





"""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_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=True							,       _lowerCAmelCase="pt"					)						->			List[Any]:
 UpperCamelCase     : Any	     =       {"add_prefix_space": True} if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not line.startswith(" "					) else {}
 UpperCamelCase     : List[str]	     =       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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       )						->			List[str]:
 UpperCamelCase     : 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  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_="train"	,							A_=None	,							A_=None	,							A_=None	,							A_=""	,							):
       '''simple docstring'''




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





      def __len__(			self       ):
       '''simple docstring'''




       return len(self.src_lens       )





      def __getitem__(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       index + 1  # linecache starts at 1
       UpperCamelCase     : Union[str, Any]	     =       self.prefix + linecache.getline(str(self.src_file       )	,							A_       ).rstrip("\n"       )
       UpperCamelCase     : List[Any]	     =       linecache.getline(str(self.tgt_file       )	,							A_       ).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	,							A_       ):
        source_line += self.tokenizer.eos_token
        tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       UpperCamelCase     : str	     =       (
           self.tokenizer.question_encoder if isinstance(self.tokenizer	,							A_       ) else self.tokenizer
       )
       UpperCamelCase     : int	     =       self.tokenizer.generator if isinstance(self.tokenizer	,							A_       ) else self.tokenizer

       UpperCamelCase     : Union[str, Any]	     =       encode_line(A_	,							A_	,							self.max_source_length	,							"right"       )
       UpperCamelCase     : Optional[int]	     =       encode_line(A_	,							A_	,							self.max_target_length	,							"right"       )

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





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




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


__lowerCamelCase	:					Any			  =					getLogger(__name__)



def 		A_    (					_lowerCAmelCase					)						->			Union[str, Any]:
 return list(itertools.chain.from_iterable(_lowerCAmelCase					)					)



def 		A_    (					_lowerCAmelCase					)						->			None:
 UpperCamelCase     : Any	     =       get_git_info()
 save_json(_lowerCAmelCase							,       os.path.join(_lowerCAmelCase							,       "git_log.json"					)					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=4							,       **_lowerCAmelCase					)						->			Optional[int]:
 with open(_lowerCAmelCase							,       "w"					) as f:
  json.dump(_lowerCAmelCase							,       _lowerCAmelCase							,       indent=_lowerCAmelCase							,       **_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase					)						->			Any:
 with open(_lowerCAmelCase					) as f:
  return json.load(_lowerCAmelCase					)



def 		A_    (					)						->			str:
 UpperCamelCase     : int	     =       git.Repo(search_parent_directories=_lowerCAmelCase					)
 UpperCamelCase     : Dict	     =       {
     "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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List:
 return list(map(_lowerCAmelCase							,       _lowerCAmelCase					)					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 with open(_lowerCAmelCase							,       "wb"					) as f:
  return pickle.dump(_lowerCAmelCase							,       _lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase					)						->			Dict:

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

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

 def remove_punc(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       set(string.punctuation					)
  return "".join(ch for ch in text if ch not in exclude					)

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

 return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase					)					)					)					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Optional[int]	     =       normalize_answer(_lowerCAmelCase					).split()
 UpperCamelCase     : Optional[Any]	     =       normalize_answer(_lowerCAmelCase					).split()
 UpperCamelCase     : List[str]	     =       Counter(_lowerCAmelCase					) & Counter(_lowerCAmelCase					)
 UpperCamelCase     : int	     =       sum(common.values()					)
 if num_same == 0:
  return 0
 UpperCamelCase     : Optional[int]	     =       1.0 * num_same / len(_lowerCAmelCase					)
 UpperCamelCase     : Optional[int]	     =       1.0 * num_same / len(_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       (2 * precision * recall) / (precision + recall)
 return fa



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Any:
 return normalize_answer(_lowerCAmelCase					) == normalize_answer(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 assert len(_lowerCAmelCase					) == len(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       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 		A_    (					_lowerCAmelCase					)						->			List[Any]:
 return model_prefix.startswith("rag"					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[str]:
 UpperCamelCase     : 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(_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
   UpperCamelCase     : str	     =       p if hasattr(_lowerCAmelCase							,       _lowerCAmelCase					) else equivalent_param[p]
   setattr(_lowerCAmelCase							,       _lowerCAmelCase							,       getattr(_lowerCAmelCase							,       _lowerCAmelCase					)					)
   delattr(_lowerCAmelCase							,       _lowerCAmelCase					)
 return hparams, config







import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)


__lowerCamelCase	:					Any			  =					r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       raise NotImplementedError("StoppingCriteria needs to be subclassed"       )



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       max_length
       UpperCamelCase     : Dict	     =       max_position_embeddings





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       input_ids.shape[-1]
       UpperCamelCase     : str	     =       cur_len >= self.max_length
       if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
        logger.warning_once(
            "This is a friendly reminder - the current text generation call will exceed the model's predefined "
            F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
            "exceptions, performance degradation, or nothing at all."       )
       return is_done



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_       ):
       '''simple docstring'''




       warnings.warn(
           "The class `MaxNewTokensCriteria` is deprecated. "
           F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
           "with `max_length = start_length + max_new_tokens` instead."	,							A_	,							)
       UpperCamelCase     : Union[str, Any]	     =       start_length
       UpperCamelCase     : List[str]	     =       max_new_tokens
       UpperCamelCase     : Tuple	     =       start_length + max_new_tokens





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return input_ids.shape[-1] >= self.max_length



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       max_time
       UpperCamelCase     : Dict	     =       time.time() if initial_timestamp is None else initial_timestamp





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return time.time() - self.initial_timestamp > self.max_time



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return any(criteria(A_	,							A_       ) for criteria in self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       for stopping_criterium in self:
        if isinstance(A_	,							A_       ):
         return stopping_criterium.max_length
        elif isinstance(A_	,							A_       ):
         return stopping_criterium.max_length
       return None



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			StoppingCriteriaList:
 UpperCamelCase     : Tuple	     =       stopping_criteria.max_length
 UpperCamelCase     : Union[str, Any]	     =       deepcopy(_lowerCAmelCase					)
 if stopping_max_length is not None and stopping_max_length != max_length:
  warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"							,       _lowerCAmelCase					)
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCAmelCase					)					)
 return new_stopping_criteria
