





'''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    (							_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Dict						,	_lowerCamelCase : List[Any]=True						,	_lowerCamelCase : Union[str, Any]="pt"	):
    A__    =      {"add_prefix_space": True} if isinstance(_lowerCamelCase						,	_lowerCamelCase	) and not line.startswith(" "	) else {}
    A__    =      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    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Optional[Any]=None						,	):
    A__    =      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     (	UpperCamelCase__    ):
 def __init__(    self				:Optional[int]						,	lowercase_				:Optional[Any]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]="train"						,	lowercase_				:Any=None						,	lowercase_				:Optional[int]=None						,	lowercase_				:str=None						,	lowercase_				:int=""						,	)->							int:
     super().__init__()
     A__    =      Path(lowercase_							).joinpath(type_path + ".source"							)
     A__    =      Path(lowercase_							).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				:Dict							)->							Dict:
     return len(self.src_lens							)
 def __getitem__(    self				:int						,	lowercase_				:List[str]							)->							Dict[str, torch.Tensor]:
     A__    =      index + 1  # linecache starts at 1
     A__    =      self.prefix + linecache.getline(str(self.src_file							)						,	lowercase_							).rstrip("\n"							)
     A__    =      linecache.getline(str(self.tgt_file							)						,	lowercase_							).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						,	lowercase_							):
         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						,	lowercase_							) else self.tokenizer
     )
     A__    =      self.tokenizer.generator if isinstance(self.tokenizer						,	lowercase_							) else self.tokenizer

     A__    =      encode_line(lowercase_						,	lowercase_						,	self.max_source_length						,	"right"							)
     A__    =      encode_line(lowercase_						,	lowercase_						,	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_							(    lowercase_				:Any							)->							List[str]:
     return [len(lowercase_							) for x in Path(lowercase_							).open().readlines()]



 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Dict							)->							Dict[str, torch.Tensor]:
     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						,	lowercase_							)
         else self.tokenizer.pad_token_id
     )
     A__    =      (
         self.tokenizer.question_encoder.pad_token_id
         if isinstance(self.tokenizer						,	lowercase_							)
         else self.tokenizer.pad_token_id
     )
     A__    =      trim_batch(lowercase_						,	lowercase_							)
     A__,     A__    =      trim_batch(lowercase_						,	lowercase_						,	attention_mask=lowercase_							)
     A__    =      {
         "input_ids": source_ids,
         "attention_mask": source_mask,
         "decoder_input_ids": y,
     }
     return batch

__lowerCAmelCase							:     Union[str, Any]		     =getLogger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : List[List]	):
    return list(itertools.chain.from_iterable(_lowerCamelCase	)	)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      get_git_info()
    save_json(_lowerCamelCase						,	os.path.join(_lowerCamelCase						,	"git_log.json"	)	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Any						,	_lowerCamelCase : str=4						,	**_lowerCamelCase : int	):
    with open(_lowerCamelCase						,	"w"	) as f:
        json.dump(_lowerCamelCase						,	_lowerCamelCase						,	indent=_lowerCamelCase						,	**_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]	):
    with open(_lowerCamelCase	) as f:
        return json.load(_lowerCamelCase	)





def 						UpperCamelCase    (							):
    A__    =      git.Repo(search_parent_directories=_lowerCamelCase	)
    A__    =      {
        "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    (							_lowerCamelCase : Callable						,	_lowerCamelCase : Iterable	):
    return list(map(_lowerCamelCase						,	_lowerCamelCase	)	)





def 						UpperCamelCase    (							_lowerCamelCase : Optional[int]						,	_lowerCamelCase : int	):
    with open(_lowerCamelCase						,	"wb"	) as f:
        return pickle.dump(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):

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

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

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

    def lower(_lowerCamelCase : Dict	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase	)	)	)	)





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Dict	):
    A__    =      normalize_answer(_lowerCamelCase	).split()
    A__    =      normalize_answer(_lowerCamelCase	).split()
    A__    =      Counter(_lowerCamelCase	) & Counter(_lowerCamelCase	)
    A__    =      sum(common.values()	)
    if num_same == 0:
        return 0
    A__    =      1.0 * num_same / len(_lowerCamelCase	)
    A__    =      1.0 * num_same / len(_lowerCamelCase	)
    A__    =      (2 * precision * recall) / (precision + recall)
    return fa





def 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : List[str]	):
    return normalize_answer(_lowerCamelCase	) == normalize_answer(_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]	):
    assert len(_lowerCamelCase	) == len(_lowerCamelCase	)
    A__    =      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    (							_lowerCamelCase : List[Any]	):
    return model_prefix.startswith("rag"	)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Optional[Any]	):
    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(_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
            A__    =      p if hasattr(_lowerCamelCase						,	_lowerCamelCase	) else equivalent_param[p]
            setattr(_lowerCamelCase						,	_lowerCamelCase						,	getattr(_lowerCamelCase						,	_lowerCamelCase	)	)
            delattr(_lowerCamelCase						,	_lowerCamelCase	)
    return hparams, config










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:     Any		     ={
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:     Tuple		     =[
      "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Swinv2ForImageClassification",
      "Swinv2ForMaskedImageModeling",
      "Swinv2Model",
      "Swinv2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase							:     List[Any]		     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



