



"""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				(  __snake_case					,__snake_case					,__snake_case					,__snake_case					,__snake_case=True					,__snake_case="pt"	)						->   str:
			__lowerCAmelCase      :  List[Any]												=       {"add_prefix_space": True} if isinstance(__snake_case					,__snake_case	) and not line.startswith(" "	) else {}
			__lowerCAmelCase      :  Tuple												=       padding_side
			return tokenizer(
			    [line]					,max_length=__snake_case					,padding="max_length" if pad_to_max_length else None					,truncation=__snake_case					,return_tensors=__snake_case					,add_special_tokens=__snake_case					,**__snake_case					,)


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case=None					,)						->   Dict:
			__lowerCAmelCase      :  List[Any]												=       input_ids.ne(__snake_case	).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__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def __init__(     self:		int       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Any="train"       ,			_SCREAMING_SNAKE_CASE:		int=None       ,			_SCREAMING_SNAKE_CASE:		List[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Tuple=None       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=""       ,			)				->       List[str]:

				"""simple docstring"""

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

	def __len__(     self:		List[str])				->       int:

				"""simple docstring"""

				return len(self.src_lens)

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

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       index + 1  # linecache starts at 1
				__lowerCAmelCase      :  int												=       self.prefix + linecache.getline(str(self.src_file)       ,			_SCREAMING_SNAKE_CASE).rstrip("\n")
				__lowerCAmelCase      :  int												=       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
				__lowerCAmelCase      :  Any												=       (
				    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,			_SCREAMING_SNAKE_CASE) else self.tokenizer
				)
				__lowerCAmelCase      :  Tuple												=       self.tokenizer.generator if isinstance(self.tokenizer       ,			_SCREAMING_SNAKE_CASE) else self.tokenizer

				__lowerCAmelCase      :  Optional[Any]												=       encode_line(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			self.max_source_length       ,			"right")
				__lowerCAmelCase      :  int												=       encode_line(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			self.max_target_length       ,			"right")

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

	@staticmethod
	def 							_SCREAMING_SNAKE_CASE   (     _SCREAMING_SNAKE_CASE:		List[Any])				->       List[str]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Dict[str, torch.Tensor]:

				"""simple docstring"""

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






__snake_case							:   Tuple      =    getLogger(__name__)


def 			_lowercase				(  __snake_case	)						->   Tuple:
			return list(itertools.chain.from_iterable(__snake_case	)	)


def 			_lowercase				(  __snake_case	)						->   None:
			__lowerCAmelCase      :  Any												=       get_git_info()
			save_json(__snake_case					,os.path.join(__snake_case					,"git_log.json"	)	)


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case=4					,**__snake_case	)						->   Union[str, Any]:
			with open(__snake_case					,"w"	) as f:
						json.dump(__snake_case					,__snake_case					,indent=__snake_case					,**__snake_case	)


def 			_lowercase				(  __snake_case	)						->   List[Any]:
			with open(__snake_case	) as f:
						return json.load(__snake_case	)


def 			_lowercase				(  )						->   Optional[Any]:
			__lowerCAmelCase      :  Dict												=       git.Repo(search_parent_directories=__snake_case	)
			__lowerCAmelCase      :  Union[str, Any]												=       {
			    "repo_id": str(__snake_case	),
			    "repo_sha": str(repo.head.object.hexsha	),
			    "repo_branch": str(repo.active_branch	),
			    "hostname": str(socket.gethostname()	),
			}
			return repo_infos


def 			_lowercase				(  __snake_case					,__snake_case	)						->   List:
			return list(map(__snake_case					,__snake_case	)	)


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Optional[Any]:
			with open(__snake_case					,"wb"	) as f:
						return pickle.dump(__snake_case					,__snake_case	)


def 			_lowercase				(  __snake_case	)						->   Union[str, Any]:

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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(__snake_case	)	)	)	)


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


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			return normalize_answer(__snake_case	) == normalize_answer(__snake_case	)


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			assert len(__snake_case	) == len(__snake_case	)
			__lowerCAmelCase      :  Optional[int]												=       0
			for hypo, pred in zip(__snake_case					,__snake_case	):
						em += exact_match_score(__snake_case					,__snake_case	)
			if len(__snake_case	) > 0:
						em /= len(__snake_case	)
			return {"em": em}


def 			_lowercase				(  __snake_case	)						->   Tuple:
			return model_prefix.startswith("rag"	)





def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Optional[Any]:
			__lowerCAmelCase      :  List[Any]												=       {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			__lowerCAmelCase      :  List[str]												=       "dropout_rate"
			for p in extra_params:
						if getattr(__snake_case					,__snake_case					,__snake_case	):
									if not hasattr(__snake_case					,__snake_case	) and not hasattr(__snake_case					,equivalent_param[p]	):
												logger.info("config doesn't have a `{}` attribute".format(__snake_case	)	)
												delattr(__snake_case					,__snake_case	)
												continue
									__lowerCAmelCase      :  List[Any]												=       p if hasattr(__snake_case					,__snake_case	) else equivalent_param[p]
									setattr(__snake_case					,__snake_case					,getattr(__snake_case					,__snake_case	)	)
									delattr(__snake_case					,__snake_case	)
			return hparams, config




"""simple docstring"""






import gc
import threading
import time

import psutil
import torch



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       psutil.Process()
				__lowerCAmelCase      :  str												=       False

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       -1

				while True:
							__lowerCAmelCase      :  str												=       max(self.process.memory_info().rss       ,			self.cpu_memory_peak)

							# can't sleep or will not catch the peak right (this comment is here on purpose)
							if not self.peak_monitoring:
										break

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       threading.Thread(target=self.peak_monitor)
				__lowerCAmelCase      :  Tuple												=       True
				self.thread.start()







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       False
				self.thread.join()
				return self.cpu_memory_peak






__snake_case							:   Tuple      =    PeakCPUMemory()


def 			_lowercase				(  )						->   str:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  Optional[Any]												=       psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       torch.cuda.memory_allocated(__snake_case	)
			torch.cuda.reset_peak_memory_stats()

			return measures


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  str												=       (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase      :  List[str]												=       (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       (torch.cuda.memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20
						__lowerCAmelCase      :  Any												=       (torch.cuda.max_memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20

			return measures





def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			print(F"""{description}:"""	)
			print(F"""- Time: {measures['time']:.2f}s"""	)
			for i in range(torch.cuda.device_count()	):
						print(F"""- GPU {i} allocated: {measures[str(__snake_case	)]:.2f}MiB"""	)
						__lowerCAmelCase      :  Optional[Any]												=       measures[F"""{i}-peak"""]
						print(F"""- GPU {i} peak: {peak:.2f}MiB"""	)
			print(F"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""	)
			print(F"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""	)