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		(			_A							,				_A							,				_A							,				_A							,				_A=True							,				_A="pt"					):



 """simple docstring"""


 __a       =  {"add_prefix_space": True} if isinstance(_A							,				_A					) and not line.startswith(" "					) else {}
 __a       =  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   __A		(			_A							,				_A							,				_A=None							,				):



 """simple docstring"""


 __a       =  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    A_ (       a_ ):

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

  def __getitem__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __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 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      Tuple       ):
   return [len(__SCREAMING_SNAKE_CASE       ) for x in Path(__SCREAMING_SNAKE_CASE       ).open().readlines()]

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   __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
SCREAMING_SNAKE_CASE :	Optional[Any]							     =		getLogger(__name__)



def   __A		(			_A					):



 """simple docstring"""


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



def   __A		(			_A					):



 """simple docstring"""


 __a       =  get_git_info()
 save_json(_A							,				os.path.join(_A							,				"git_log.json"					)					)



def   __A		(			_A							,				_A							,				_A=4							,				**_A					):



 """simple docstring"""


 with open(_A							,				"w"					) as f:
  json.dump(_A							,				_A							,				indent=_A							,				**_A					)



def   __A		(			_A					):



 """simple docstring"""


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



def   __A		(			):



 """simple docstring"""


 __a       =  git.Repo(search_parent_directories=_A					)
 __a       =  {
     "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   __A		(			_A							,				_A					):



 """simple docstring"""


 return list(map(_A							,				_A					)					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 with open(_A							,				"wb"					) as f:
  return pickle.dump(_A							,				_A					)



def   __A		(			_A					):



 """simple docstring"""


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

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

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

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

 return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A					):



 """simple docstring"""


 return normalize_answer(_A					) == normalize_answer(_A					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 assert len(_A					) == len(_A					)
 __a       =  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   __A		(			_A					):



 """simple docstring"""


 return model_prefix.startswith("rag"					)






def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __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(_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
   __a       =  p if hasattr(_A							,				_A					) else equivalent_param[p]
   setattr(_A							,				_A							,				getattr(_A							,				_A					)					)
   delattr(_A							,				_A					)
 return hparams, config



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""xlm-roberta-xl"""

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int=25_08_80     ,							__SCREAMING_SNAKE_CASE :      Dict=25_60     ,							__SCREAMING_SNAKE_CASE :      List[str]=36     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      int=1_02_40     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_14     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1E-05     ,							__SCREAMING_SNAKE_CASE :      str=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="absolute"     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  position_embedding_type
   __a       =  use_cache
   __a       =  classifier_dropout
class    A_ (       a_ ):

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if self.task == "multiple-choice":
    __a       =  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    __a       =  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]       )


