

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__							(		SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							Optional[Any]=True		,				SCREAMING_SNAKE_CASE_:							Optional[int]="pt"					)	->					Tuple:




				'''simple docstring'''





				A__						=	{"add_prefix_space": True} if isinstance(__snake_case		,				__snake_case					) and not line.startswith(" "					) else {}
				A__						=	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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[str]		,				SCREAMING_SNAKE_CASE_:							Optional[int]=None		,				)	->					Optional[int]:




				'''simple docstring'''





				A__						=	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__  (    a_	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase					,					lowercase="train"					,					lowercase=None					,					lowercase=None					,					lowercase=None					,					lowercase=""					,					)     ->  Union[str, Any]:
							'''simple docstring'''






							super().__init__()
							A__						=	Path(_lowerCamelCase				).joinpath(type_path + ".source"				)
							A__						=	Path(_lowerCamelCase				).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				)     ->  Dict:
							'''simple docstring'''






							return len(self.src_lens				)


			def __getitem__(			self					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	index + 1  # linecache starts at 1
							A__						=	self.prefix + linecache.getline(str(self.src_file				)					,					_lowerCamelCase				).rstrip("\n"				)
							A__						=	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
							A__						=	(
							    self.tokenizer.question_encoder if isinstance(self.tokenizer					,					_lowerCamelCase				) else self.tokenizer
							)
							A__						=	self.tokenizer.generator if isinstance(self.tokenizer					,					_lowerCamelCase				) else self.tokenizer

							A__						=	encode_line(_lowerCamelCase					,					_lowerCamelCase					,					self.max_source_length					,					"right"				)
							A__						=	encode_line(_lowerCamelCase					,					_lowerCamelCase					,					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						(			lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							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					,					_lowerCamelCase				)
							    else self.tokenizer.pad_token_id
							)
							A__						=	(
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer					,					_lowerCamelCase				)
							    else self.tokenizer.pad_token_id
							)
							A__						=	trim_batch(_lowerCamelCase					,					_lowerCamelCase				)
							A__			, A__						=	trim_batch(_lowerCamelCase					,					_lowerCamelCase					,					attention_mask=_lowerCamelCase				)
							A__						=	{
							    "input_ids": source_ids,
							    "attention_mask": source_mask,
							    "decoder_input_ids": y,
							}
							return batch

lowerCAmelCase__		      =       getLogger(__name__)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[List]					)	->					List[str]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str					)	->					Any:




				'''simple docstring'''





				A__						=	get_git_info()
				save_json(__snake_case		,				os.path.join(__snake_case		,				"git_log.json"					)					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							int=4		,				**SCREAMING_SNAKE_CASE_:							List[str]					)	->					Optional[int]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]					)	->					Dict:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		)	->					List[Any]:




				'''simple docstring'''





				A__						=	git.Repo(search_parent_directories=__snake_case					)
				A__						=	{
				    "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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Callable		,				SCREAMING_SNAKE_CASE_:							Iterable					)	->					Tuple:




				'''simple docstring'''





				return list(map(__snake_case		,				__snake_case					)					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Tuple:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Tuple:




				'''simple docstring'''





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

				def white_space_fix(SCREAMING_SNAKE_CASE_:							Tuple					):
								return " ".join(text.split()					)

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

				def lower(SCREAMING_SNAKE_CASE_:							List[str]					):
								return text.lower()

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							str					)	->					List[str]:




				'''simple docstring'''





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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Optional[int]:




				'''simple docstring'''





				return normalize_answer(__snake_case					) == normalize_answer(__snake_case					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,				SCREAMING_SNAKE_CASE_:							List[str]					)	->					List[Any]:




				'''simple docstring'''





				assert len(__snake_case					) == len(__snake_case					)
				A__						=	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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Dict:




				'''simple docstring'''





				return model_prefix.startswith("rag"					)





def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							int					)	->					str:




				'''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(__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
												A__						=	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 os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


