





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





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




       return len(self.src_lens   )





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




       UpperCamelCase	:					Optional[Any]       	=   index + 1  # linecache starts at 1
       UpperCamelCase	:					Dict       	=   self.prefix + linecache.getline(str(self.src_file   )       ,							A_   ).rstrip("\n"   )
       UpperCamelCase	:					Dict       	=   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	:					Any       	=   self.tokenizer.generator if isinstance(self.tokenizer       ,							A_   ) else self.tokenizer

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

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


__lowerCamelCase       :     List[Any]   =     getLogger(__name__)



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



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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=4							,   **_lowerCAmelCase     )						-> List[str]:
 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_				(     )						-> Any:
 UpperCamelCase	:					List[Any]       	=   git.Repo(search_parent_directories=_lowerCAmelCase     )
 UpperCamelCase	:					int       	=   {
     "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     )						-> List[Any]:
 with open(_lowerCAmelCase							,   "wb"     ) as f:
  return pickle.dump(_lowerCAmelCase							,   _lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> Union[str, Any]:

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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 return normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )



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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Any       	=   {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 UpperCamelCase	:					Tuple       	=   "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	:					Optional[Any]       	=   p if hasattr(_lowerCAmelCase							,   _lowerCAmelCase     ) else equivalent_param[p]
   setattr(_lowerCAmelCase							,   _lowerCAmelCase							,   getattr(_lowerCAmelCase							,   _lowerCAmelCase     )     )
   delattr(_lowerCAmelCase							,   _lowerCAmelCase     )
 return hparams, config







import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase       :     str   =     """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase       :     Tuple   =     direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCamelCase       :     List[str]   =     transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__lowerCamelCase       :     Optional[Any]   =     re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


__lowerCamelCase       :     List[str]   =     {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 		A_				(     _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					Optional[Any]       	=   None

 # source code of `config_class`
 UpperCamelCase	:					Tuple       	=   inspect.getsource(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[Any]       	=   _re_checkpoint.findall(_lowerCAmelCase     )

 # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
 # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
 for ckpt_name, ckpt_link in checkpoints:
  # allow the link to end with `/`
  if ckpt_link.endswith("/"     ):
   UpperCamelCase	:					Dict       	=   ckpt_link[:-1]

  # verify the checkpoint name corresponds to the checkpoint link
  UpperCamelCase	:					Any       	=   F"""https://huggingface.co/{ckpt_name}"""
  if ckpt_link == ckpt_link_from_name:
   UpperCamelCase	:					List[Any]       	=   ckpt_name
   break

 return checkpoint



def 		A_				(     )						-> List[str]:
 UpperCamelCase	:					Optional[int]       	=   []

 for config_class in list(CONFIG_MAPPING.values()     ):
  # Skip deprecated models
  if "models.deprecated" in config_class.__module__:
   continue
  UpperCamelCase	:					Union[str, Any]       	=   get_checkpoint_from_config_class(_lowerCAmelCase     )

  UpperCamelCase	:					Optional[int]       	=   config_class.__name__
  if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
   configs_without_checkpoint.append(_lowerCAmelCase     )

 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Any       	=   "\n".join(sorted(_lowerCAmelCase     )     )
  raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""     )


if __name__ == "__main__":
    check_config_docstrings_have_checkpoints()
