






'''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     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase,						_lowercase=True,						_lowercase="pt"     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Optional[int]				   =				{"""add_prefix_space""": True} if isinstance(lowerCamelCase__,						lowerCamelCase__     ) and not line.startswith(""" """     ) else {}
     __lowerCamelCase						:       Optional[int]				   =				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     __lowercase  (_lowercase,						_lowercase,						_lowercase=None,						)   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Union[str, Any]	,    A__      : List[str]	,    A__      : int	,    A__      : str	,    A__      : Optional[Any]	,    A__      : Dict="train"	,    A__      : Dict=None	,    A__      : str=None	,    A__      : Union[str, Any]=None	,    A__      : Dict=""	,    ):


            """simple docstring"""







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




       def __len__(					self      : Dict					):


            """simple docstring"""







            return len(self.src_lens					)




       def __getitem__(					self      : Union[str, Any]	,    A__      : Optional[int]					):


            """simple docstring"""







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

            __lowerCamelCase						:       Any				   =				encode_line(A__	,    A__	,    self.max_source_length	,    """right"""					)
            __lowerCamelCase						:       List[str]				   =				encode_line(A__	,    A__	,    self.max_target_length	,    """right"""					)

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




       @staticmethod
       def 					a_					(					A__      : List[str]					):


            """simple docstring"""







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




       def 					a_					(					self      : Union[str, Any]	,    A__      : List[str]					):


            """simple docstring"""







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

UpperCAmelCase__						:List[str]          =			getLogger(__name__)
def     __lowercase  (_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     return list(itertools.chain.from_iterable(lowerCamelCase__     )     )
def     __lowercase  (_lowercase     )   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				get_git_info()
     save_json(lowerCamelCase__,						os.path.join(lowerCamelCase__,						"""git_log.json"""     )     )
def     __lowercase  (_lowercase,						_lowercase,						_lowercase=4,						**_lowercase     )   ->     Any:


     """simple docstring"""
     with open(lowerCamelCase__,						"""w"""     ) as f:
          json.dump(lowerCamelCase__,						lowerCamelCase__,						indent=lowerCamelCase__,						**lowerCamelCase__     )
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     with open(lowerCamelCase__     ) as f:
          return json.load(lowerCamelCase__     )
def     __lowercase  ()   ->     Dict:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				git.Repo(search_parent_directories=lowerCamelCase__     )
     __lowerCamelCase						:       List[Any]				   =				{
         """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     __lowercase  (_lowercase,						_lowercase     )   ->     List:


     """simple docstring"""
     return list(map(lowerCamelCase__,						lowerCamelCase__     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Dict:


     """simple docstring"""
     with open(lowerCamelCase__,						"""wb"""     ) as f:
          return pickle.dump(lowerCamelCase__,						lowerCamelCase__     )
def     __lowercase  (_lowercase     )   ->     List[str]:


     """simple docstring"""

     def remove_articles(_lowercase     ):
          return re.sub(R"""\b(a|an|the)\b""",						""" """,						lowerCamelCase__     )

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

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

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

     return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__     )     )     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				normalize_answer(lowerCamelCase__     ).split()
     __lowerCamelCase						:       List[Any]				   =				normalize_answer(lowerCamelCase__     ).split()
     __lowerCamelCase						:       List[str]				   =				Counter(lowerCamelCase__     ) & Counter(lowerCamelCase__     )
     __lowerCamelCase						:       Optional[Any]				   =				sum(common.values()     )
     if num_same == 0:
          return 0
     __lowerCamelCase						:       List[str]				   =				1.0 * num_same / len(lowerCamelCase__     )
     __lowerCamelCase						:       Optional[Any]				   =				1.0 * num_same / len(lowerCamelCase__     )
     __lowerCamelCase						:       str				   =				(2 * precision * recall) / (precision + recall)
     return fa
def     __lowercase  (_lowercase,						_lowercase     )   ->     Dict:


     """simple docstring"""
     return normalize_answer(lowerCamelCase__     ) == normalize_answer(lowerCamelCase__     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Dict:


     """simple docstring"""
     assert len(lowerCamelCase__     ) == len(lowerCamelCase__     )
     __lowerCamelCase						:       int				   =				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     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     return model_prefix.startswith("""rag"""     )
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     __lowerCamelCase						:       Optional[int]				   =				"""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
               __lowerCamelCase						:       List[Any]				   =				p if hasattr(lowerCamelCase__,						lowerCamelCase__     ) else equivalent_param[p]
               setattr(lowerCamelCase__,						lowerCamelCase__,						getattr(lowerCamelCase__,						lowerCamelCase__     )     )
               delattr(lowerCamelCase__,						lowerCamelCase__     )
     return hparams, config











'''simple docstring'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				HfArgumentParser(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     __lowerCamelCase						:       Dict				   =				TensorFlowBenchmark(args=_lowercase     )
     try:
          __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          __lowerCamelCase						:       Tuple				   =				"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
          __lowerCamelCase						:       List[Any]				   =				""" """.join(str(_lowercase     ).split(""" """     )[:-1]     )
          __lowerCamelCase						:       Tuple				   =				""""""
          __lowerCamelCase						:       List[str]				   =				eval(str(_lowercase     ).split(""" """     )[-1]     )
          __lowerCamelCase						:       Tuple				   =				[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]     )
               else:
                    wrong_args.append(_lowercase     )
          if len(_lowercase     ) > 0:
               __lowerCamelCase						:       Tuple				   =				full_error_msg + begin_error_msg + str(_lowercase     )
          raise ValueError(_lowercase     )
     benchmark.run()


if __name__ == "__main__":
   main()



