





"""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 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		List[Any]			,       a_					:		Tuple			,       a_					:		List[Any]			,       a_					:		Optional[int]=True			,       a_					:		Dict="pt"							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  {'''add_prefix_space''': True} if isinstance(a_			,       a_							) and not line.startswith(''' '''							) else {}
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  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 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Dict			,       a_					:		Dict=None			,       )      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="train"		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=""		,)      ->				List[Any]:





             """simple docstring"""






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




       def __len__(						self							)      ->				Dict:





             """simple docstring"""






             return len(self.src_lens							)




       def __getitem__(						self		,SCREAMING_SNAKE_CASE__							)      ->				Dict[str, torch.Tensor]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  index + 1  # linecache starts at 1
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.prefix + linecache.getline(str(self.src_file							)		,SCREAMING_SNAKE_CASE__							).rstrip('''\n'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  linecache.getline(str(self.tgt_file							)		,SCREAMING_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		,SCREAMING_SNAKE_CASE__							):
                   source_line += self.tokenizer.eos_token
                   tgt_line += self.tokenizer.eos_token

             # Pad source and target to the right
             __SCREAMING_SNAKE_CASE       :str		    =  (
                 self.tokenizer.question_encoder if isinstance(self.tokenizer		,SCREAMING_SNAKE_CASE__							) else self.tokenizer
             )
             __SCREAMING_SNAKE_CASE       :str		    =  self.tokenizer.generator if isinstance(self.tokenizer		,SCREAMING_SNAKE_CASE__							) else self.tokenizer

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  encode_line(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.max_source_length		,'''right'''							)
             __SCREAMING_SNAKE_CASE       :str		    =  encode_line(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.max_target_length		,'''right'''							)

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




       @staticmethod
       def    _UpperCamelCase					(						SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Dict[str, torch.Tensor]:





             """simple docstring"""






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




lowerCamelCase_       =		getLogger(__name__)



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



def 					__lowerCamelCase       (							a_					:		str							)      ->				None:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  get_git_info()
      save_json(a_			,       os.path.join(a_			,       '''git_log.json'''							)							)



def 					__lowerCamelCase       (							a_					:		Dict			,       a_					:		List[str]			,       a_					:		Dict=4			,       **a_					:		Union[str, Any]							)      ->				Tuple:
      with open(a_			,       '''w'''							) as f:
            json.dump(a_			,       a_			,       indent=a_			,       **a_							)



def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				Tuple:
      with open(a_							) as f:
            return json.load(a_							)



def 					__lowerCamelCase       (							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :str		    =  git.Repo(search_parent_directories=a_							)
      __SCREAMING_SNAKE_CASE       :Dict		    =  {
          '''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 					__lowerCamelCase       (							a_					:		Callable			,       a_					:		Iterable							)      ->				List:
      return list(map(a_			,       a_							)							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Any							)      ->				str:
      with open(a_			,       '''wb'''							) as f:
            return pickle.dump(a_			,       a_							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]							)      ->				Tuple:

      def remove_articles(a_					:		Dict							):
            return re.sub(r'''\b(a|an|the)\b'''			,       ''' '''			,       a_							)

      def white_space_fix(a_					:		Union[str, Any]							):
            return " ".join(text.split()							)

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



def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Union[str, Any]							)      ->				Optional[int]:
      return normalize_answer(a_							) == normalize_answer(a_							)



def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		List[str]							)      ->				Dict:
      assert len(a_							) == len(a_							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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 					__lowerCamelCase       (							a_					:		Union[str, Any]							)      ->				Union[str, Any]:
      return model_prefix.startswith('''rag'''							)







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






"""simple docstring"""
from itertools import product



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				list[int]:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  sides_number
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  max_face_number * dice_number
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  [0] * (max_total + 1)

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  1
      __SCREAMING_SNAKE_CASE       :Tuple		    =  range(a_			,       max_face_number + 1							)
      for dice_numbers in product(a_			,       repeat=a_							):
            __SCREAMING_SNAKE_CASE       :Any		    =  sum(a_							)
            totals_frequencies[total] += 1

      return totals_frequencies



def 					__lowerCamelCase       (							)      ->				float:
      __SCREAMING_SNAKE_CASE       :Dict		    =  total_frequency_distribution(
          sides_number=4			,       dice_number=9							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  total_frequency_distribution(
          sides_number=6			,       dice_number=6							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  0
      __SCREAMING_SNAKE_CASE       :Any		    =  9
      __SCREAMING_SNAKE_CASE       :List[str]		    =  4 * 9
      __SCREAMING_SNAKE_CASE       :Dict		    =  6
      for peter_total in range(a_			,       max_peter_total + 1							):
            peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                colin_totals_frequencies[min_colin_total:peter_total]							)

      __SCREAMING_SNAKE_CASE       :List[str]		    =  (4**9) * (6**6)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  peter_wins_count / total_games_number

      __SCREAMING_SNAKE_CASE       :str		    =  round(a_			,       ndigits=7							)

      return rounded_peter_win_probability


if __name__ == "__main__":
  print(f'{solution() = }')