


"""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__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   List[Any]=True    ,				SCREAMING_SNAKE_CASE   :   str="pt"    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[Any]								=							{"add_prefix_space": True} if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ) and not line.startswith(" "    ) else {}
  lowerCAmelCase					:					Optional[Any]								=							padding_side
  return tokenizer(
      [line]    ,				max_length=SCREAMING_SNAKE_CASE    ,				padding="max_length" if pad_to_max_length else None    ,				truncation=SCREAMING_SNAKE_CASE    ,				return_tensors=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE    ,				**SCREAMING_SNAKE_CASE    ,				)
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Dict=None    ,				):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							input_ids.ne(SCREAMING_SNAKE_CASE    ).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 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__="train"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=None		,					snake_case__=""		,					):






         """simple docstring"""





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

       def __len__(						self						):






         """simple docstring"""





         return len(self.src_lens						)

       def __getitem__(						self		,					snake_case__						):






         """simple docstring"""





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

         # Pad source and target to the right
         lowerCAmelCase					:					Tuple								=							(
             self.tokenizer.question_encoder if isinstance(self.tokenizer		,					snake_case__						) else self.tokenizer
         )
         lowerCAmelCase					:					Optional[int]								=							self.tokenizer.generator if isinstance(self.tokenizer		,					snake_case__						) else self.tokenizer

         lowerCAmelCase					:					int								=							encode_line(snake_case__		,					snake_case__		,					self.max_source_length		,					"right"						)
         lowerCAmelCase					:					Any								=							encode_line(snake_case__		,					snake_case__		,					self.max_target_length		,					"right"						)

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

       @staticmethod
       def 	lowercase__							(						snake_case__						):






         """simple docstring"""





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







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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

lowerCAmelCase__				    =       getLogger(__name__)
def  a__      (    SCREAMING_SNAKE_CASE   :   List[List]    ):


  '''simple docstring'''







  return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  lowerCAmelCase					:					Union[str, Any]								=							get_git_info()
  save_json(SCREAMING_SNAKE_CASE    ,				os.path.join(SCREAMING_SNAKE_CASE    ,				"git_log.json"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict=4    ,				**SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







  with open(SCREAMING_SNAKE_CASE    ,				"w"    ) as f:
    json.dump(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				indent=SCREAMING_SNAKE_CASE    ,				**SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  with open(SCREAMING_SNAKE_CASE    ) as f:
    return json.load(SCREAMING_SNAKE_CASE    )
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[str]								=							git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[int]								=							{
      "repo_id": str(SCREAMING_SNAKE_CASE    ),
      "repo_sha": str(repo.head.object.hexsha    ),
      "repo_branch": str(repo.active_branch    ),
      "hostname": str(socket.gethostname()    ),
  }
  return repo_infos
def  a__      (    SCREAMING_SNAKE_CASE   :   Callable    ,				SCREAMING_SNAKE_CASE   :   Iterable    ):


  '''simple docstring'''







  return list(map(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  with open(SCREAMING_SNAKE_CASE    ,				"wb"    ) as f:
    return pickle.dump(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


  '''simple docstring'''







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

  def white_space_fix(SCREAMING_SNAKE_CASE   :   Optional[Any]    ):
    return " ".join(text.split()    )

  def remove_punc(SCREAMING_SNAKE_CASE   :   Dict    ):
    lowerCAmelCase					:					Union[str, Any]								=							set(string.punctuation    )
    return "".join(ch for ch in text if ch not in exclude    )

  def lower(SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE    )    )    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[Any]								=							normalize_answer(SCREAMING_SNAKE_CASE    ).split()
  lowerCAmelCase					:					Optional[Any]								=							normalize_answer(SCREAMING_SNAKE_CASE    ).split()
  lowerCAmelCase					:					str								=							Counter(SCREAMING_SNAKE_CASE    ) & Counter(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[Any]								=							sum(common.values()    )
  if num_same == 0:
    return 0
  lowerCAmelCase					:					Any								=							1.0 * num_same / len(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					int								=							1.0 * num_same / len(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					str								=							(2 * precision * recall) / (precision + recall)
  return fa
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):


  '''simple docstring'''







  return normalize_answer(SCREAMING_SNAKE_CASE    ) == normalize_answer(SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  assert len(SCREAMING_SNAKE_CASE    ) == len(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[int]								=							0
  for hypo, pred in zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    em += exact_match_score(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  if len(SCREAMING_SNAKE_CASE    ) > 0:
    em /= len(SCREAMING_SNAKE_CASE    )
  return {"em": em}
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ):


  '''simple docstring'''







  return model_prefix.startswith("rag"    )





def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Dict								=							{p: p for p in extra_params}
  # T5 models don't have `dropout` param, they have `dropout_rate` instead
  lowerCAmelCase					:					Tuple								=							"dropout_rate"
  for p in extra_params:
    if getattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
      if not hasattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ) and not hasattr(SCREAMING_SNAKE_CASE    ,				equivalent_param[p]    ):
        logger.info("config doesn't have a `{}` attribute".format(SCREAMING_SNAKE_CASE    )    )
        delattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
        continue
      lowerCAmelCase					:					str								=							p if hasattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ) else equivalent_param[p]
      setattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				getattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )    )
      delattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  return hparams, config









"""simple docstring"""







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__				    =       {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__				    =       [
         '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''VanForImageClassification''',
         '''VanModel''',
         '''VanPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase__				    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)





