask,
														    """decoder_input_ids""": y,
														}
														return batch


__SCREAMING_SNAKE_CASE							:    List[Any]		=    getLogger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				None:
							snake_case_           =		get_git_info()
							save_json(_SCREAMING_SNAKE_CASE				, os.path.join(_SCREAMING_SNAKE_CASE				, """git_log.json"""				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=4				, **_SCREAMING_SNAKE_CASE				)    ->				int:
							with open(_SCREAMING_SNAKE_CASE				, """w"""				) as f:
														json.dump(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, indent=_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Any:
							with open(_SCREAMING_SNAKE_CASE				) as f:
														return json.load(_SCREAMING_SNAKE_CASE				)







def 						_a				(      )    ->				Any:
							snake_case_           =		git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		{
							    """repo_id""": str(_SCREAMING_SNAKE_CASE				),
							    """repo_sha""": str(repo.head.object.hexsha				),
							    """repo_branch""": str(repo.active_branch				),
							    """hostname""": str(socket.gethostname()				),
							}
							return repo_infos







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List:
							return list(map(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Dict:
							with open(_SCREAMING_SNAKE_CASE				, """wb"""				) as f:
														return pickle.dump(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:

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

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE				)				)				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							snake_case_           =		normalize_answer(_SCREAMING_SNAKE_CASE				).split()
							snake_case_           =		normalize_answer(_SCREAMING_SNAKE_CASE				).split()
							snake_case_           =		Counter(_SCREAMING_SNAKE_CASE				) & Counter(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		sum(common.values()				)
							if num_same == 0:
														return 0
							snake_case_           =		1.0 * num_same / len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		1.0 * num_same / len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		(2 * precision * recall) / (precision + recall)
							return fa







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							return normalize_answer(_SCREAMING_SNAKE_CASE				) == normalize_answer(_SCREAMING_SNAKE_CASE				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Dict:
							assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		0
							for hypo, pred in zip(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														em += exact_match_score(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							if len(_SCREAMING_SNAKE_CASE				) > 0:
														em /= len(_SCREAMING_SNAKE_CASE				)
							return {"em": em}







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Dict:
							return model_prefix.startswith("""rag"""				)







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







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



