






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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=None      ,       )			->							Tuple:
  UpperCamelCase_:   Dict        =							input_ids.ne(UpperCAmelCase__    ).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 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







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





       def __len__(   self				):
         return len(self.src_lens				)





       def __getitem__(   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   Union[str, Any]        =							index + 1  # linecache starts at 1
         UpperCamelCase_:   Any        =							self.prefix + linecache.getline(str(self.src_file				)    ,     _lowerCamelCase				).rstrip('\n'				)
         UpperCamelCase_:   str        =							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
         UpperCamelCase_:   int        =							(
             self.tokenizer.question_encoder if isinstance(self.tokenizer    ,     _lowerCamelCase				) else self.tokenizer
         )
         UpperCamelCase_:   Optional[Any]        =							self.tokenizer.generator if isinstance(self.tokenizer    ,     _lowerCamelCase				) else self.tokenizer

         UpperCamelCase_:   Optional[int]        =							encode_line(_lowerCamelCase    ,     _lowerCamelCase    ,     self.max_source_length    ,     'right'				)
         UpperCamelCase_:   Optional[Any]        =							encode_line(_lowerCamelCase    ,     _lowerCamelCase    ,     self.max_target_length    ,     'right'				)

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





       @staticmethod
       def      _a       (   _lowerCamelCase				):
         return [len(_lowerCamelCase				) for x in Path(_lowerCamelCase				).open().readlines()]





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







A_						:						int						      =					getLogger(__name__)

def     snake_case   (UpperCAmelCase__    )			->							Optional[int]:
  return list(itertools.chain.from_iterable(UpperCAmelCase__    )    )

def     snake_case   (UpperCAmelCase__    )			->							None:
  UpperCamelCase_:   Any        =							get_git_info()
  save_json(UpperCAmelCase__      ,       os.path.join(UpperCAmelCase__      ,       'git_log.json'    )    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=4      ,       **UpperCAmelCase__    )			->							Dict:
  with open(UpperCAmelCase__      ,       'w'    ) as f:
    json.dump(UpperCAmelCase__      ,       UpperCAmelCase__      ,       indent=UpperCAmelCase__      ,       **UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Any:
  with open(UpperCAmelCase__    ) as f:
    return json.load(UpperCAmelCase__    )

def     snake_case   ()			->							Optional[Any]:
  UpperCamelCase_:   List[Any]        =							git.Repo(search_parent_directories=UpperCAmelCase__    )
  UpperCamelCase_:   Optional[int]        =							{
      'repo_id': str(UpperCAmelCase__    ),
      'repo_sha': str(repo.head.object.hexsha    ),
      'repo_branch': str(repo.active_branch    ),
      'hostname': str(socket.gethostname()    ),
  }
  return repo_infos

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List:
  return list(map(UpperCAmelCase__      ,       UpperCAmelCase__    )    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  with open(UpperCAmelCase__      ,       'wb'    ) as f:
    return pickle.dump(UpperCAmelCase__      ,       UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Tuple:

  def remove_articles(UpperCAmelCase__    ):
    return re.sub(R'\b(a|an|the)\b'      ,       ' '      ,       UpperCAmelCase__    )

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

  def remove_punc(UpperCAmelCase__    ):
    UpperCamelCase_:   Dict        =							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(UpperCAmelCase__    )    )    )    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   int        =							normalize_answer(UpperCAmelCase__    ).split()
  UpperCamelCase_:   Optional[int]        =							normalize_answer(UpperCAmelCase__    ).split()
  UpperCamelCase_:   Any        =							Counter(UpperCAmelCase__    ) & Counter(UpperCAmelCase__    )
  UpperCamelCase_:   List[str]        =							sum(common.values()    )
  if num_same == 0:
    return 0
  UpperCamelCase_:   List[str]        =							1.0 * num_same / len(UpperCAmelCase__    )
  UpperCamelCase_:   str        =							1.0 * num_same / len(UpperCAmelCase__    )
  UpperCamelCase_:   int        =							(2 * precision * recall) / (precision + recall)
  return fa

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:
  return normalize_answer(UpperCAmelCase__    ) == normalize_answer(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Dict:
  assert len(UpperCAmelCase__    ) == len(UpperCAmelCase__    )
  UpperCamelCase_:   Optional[Any]        =							0
  for hypo, pred in zip(UpperCAmelCase__      ,       UpperCAmelCase__    ):
    em += exact_match_score(UpperCAmelCase__      ,       UpperCAmelCase__    )
  if len(UpperCAmelCase__    ) > 0:
    em /= len(UpperCAmelCase__    )
  return {"em": em}

def     snake_case   (UpperCAmelCase__    )			->							int:
  return model_prefix.startswith('rag'    )

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






"""simple docstring"""




import gc
import threading
import time

import psutil
import torch


class 			__UpperCamelCase							:






							def __init__(  self )      ->					Any:
								a :						str      =							psutil.Process()
								a :						str      =							False






							def        __a  (  self )      ->					str:
								a :						List[Any]      =							-1

								while True:
									a :						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        __a  (  self )      ->					List[str]:
								a :						Dict      =							True
								a :						Optional[Any]      =							threading.Thread(target=self.peak_monitor )
								a :						List[Any]      =							True
								self.thread.start()






							def        __a  (  self )      ->					List[str]:
								a :						Optional[int]      =							False
								self.thread.join()
								return self.cpu_memory_peak
a		:					Tuple	      =     PeakCPUMemory()






def  _SCREAMING_SNAKE_CASE						(      )					->List[Any]:






	'''simple docstring'''






	a :						List[str]      =							{"time": time.time()}

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

	# CPU mem
	a :						Tuple      =							psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()   ):
		a :						Any      =							torch.cuda.memory_allocated(_lowercase   )
	torch.cuda.reset_peak_memory_stats()

	return measures






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






	'''simple docstring'''






	a :						int      =							{"time": time.time() - start_measures["time"]}

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

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

	# GPU mem
	for i in range(torch.cuda.device_count()   ):
		a :						Optional[Any]      =							(torch.cuda.memory_allocated(_lowercase   ) - start_measures[str(_lowercase   )]) / 2**20
		a :						Optional[Any]      =							(torch.cuda.max_memory_allocated(_lowercase   ) - start_measures[str(_lowercase   )]) / 2**20

	return measures







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       Union[str, Any]   )					->Optional[int]:






	'''simple docstring'''






	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(_lowercase   )]:.2f}MiB"""   )
		a :						str      =							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"""   )


