
"""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 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase=True      ,					UpperCAmelCase="pt"			)		->Dict:

   """simple docstring"""



   a_							=			{"add_prefix_space": True} if isinstance(__lowercase      ,					__lowercase			) and not line.startswith(" "			) else {}
   a_							=			padding_side
   return tokenizer(
       [line]      ,					max_length=__lowercase      ,					padding="max_length" if pad_to_max_length else None      ,					truncation=__lowercase      ,					return_tensors=__lowercase      ,					add_special_tokens=__lowercase      ,					**__lowercase      ,					)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase=None      ,					)		->List[str]:

   """simple docstring"""



   a_							=			input_ids.ne(__lowercase			).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 				snake_case    (			SCREAMING_SNAKE_CASE_						):







   def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase="train"    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=""    ,				)					->Dict:
      super().__init__()
      a_							=			Path(__UpperCAmelCase).joinpath(type_path + ".source")
      a_							=			Path(__UpperCAmelCase).joinpath(type_path + ".target")
      a_							=			self.get_char_lens(self.src_file)
      a_							=			max_source_length
      a_							=			max_target_length
      assert min(self.src_lens) > 0, F'''found empty line in {self.src_file}'''
      a_							=			tokenizer
      a_							=			prefix
      if n_obs is not None:
         a_							=			self.src_lens[:n_obs]
      a_							=			src_lang
      a_							=			tgt_lang







   def __len__(      self)					->Optional[Any]:
      return len(self.src_lens)







   def __getitem__(      self    ,				__UpperCAmelCase)					->Tuple:
      a_							=			index + 1  # linecache starts at 1
      a_							=			self.prefix + linecache.getline(str(self.src_file)    ,				__UpperCAmelCase).rstrip("\n")
      a_							=			linecache.getline(str(self.tgt_file)    ,				__UpperCAmelCase).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    ,				__UpperCAmelCase):
         source_line += self.tokenizer.eos_token
         tgt_line += self.tokenizer.eos_token

      # Pad source and target to the right
      a_							=			(
          self.tokenizer.question_encoder if isinstance(self.tokenizer    ,				__UpperCAmelCase) else self.tokenizer
      )
      a_							=			self.tokenizer.generator if isinstance(self.tokenizer    ,				__UpperCAmelCase) else self.tokenizer

      a_							=			encode_line(__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.max_source_length    ,				"right")
      a_							=			encode_line(__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.max_target_length    ,				"right")

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







   @staticmethod
   def 					UpperCAmelCase__     (      __UpperCAmelCase)					->Optional[Any]:
      return [len(__UpperCAmelCase) for x in Path(__UpperCAmelCase).open().readlines()]







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






UpperCamelCase_							=					getLogger(__name__)
def 					UpperCamelCase		( UpperCAmelCase			)		->Tuple:

   """simple docstring"""



   return list(itertools.chain.from_iterable(__lowercase			)			)
def 					UpperCamelCase		( UpperCAmelCase			)		->None:

   """simple docstring"""



   a_							=			get_git_info()
   save_json(__lowercase      ,					os.path.join(__lowercase      ,					"git_log.json"			)			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase=4      ,					**UpperCAmelCase			)		->List[Any]:

   """simple docstring"""



   with open(__lowercase      ,					"w"			) as f:
      json.dump(__lowercase      ,					__lowercase      ,					indent=__lowercase      ,					**__lowercase			)
def 					UpperCamelCase		( UpperCAmelCase			)		->int:

   """simple docstring"""



   with open(__lowercase			) as f:
      return json.load(__lowercase			)
def 					UpperCamelCase		( )		->int:

   """simple docstring"""



   a_							=			git.Repo(search_parent_directories=__lowercase			)
   a_							=			{
       "repo_id": str(__lowercase			),
       "repo_sha": str(repo.head.object.hexsha			),
       "repo_branch": str(repo.active_branch			),
       "hostname": str(socket.gethostname()			),
   }
   return repo_infos
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->List:

   """simple docstring"""



   return list(map(__lowercase      ,					__lowercase			)			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->Tuple:

   """simple docstring"""



   with open(__lowercase      ,					"wb"			) as f:
      return pickle.dump(__lowercase      ,					__lowercase			)
def 					UpperCamelCase		( UpperCAmelCase			)		->Any:

   """simple docstring"""



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

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

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

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

   return white_space_fix(remove_articles(remove_punc(lower(__lowercase			)			)			)			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->Any:

   """simple docstring"""



   a_							=			normalize_answer(__lowercase			).split()
   a_							=			normalize_answer(__lowercase			).split()
   a_							=			Counter(__lowercase			) & Counter(__lowercase			)
   a_							=			sum(common.values()			)
   if num_same == 0:
      return 0
   a_							=			1.0 * num_same / len(__lowercase			)
   a_							=			1.0 * num_same / len(__lowercase			)
   a_							=			(2 * precision * recall) / (precision + recall)
   return fa
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->List[str]:

   """simple docstring"""



   return normalize_answer(__lowercase			) == normalize_answer(__lowercase			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->Dict:

   """simple docstring"""



   assert len(__lowercase			) == len(__lowercase			)
   a_							=			0
   for hypo, pred in zip(__lowercase      ,					__lowercase			):
      em += exact_match_score(__lowercase      ,					__lowercase			)
   if len(__lowercase			) > 0:
      em /= len(__lowercase			)
   return {"em": em}
def 					UpperCamelCase		( UpperCAmelCase			)		->Optional[int]:

   """simple docstring"""



   return model_prefix.startswith("rag"			)



def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase			)		->List[Any]:

   """simple docstring"""



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

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int | None, int | None, float]:
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			_snake_case		=			(low + high) // 2
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					mid + 1       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_cross_sum(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int, int, float]:
			_snake_case	,					_snake_case		=			float('-inf' ), -1
			_snake_case	,					_snake_case		=			float('-inf' ), -1

			_snake_case		=			0
			for i in range(__lowercase       ,					low - 1       ,					-1 ):
						summ += arr[i]
						if summ > left_sum:
									_snake_case		=			summ
									_snake_case		=			i

			_snake_case		=			0
			for i in range(mid + 1       ,					high + 1 ):
						summ += arr[i]
						if summ > right_sum:
									_snake_case		=			summ
									_snake_case		=			i

			return max_left, max_right, (left_sum + right_sum)






def        a_		(       __lowercase   :	int ) ->  float:
			_snake_case		=			[randint(1       ,					__lowercase ) for _ in range(__lowercase )]
			_snake_case		=			time.time()
			max_subarray(__lowercase       ,					0       ,					input_size - 1 )
			_snake_case		=			time.time()
			return end - start






def        a_		(       ) ->  None:
			_snake_case		=			[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
			_snake_case		=			[time_max_subarray(__lowercase ) for input_size in input_sizes]
			print('No of Inputs\t\tTime Taken' )
			for input_size, runtime in zip(__lowercase       ,					__lowercase ):
						print(__lowercase       ,					'\t\t'       ,					__lowercase )
			plt.plot(__lowercase       ,					__lowercase )
			plt.xlabel('Number of Inputs' )
			plt.ylabel('Time taken in seconds' )
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()