




"""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 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=True     ,							__UpperCamelCase="pt"     ):
			__lowercase       :   str        =    {'''add_prefix_space''': True} if isinstance(__lowerCamelCase     ,							__lowerCamelCase     ) and not line.startswith(''' '''     ) else {}
			__lowercase       :   int        =    padding_side
			return tokenizer(
			    [line]     ,							max_length=__lowerCamelCase     ,							padding='''max_length''' if pad_to_max_length else None     ,							truncation=__lowerCamelCase     ,							return_tensors=__lowerCamelCase     ,							add_special_tokens=__lowerCamelCase     ,							**__lowerCamelCase     ,							)







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							):
			__lowercase       :   Any        =    input_ids.ne(__lowerCamelCase     ).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    UpperCAmelCase_  (       lowerCAmelCase_						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_="train"							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=""							,		)		->    List[Any]:
					super().__init__()
					__lowercase       :   Tuple        =    Path(UpperCamelCase_    ).joinpath(type_path + '''.source'''    )
					__lowercase       :   str        =    Path(UpperCamelCase_    ).joinpath(type_path + '''.target'''    )
					__lowercase       :   Dict        =    self.get_char_lens(self.src_file    )
					__lowercase       :   Tuple        =    max_source_length
					__lowercase       :   Optional[int]        =    max_target_length
					assert min(self.src_lens    ) > 0, F"""found empty line in {self.src_file}"""
					__lowercase       :   Dict        =    tokenizer
					__lowercase       :   List[str]        =    prefix
					if n_obs is not None:
								__lowercase       :   int        =    self.src_lens[:n_obs]
					__lowercase       :   Dict        =    src_lang
					__lowercase       :   Tuple        =    tgt_lang
		def __len__(		self    )		->    Optional[int]:
					return len(self.src_lens    )
		def __getitem__(		self							,		UpperCamelCase_    )		->    Dict[str, torch.Tensor]:
					__lowercase       :   List[Any]        =    index + 1  # linecache starts at 1
					__lowercase       :   Optional[int]        =    self.prefix + linecache.getline(str(self.src_file    )							,		UpperCamelCase_    ).rstrip('''\n'''    )
					__lowercase       :   Optional[Any]        =    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
					__lowercase       :   Optional[int]        =    (
					    self.tokenizer.question_encoder if isinstance(self.tokenizer							,		UpperCamelCase_    ) else self.tokenizer
					)
					__lowercase       :   Tuple        =    self.tokenizer.generator if isinstance(self.tokenizer							,		UpperCamelCase_    ) else self.tokenizer

					__lowercase       :   Optional[int]        =    encode_line(UpperCamelCase_							,		UpperCamelCase_							,		self.max_source_length							,		'''right'''    )
					__lowercase       :   str        =    encode_line(UpperCamelCase_							,		UpperCamelCase_							,		self.max_target_length							,		'''right'''    )

					__lowercase       :   str        =    source_inputs['''input_ids'''].squeeze()
					__lowercase       :   str        =    target_inputs['''input_ids'''].squeeze()
					__lowercase       :   Union[str, Any]        =    source_inputs['''attention_mask'''].squeeze()
					return {
					    "input_ids": source_ids,
					    "attention_mask": src_mask,
					    "decoder_input_ids": target_ids,
					}
		@staticmethod
		def      _lowerCamelCase				(		UpperCamelCase_    )		->    Optional[int]:
					return [len(UpperCamelCase_    ) for x in Path(UpperCamelCase_    ).open().readlines()]
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Dict[str, torch.Tensor]:
					__lowercase       :   List[Any]        =    torch.stack([x['''input_ids'''] for x in batch]    )
					__lowercase       :   int        =    torch.stack([x['''attention_mask'''] for x in batch]    )
					__lowercase       :   Union[str, Any]        =    torch.stack([x['''decoder_input_ids'''] for x in batch]    )
					__lowercase       :   str        =    (
					    self.tokenizer.generator.pad_token_id
					    if isinstance(self.tokenizer							,		UpperCamelCase_    )
					    else self.tokenizer.pad_token_id
					)
					__lowercase       :   List[str]        =    (
					    self.tokenizer.question_encoder.pad_token_id
					    if isinstance(self.tokenizer							,		UpperCamelCase_    )
					    else self.tokenizer.pad_token_id
					)
					__lowercase       :   Optional[int]        =    trim_batch(UpperCamelCase_							,		UpperCamelCase_    )
					__lowercase       :   Any        =    trim_batch(UpperCamelCase_							,		UpperCamelCase_							,		attention_mask=UpperCamelCase_    )
					__lowercase       :   List[str]        =    {
					    '''input_ids''': source_ids,
					    '''attention_mask''': source_mask,
					    '''decoder_input_ids''': y,
					}
					return batch


a_	   =    getLogger(__name__)







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			return list(itertools.chain.from_iterable(__lowerCamelCase     )     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			__lowercase       :   Dict        =    get_git_info()
			save_json(__lowerCamelCase     ,							os.path.join(__lowerCamelCase     ,							'''git_log.json'''     )     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=4     ,							**__UpperCamelCase     ):
			with open(__lowerCamelCase     ,							'''w'''     ) as f:
						json.dump(__lowerCamelCase     ,							__lowerCamelCase     ,							indent=__lowerCamelCase     ,							**__lowerCamelCase     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			with open(__lowerCamelCase     ) as f:
						return json.load(__lowerCamelCase     )







def 			__UpperCAmelCase			(      ):
			__lowercase       :   List[Any]        =    git.Repo(search_parent_directories=__lowerCamelCase     )
			__lowercase       :   Union[str, Any]        =    {
			    '''repo_id''': str(__lowerCamelCase     ),
			    '''repo_sha''': str(repo.head.object.hexsha     ),
			    '''repo_branch''': str(repo.active_branch     ),
			    '''hostname''': str(socket.gethostname()     ),
			}
			return repo_infos







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			return list(map(__lowerCamelCase     ,							__lowerCamelCase     )     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			with open(__lowerCamelCase     ,							'''wb'''     ) as f:
						return pickle.dump(__lowerCamelCase     ,							__lowerCamelCase     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ):

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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase     )     )     )     )







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







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			return normalize_answer(__lowerCamelCase     ) == normalize_answer(__lowerCamelCase     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			assert len(__lowerCamelCase     ) == len(__lowerCamelCase     )
			__lowercase       :   Any        =    0
			for hypo, pred in zip(__lowerCamelCase     ,							__lowerCamelCase     ):
						em += exact_match_score(__lowerCamelCase     ,							__lowerCamelCase     )
			if len(__lowerCamelCase     ) > 0:
						em /= len(__lowerCamelCase     )
			return {"em": em}







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			return model_prefix.startswith('''rag'''     )







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ):
			__lowercase       :   Any        =    {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			__lowercase       :   Optional[int]        =    '''dropout_rate'''
			for p in extra_params:
						if getattr(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ):
									if not hasattr(__lowerCamelCase     ,							__lowerCamelCase     ) and not hasattr(__lowerCamelCase     ,							equivalent_param[p]     ):
												logger.info('''config doesn\'t have a `{}` attribute'''.format(__lowerCamelCase     )     )
												delattr(__lowerCamelCase     ,							__lowerCamelCase     )
												continue
									__lowercase       :   List[Any]        =    p if hasattr(__lowerCamelCase     ,							__lowerCamelCase     ) else equivalent_param[p]
									setattr(__lowerCamelCase     ,							__lowerCamelCase     ,							getattr(__lowerCamelCase     ,							__lowerCamelCase     )     )
									delattr(__lowerCamelCase     ,							__lowerCamelCase     )
			return hparams, config








"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase : Optional[int]      						=						"▁"

_lowercase : Optional[Any]      						=						{"vocab_file": "spiece.model"}

_lowercase : Optional[Any]      						=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_lowercase : Tuple      						=						{
    "google/pegasus-xsum": 5_1_2,
}


_lowercase : Optional[int]      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES

					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any="<pad>",  lowerCamelCase							:  Optional[Any]="</s>",  lowerCamelCase							:  Any="<unk>",  lowerCamelCase							:  Tuple="<mask_2>",  lowerCamelCase							:  int="<mask_1>",  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Dict=103,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Optional[int],  )->					None:
								lowerCamelCase__		:		Union[str, Any]			=offset
								if additional_special_tokens is not None:
											if not isinstance(lowerCamelCase,  lowerCamelCase     ):
														raise TypeError(
														    F'''additional_special_tokens should be of type {type(lowerCamelCase     )}, but is'''
														    F''' {type(lowerCamelCase     )}'''     )

											lowerCamelCase__		:		Any			=(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    F'''<unk_{i}>''' for i in range(len(lowerCamelCase     ),  self.offset - 1     )
											]

											if len(set(lowerCamelCase     )     ) != len(lowerCamelCase     ):
														raise ValueError(
														    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
														    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''     )
											lowerCamelCase__		:		Optional[Any]			=additional_special_tokens_extended
								else:
											lowerCamelCase__		:		Tuple			=[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [F'''<unk_{i}>''' for i in range(2,  self.offset     )]

								lowerCamelCase__		:		Tuple			={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  mask_token=lowerCamelCase,  pad_token=lowerCamelCase,  mask_token_sent=lowerCamelCase,  offset=lowerCamelCase,  additional_special_tokens=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )
								lowerCamelCase__		:		Optional[int]			=mask_token_sent
								lowerCamelCase__		:		Optional[Any]			=vocab_file
								lowerCamelCase__		:		List[str]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(lowerCamelCase     )

								# add special tokens to encoder dict
								lowerCamelCase__		:		Dict[int, str]			={
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }     )

								if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,  self.offset - 1     )}     )

								lowerCamelCase__		:		Dict[str, int]			={v: k for k, v in self.encoder.items()}



					@property
					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								return len(self.sp_model     ) + self.offset



					def 		snake_case       (       self							:  Optional[Any]     )->					Dict[str, int]:
								lowerCamelCase__		:		List[Any]			={self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



					def __getstate__(       self							:  str     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=self.__dict__.copy()
								lowerCamelCase__		:		Optional[int]			=None
								return state



					def __setstate__(       self							:  Dict,  lowerCamelCase							:  int     )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''     ):
											lowerCamelCase__		:		str			={}

								lowerCamelCase__		:		Union[str, Any]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase     )



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str     )->					int:
								if token in self.decoder:
											return self.decoder[token]
								elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
								lowerCamelCase__		:		Any			=self.sp_model.piece_to_id(lowerCamelCase     )
								return sp_id + self.offset



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int     )->					str:
								if index in self.encoder:
											return self.encoder[index]
								elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
								else:
											lowerCamelCase__		:		Any			=self.sp_model.IdToPiece(index - self.offset     )
								return token



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		Optional[int]			=[]
								lowerCamelCase__		:		Tuple			=''''''
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														out_string += self.sp_model.decode(lowerCamelCase     ) + token
														lowerCamelCase__		:		str			=[]
											else:
														current_sub_tokens.append(lowerCamelCase     )
								out_string += self.sp_model.decode(lowerCamelCase     )
								return out_string.strip()



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any]=False     )->					List[str]:
								return 1



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[int]     )->					Tuple:
								lowerCamelCase__		:		Tuple			=set(self.all_special_ids     )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

								return [1 if x in all_special_ids else 0 for x in seq]



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  List,  lowerCamelCase							:  Optional[List] = None,  lowerCamelCase							:  bool = False     )->					List[int]:
								if already_has_special_tokens:
											return self._special_token_mask(lowerCamelCase     )
								elif token_ids_a is None:
											return self._special_token_mask(lowerCamelCase     ) + [1]
								else:
											return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[int]=None     )->					List[int]:
								if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




					def 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None     )->					Tuple[str]:
								if not os.path.isdir(lowerCamelCase     ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								lowerCamelCase__		:		List[str]			=os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCamelCase     ) and os.path.isfile(self.vocab_file     ):
											copyfile(self.vocab_file,  lowerCamelCase     )
								elif not os.path.isfile(self.vocab_file     ):
											with open(lowerCamelCase,  '''wb'''     ) as fi:
														lowerCamelCase__		:		int			=self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase     )

								return (out_vocab_file,)




