
'''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 	_snake_case						(      A_       :    Optional[Any]     ,		A_       :    str     ,		A_       :    str     ,		A_       :    Any     ,		A_       :    Union[str, Any]=True     ,		A_       :    Union[str, Any]="pt"   ):
  """simple docstring"""







  a_      :	List[Any]      =							{"""add_prefix_space""": True} if isinstance(A_     ,		A_   ) and not line.startswith(""" """   ) else {}
  a_      :	List[str]      =							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 	_snake_case						(      A_       :    List[Any]     ,		A_       :    Optional[Any]     ,		A_       :    Union[str, Any]=None     ,		):
  """simple docstring"""







  a_      :	List[Any]      =							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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

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

       '''simple docstring'''

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


     def __len__(      self ):

       '''simple docstring'''

       return len(self.src_lens )


     def __getitem__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

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

       # Pad source and target to the right
       a_      :	Dict      =							(
           self.tokenizer.question_encoder if isinstance(self.tokenizer							,		lowerCAmelCase_ ) else self.tokenizer
       )
       a_      :	List[str]      =							self.tokenizer.generator if isinstance(self.tokenizer							,		lowerCAmelCase_ ) else self.tokenizer

       a_      :	List[Any]      =							encode_line(lowerCAmelCase_							,		lowerCAmelCase_							,		self.max_source_length							,		"""right""" )
       a_      :	Optional[Any]      =							encode_line(lowerCAmelCase_							,		lowerCAmelCase_							,		self.max_target_length							,		"""right""" )

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


     @staticmethod
     def 							_lowerCAmelCase  (      lowerCAmelCase_ ):

       '''simple docstring'''

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






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

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







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






def 	_snake_case						(      A_       :    List[List]   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







  a_      :	List[str]      =							get_git_info()
  save_json(A_     ,		os.path.join(A_     ,		"""git_log.json"""   )   )






def 	_snake_case						(      A_       :    List[Any]     ,		A_       :    Union[str, Any]     ,		A_       :    int=4     ,		**A_       :    Any   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Optional[Any]   ):
  """simple docstring"""







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






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	str      =							git.Repo(search_parent_directories=A_   )
  a_      :	str      =							{
      """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 	_snake_case						(      A_       :    Callable     ,		A_       :    Iterable   ):
  """simple docstring"""







  return list(map(A_     ,		A_   )   )






def 	_snake_case						(      A_       :    str     ,		A_       :    str   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Optional[Any]   ):
  """simple docstring"""







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

  def white_space_fix(A_       :    Dict   ):
    return " ".join(text.split()   )

  def remove_punc(A_       :    Optional[Any]   ):
    a_      :	Optional[Any]      =							set(string.punctuation   )
    return "".join(ch for ch in text if ch not in exclude   )

  def lower(A_       :    Optional[Any]   ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(A_   )   )   )   )






def 	_snake_case						(      A_       :    str     ,		A_       :    Any   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Tuple     ,		A_       :    int   ):
  """simple docstring"""







  return normalize_answer(A_   ) == normalize_answer(A_   )






def 	_snake_case						(      A_       :    List[str]     ,		A_       :    List[str]   ):
  """simple docstring"""







  assert len(A_   ) == len(A_   )
  a_      :	Tuple      =							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 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







  return model_prefix.startswith("""rag"""   )






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    List[str]     ,		A_       :    Optional[int]   ):
  """simple docstring"""







  a_      :	str      =							{p: p for p in extra_params}
  # T5 models don't have `dropout` param, they have `dropout_rate` instead
  a_      :	Union[str, Any]      =							"""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_      :	List[str]      =							p if hasattr(A_     ,		A_   ) else equivalent_param[p]
      setattr(A_     ,		A_     ,		getattr(A_     ,		A_   )   )
      delattr(A_     ,		A_   )
  return hparams, config




'''simple docstring'''

from torch import nn






def 	_snake_case						(      A_       :    Dict   ):
  """simple docstring"""







  if act_fn in ["swish", "silu"]:
    return nn.SiLU()
  elif act_fn == "mish":
    return nn.Mish()
  elif act_fn == "gelu":
    return nn.GELU()
  else:
    raise ValueError(f'''Unsupported activation function: {act_fn}'''   )


