



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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase="pt"    ):
    __a							=    {'''add_prefix_space''': True} if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ) and not line.startswith(''' '''    ) else {}
    __a							=    padding_side
    return tokenizer(
        [line]       ,			max_length=_UpperCAmelCase       ,			padding='''max_length''' if pad_to_max_length else None       ,			truncation=_UpperCAmelCase       ,			return_tensors=_UpperCAmelCase       ,			add_special_tokens=_UpperCAmelCase       ,			**_UpperCAmelCase       ,			)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			):
    __a							=    input_ids.ne(_UpperCAmelCase    ).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	(     __UpperCAmelCase   ):



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


           '''simple docstring'''

           super().__init__()
           __a							=    Path(__SCREAMING_SNAKE_CASE).joinpath(type_path + '''.source''')
           __a							=    Path(__SCREAMING_SNAKE_CASE).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							:					Any):


           '''simple docstring'''

           return len(self.src_lens)



       def __getitem__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    index + 1  # linecache starts at 1
           __a							=    self.prefix + linecache.getline(str(self.src_file)					,		__SCREAMING_SNAKE_CASE).rstrip('''\n''')
           __a							=    linecache.getline(str(self.tgt_file)					,		__SCREAMING_SNAKE_CASE).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					,		__SCREAMING_SNAKE_CASE):
               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					,		__SCREAMING_SNAKE_CASE) else self.tokenizer
           )
           __a							=    self.tokenizer.generator if isinstance(self.tokenizer					,		__SCREAMING_SNAKE_CASE) else self.tokenizer

           __a							=    encode_line(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		self.max_source_length					,		'''right''')
           __a							=    encode_line(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		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 		_lowerCamelCase      (		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __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					,		__SCREAMING_SNAKE_CASE)
               else self.tokenizer.pad_token_id
           )
           __a							=    (
               self.tokenizer.question_encoder.pad_token_id
               if isinstance(self.tokenizer					,		__SCREAMING_SNAKE_CASE)
               else self.tokenizer.pad_token_id
           )
           __a							=    trim_batch(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a	,       __a							=    trim_batch(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''input_ids''': source_ids,
               '''attention_mask''': source_mask,
               '''decoder_input_ids''': y,
           }
           return batch


__snake_case						:int						=					getLogger(__name__)





def 					__snake_case     (      _UpperCAmelCase    ):
    return list(itertools.chain.from_iterable(_UpperCAmelCase    )    )





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    get_git_info()
    save_json(_UpperCAmelCase       ,			os.path.join(_UpperCAmelCase       ,			'''git_log.json'''    )    )





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





def 					__snake_case     (      _UpperCAmelCase    ):
    with open(_UpperCAmelCase    ) as f:
        return json.load(_UpperCAmelCase    )





def 					__snake_case     (      ):
    __a							=    git.Repo(search_parent_directories=_UpperCAmelCase    )
    __a							=    {
        '''repo_id''': str(_UpperCAmelCase    ),
        '''repo_sha''': str(repo.head.object.hexsha    ),
        '''repo_branch''': str(repo.active_branch    ),
        '''hostname''': str(socket.gethostname()    ),
    }
    return repo_infos





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    return list(map(_UpperCAmelCase       ,			_UpperCAmelCase    )    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    with open(_UpperCAmelCase       ,			'''wb'''    ) as f:
        return pickle.dump(_UpperCAmelCase       ,			_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase    ):

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

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

    def remove_punc(_UpperCAmelCase    ):
        __a							=    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(_UpperCAmelCase    )    )    )    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    normalize_answer(_UpperCAmelCase    ).split()
    __a							=    normalize_answer(_UpperCAmelCase    ).split()
    __a							=    Counter(_UpperCAmelCase    ) & Counter(_UpperCAmelCase    )
    __a							=    sum(common.values()    )
    if num_same == 0:
        return 0
    __a							=    1.0 * num_same / len(_UpperCAmelCase    )
    __a							=    1.0 * num_same / len(_UpperCAmelCase    )
    __a							=    (2 * precision * recall) / (precision + recall)
    return fa





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    return normalize_answer(_UpperCAmelCase    ) == normalize_answer(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    assert len(_UpperCAmelCase    ) == len(_UpperCAmelCase    )
    __a							=    0
    for hypo, pred in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ):
        em += exact_match_score(_UpperCAmelCase       ,			_UpperCAmelCase    )
    if len(_UpperCAmelCase    ) > 0:
        em /= len(_UpperCAmelCase    )
    return {"em": em}





def 					__snake_case     (      _UpperCAmelCase    ):
    return model_prefix.startswith('''rag'''    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __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(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            if not hasattr(_UpperCAmelCase       ,			_UpperCAmelCase    ) and not hasattr(_UpperCAmelCase       ,			equivalent_param[p]    ):
                logger.info('''config doesn\'t have a `{}` attribute'''.format(_UpperCAmelCase    )    )
                delattr(_UpperCAmelCase       ,			_UpperCAmelCase    )
                continue
            __a							=    p if hasattr(_UpperCAmelCase       ,			_UpperCAmelCase    ) else equivalent_param[p]
            setattr(_UpperCAmelCase       ,			_UpperCAmelCase       ,			getattr(_UpperCAmelCase       ,			_UpperCAmelCase    )    )
            delattr(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return hparams, config










import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case						:int						=					logging.get_logger(__name__)

class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    question_encoder
           __a							=    generator
           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if os.path.isfile(__SCREAMING_SNAKE_CASE):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__SCREAMING_SNAKE_CASE					,		exist_ok=__SCREAMING_SNAKE_CASE)
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''question_encoder_tokenizer''')
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.generator.save_pretrained(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a							=    kwargs.pop('''config'''					,		__SCREAMING_SNAKE_CASE)

           if config is None:
               __a							=    RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.question_encoder					,		subfolder='''question_encoder_tokenizer''')
           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.generator					,		subfolder='''generator_tokenizer''')
           return cls(question_encoder=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					Dict					,		*__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.current_tokenizer(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		*__SCREAMING_SNAKE_CASE							:					Dict					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return self.generator.decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.generator





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[str]] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					str = "longest"					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''					,		__SCREAMING_SNAKE_CASE					,		)
           if max_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               __SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               text_target=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    labels['''input_ids''']
           return model_inputs





