






'''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 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A=True						,      _A="pt"					):
						'''simple docstring'''





						UpperCamelCase__         = {"""add_prefix_space""": True} if isinstance(_A						,      _A					) and not line.startswith(""" """					) else {}
						UpperCamelCase__         = 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 	__magic_name__( _A						,      _A						,      _A=None						,      ):
						'''simple docstring'''





						UpperCamelCase__         = 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							(		_UpperCAmelCase	):


			'''simple docstring'''





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

									'''simple docstring'''







									super().__init__()
									UpperCamelCase__         = Path(A_			).joinpath(type_path + """.source"""			)
									UpperCamelCase__         = Path(A_			).joinpath(type_path + """.target"""			)
									UpperCamelCase__         = self.get_char_lens(self.src_file			)
									UpperCamelCase__         = max_source_length
									UpperCamelCase__         = max_target_length
									assert min(self.src_lens			) > 0, f"found empty line in {self.src_file}"
									UpperCamelCase__         = tokenizer
									UpperCamelCase__         = prefix
									if n_obs is not None:
															UpperCamelCase__         = self.src_lens[:n_obs]
									UpperCamelCase__         = src_lang
									UpperCamelCase__         = tgt_lang






			def __len__(   self							:						Any			)							->    Dict:

									'''simple docstring'''







									return len(self.src_lens			)






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

									'''simple docstring'''







									UpperCamelCase__         = index + 1  # linecache starts at 1
									UpperCamelCase__         = self.prefix + linecache.getline(str(self.src_file			) ,      A_			).rstrip("""\n"""			)
									UpperCamelCase__         = linecache.getline(str(self.tgt_file			) ,      A_			).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 ,      A_			):
															source_line += self.tokenizer.eos_token
															tgt_line += self.tokenizer.eos_token

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

									UpperCamelCase__         = encode_line(A_ ,      A_ ,      self.max_source_length ,      """right"""			)
									UpperCamelCase__         = encode_line(A_ ,      A_ ,      self.max_target_length ,      """right"""			)

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






			@staticmethod
			def 				A      (   lowercase							:						str			)							->    Dict:

									'''simple docstring'''







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







			def 				A      (   self							:						int ,      lowercase							:						int			)							->    Dict[str, torch.Tensor]:

									'''simple docstring'''







									UpperCamelCase__         = torch.stack([x["""input_ids"""] for x in batch]			)
									UpperCamelCase__         = torch.stack([x["""attention_mask"""] for x in batch]			)
									UpperCamelCase__         = torch.stack([x["""decoder_input_ids"""] for x in batch]			)
									UpperCamelCase__         = (
									    self.tokenizer.generator.pad_token_id
									    if isinstance(self.tokenizer ,      A_			)
									    else self.tokenizer.pad_token_id
									)
									UpperCamelCase__         = (
									    self.tokenizer.question_encoder.pad_token_id
									    if isinstance(self.tokenizer ,      A_			)
									    else self.tokenizer.pad_token_id
									)
									UpperCamelCase__         = trim_batch(A_ ,      A_			)
									UpperCamelCase__    ,   UpperCamelCase__         = trim_batch(A_ ,      A_ ,      attention_mask=A_			)
									UpperCamelCase__         = {
									    """input_ids""": source_ids,
									    """attention_mask""": source_mask,
									    """decoder_input_ids""": y,
									}
									return batch
lowerCamelCase_				: Optional[Any]     	=							getLogger(__name__)







def 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = get_git_info()
						save_json(_A						,      os.path.join(_A						,      """git_log.json"""					)					)







def 	__magic_name__( _A						,      _A						,      _A=4						,      **_A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = git.Repo(search_parent_directories=_A					)
						UpperCamelCase__         = {
						    """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 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						return list(map(_A						,      _A					)					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A					):
						'''simple docstring'''





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

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = normalize_answer(_A					).split()
						UpperCamelCase__         = normalize_answer(_A					).split()
						UpperCamelCase__         = Counter(_A					) & Counter(_A					)
						UpperCamelCase__         = sum(common.values()					)
						if num_same == 0:
												return 0
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = (2 * precision * recall) / (precision + recall)
						return fa







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						return normalize_answer(_A					) == normalize_answer(_A					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						assert len(_A					) == len(_A					)
						UpperCamelCase__         = 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 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





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








'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						bool = True ,      lowercase							:						bool = False			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = scheduler
									UpperCamelCase__         = optimizers if isinstance(lowercase ,      (list, tuple)			) else [optimizers]
									UpperCamelCase__         = split_batches
									UpperCamelCase__         = step_with_optimizer
									UpperCamelCase__         = GradientState()






			def 				A      (   self							:						int ,      *lowercase							:						int ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*lowercase ,      **lowercase			)
															return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
															if self.gradient_state.adjust_scheduler:
																					self.scheduler._step_count += 1
															return

									for opt in self.optimizers:
															if opt.step_was_skipped:
																					return
									if self.split_batches:
															# Split batches -> the training dataloader batch size is not changed so one step per training step
															self.scheduler.step(*lowercase ,      **lowercase			)
									else:
															# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
															# num_processes steps per training step
															UpperCamelCase__         = AcceleratorState().num_processes
															for _ in range(lowercase			):
																					# Special case when using OneCycle and `drop_last` was not used
																					if hasattr(self.scheduler ,      """total_steps"""			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																	self.scheduler.step(*lowercase ,      **lowercase			)
																					else:
																											self.scheduler.step(*lowercase ,      **lowercase			)






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

									'''simple docstring'''







									return self.scheduler.get_last_lr()






			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return self.scheduler.state_dict()






			def 				A      (   self							:						Any ,      lowercase							:						int			)							->    Optional[int]:

									'''simple docstring'''







									self.scheduler.load_state_dict(lowercase			)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.scheduler.get_lr()







			def 				A      (   self							:						Union[str, Any] ,      *lowercase							:						List[Any] ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									return self.scheduler.print_lr(*lowercase ,      **lowercase			)
