'''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 (     _a	:	str				,     _a	:	List[Any]				,     _a	:	Dict				,     _a	:	Tuple				,     _a	:	Optional[Any]=True				,     _a	:	str="pt"			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  {'add_prefix_space': True} if isinstance(_a				,     _a			) and not line.startswith(' '			) else {}
    __UpperCamelCase	:	Tuple		    =  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  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Tuple=None				,     ):




    """simple docstring"""



    __UpperCamelCase	:	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 			__lowercase		(						UpperCamelCase__						):







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







     def __len__(		self    ):
         return len(self.src_lens    )







     def __getitem__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  index + 1  # linecache starts at 1
         __UpperCamelCase	:	List[Any]		    =  self.prefix + linecache.getline(str(self.src_file    )     ,			_lowerCamelCase    ).rstrip('\n'    )
         __UpperCamelCase	:	Dict		    =  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
         __UpperCamelCase	:	Optional[int]		    =  (
             self.tokenizer.question_encoder if isinstance(self.tokenizer     ,			_lowerCamelCase    ) else self.tokenizer
         )
         __UpperCamelCase	:	Any		    =  self.tokenizer.generator if isinstance(self.tokenizer     ,			_lowerCamelCase    ) else self.tokenizer

         __UpperCamelCase	:	Union[str, Any]		    =  encode_line(_lowerCamelCase     ,			_lowerCamelCase     ,			self.max_source_length     ,			'right'    )
         __UpperCamelCase	:	Optional[int]		    =  encode_line(_lowerCamelCase     ,			_lowerCamelCase     ,			self.max_target_length     ,			'right'    )

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







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase    ):
         return [len(_lowerCamelCase    ) for x in Path(_lowerCamelCase    ).open().readlines()]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  torch.stack([x['input_ids'] for x in batch]    )
         __UpperCamelCase	:	str		    =  torch.stack([x['attention_mask'] for x in batch]    )
         __UpperCamelCase	:	List[str]		    =  torch.stack([x['decoder_input_ids'] for x in batch]    )
         __UpperCamelCase	:	List[Any]		    =  (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer     ,			_lowerCamelCase    )
             else self.tokenizer.pad_token_id
         )
         __UpperCamelCase	:	Optional[Any]		    =  (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer     ,			_lowerCamelCase    )
             else self.tokenizer.pad_token_id
         )
         __UpperCamelCase	:	Any		    =  trim_batch(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  trim_batch(_lowerCamelCase     ,			_lowerCamelCase     ,			attention_mask=_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  {
             'input_ids': source_ids,
             'attention_mask': source_mask,
             'decoder_input_ids': y,
         }
         return batch




a=  getLogger(__name__)







def  _UpperCamelCase (     _a	:	List[List]			):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  get_git_info()
    save_json(_a				,     os.path.join(_a				,     'git_log.json'			)			)







def  _UpperCamelCase (     _a	:	Any				,     _a	:	List[Any]				,     _a	:	Optional[Any]=4				,     **_a	:	Union[str, Any]			):




    """simple docstring"""



    with open(_a				,     'w'			) as f:
        json.dump(_a				,     _a				,     indent=_a				,     **_a			)







def  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



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







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  git.Repo(search_parent_directories=_a			)
    __UpperCamelCase	:	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  _UpperCamelCase (     _a	:	Callable				,     _a	:	Iterable			):




    """simple docstring"""



    return list(map(_a				,     _a			)			)







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	Dict			):




    """simple docstring"""



    with open(_a				,     'wb'			) as f:
        return pickle.dump(_a				,     _a			)







def  _UpperCamelCase (     _a	:	Union[str, Any]			):




    """simple docstring"""



    def remove_articles(_a	:	str			):
        return re.sub(r'\b(a|an|the)\b'				,     ' '				,     _a			)

    def white_space_fix(_a	:	int			):
        return " ".join(text.split()			)

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

    def lower(_a	:	int			):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_a			)			)			)			)







def  _UpperCamelCase (     _a	:	Optional[Any]				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  normalize_answer(_a			).split()
    __UpperCamelCase	:	Optional[int]		    =  normalize_answer(_a			).split()
    __UpperCamelCase	:	Tuple		    =  Counter(_a			) & Counter(_a			)
    __UpperCamelCase	:	int		    =  sum(common.values()			)
    if num_same == 0:
        return 0
    __UpperCamelCase	:	Optional[Any]		    =  1.0 * num_same / len(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  1.0 * num_same / len(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  (2 * precision * recall) / (precision + recall)
    return fa







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	List[Any]			):




    """simple docstring"""



    return normalize_answer(_a			) == normalize_answer(_a			)







def  _UpperCamelCase (     _a	:	List[str]				,     _a	:	List[str]			):




    """simple docstring"""



    assert len(_a			) == len(_a			)
    __UpperCamelCase	:	str		    =  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  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    return model_prefix.startswith('rag'			)







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  {p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    __UpperCamelCase	:	str		    =  '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
            __UpperCamelCase	:	Dict		    =  p if hasattr(_a				,     _a			) else equivalent_param[p]
            setattr(_a				,     _a				,     getattr(_a				,     _a			)			)
            delattr(_a				,     _a			)
    return hparams, config




'''simple docstring'''
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a=  logging.get_logger(__name__)

a=  {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''xlnet'''
     SCREAMING_SNAKE_CASE__		   =						['''mems''']
     SCREAMING_SNAKE_CASE__		   =						{
         '''n_token''': '''vocab_size''',  # Backward compatibility
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }







     def __init__(		self     ,			_lowerCamelCase=3_2_0_0_0     ,			_lowerCamelCase=1_0_2_4     ,			_lowerCamelCase=2_4     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=4_0_9_6     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="bi"     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-1_2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=-1     ,			_lowerCamelCase=False     ,			_lowerCamelCase="last"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="tanh"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[str]		    =  vocab_size
         __UpperCamelCase	:	List[Any]		    =  d_model
         __UpperCamelCase	:	List[str]		    =  n_layer
         __UpperCamelCase	:	Optional[Any]		    =  n_head
         if d_model % n_head != 0:
             raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""    )
         if "d_head" in kwargs:
             if kwargs["d_head"] != d_model // n_head:
                 raise ValueError(
                     f"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""    )
         __UpperCamelCase	:	str		    =  d_model // n_head
         __UpperCamelCase	:	Union[str, Any]		    =  ff_activation
         __UpperCamelCase	:	str		    =  d_inner
         __UpperCamelCase	:	Any		    =  untie_r
         __UpperCamelCase	:	List[Any]		    =  attn_type

         __UpperCamelCase	:	str		    =  initializer_range
         __UpperCamelCase	:	Optional[int]		    =  layer_norm_eps

         __UpperCamelCase	:	Union[str, Any]		    =  dropout
         __UpperCamelCase	:	List[str]		    =  mem_len
         __UpperCamelCase	:	Optional[int]		    =  reuse_len
         __UpperCamelCase	:	str		    =  bi_data
         __UpperCamelCase	:	Tuple		    =  clamp_len
         __UpperCamelCase	:	List[Any]		    =  same_length

         __UpperCamelCase	:	Optional[Any]		    =  summary_type
         __UpperCamelCase	:	List[str]		    =  summary_use_proj
         __UpperCamelCase	:	Union[str, Any]		    =  summary_activation
         __UpperCamelCase	:	str		    =  summary_last_dropout
         __UpperCamelCase	:	Union[str, Any]		    =  start_n_top
         __UpperCamelCase	:	str		    =  end_n_top

         __UpperCamelCase	:	Optional[int]		    =  bos_token_id
         __UpperCamelCase	:	Optional[Any]		    =  pad_token_id
         __UpperCamelCase	:	List[Any]		    =  eos_token_id

         if "use_cache" in kwargs:
             warnings.warn(
                 'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
                 ' instead.'     ,			_lowerCamelCase     ,			)
             __UpperCamelCase	:	Optional[int]		    =  kwargs['use_cache']

         __UpperCamelCase	:	Optional[int]		    =  use_mems_eval
         __UpperCamelCase	:	Tuple		    =  use_mems_train
         super().__init__(pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			**_lowerCamelCase    )







     @property
     def lowerCAmelCase     (		self    ):
         logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""    )
         return -1







     @max_position_embeddings.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # Message copied from Transformer-XL documentation
         raise NotImplementedError(
             f"""The model {self.model_type} is one of the few models that has no sequence length limit."""    )



