
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  :   Dict ,			_UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   int=True ,			_UpperCAmelCase  :   Any="pt"  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	str           =   {"add_prefix_space": True} if isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and not line.startswith(" "  ) else {}
						_lowercase       :	Optional[int]           =   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 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   int=None ,			)   ->			List[Any]:






						'''simple docstring'''

						_lowercase       :	Optional[int]           =   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   __lowercase						(						__snake_case       ):






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






	def __len__(self				: List[Any]    ) ->			Union[str, Any]:
							return len(self.src_lens    )






	def __getitem__(self				: Any      ,	snake_case				: str    ) ->			Dict[str, torch.Tensor]:
							_lowercase       :	Any           =   index + 1  # linecache starts at 1
							_lowercase       :	Dict           =   self.prefix + linecache.getline(str(self.src_file    )      ,	snake_case    ).rstrip("\n"    )
							_lowercase       :	Union[str, Any]           =   linecache.getline(str(self.tgt_file    )      ,	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      ,	snake_case    ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							_lowercase       :	str           =   (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer      ,	snake_case    ) else self.tokenizer
							)
							_lowercase       :	List[str]           =   self.tokenizer.generator if isinstance(self.tokenizer      ,	snake_case    ) else self.tokenizer

							_lowercase       :	Optional[Any]           =   encode_line(snake_case      ,	snake_case      ,	self.max_source_length      ,	"right"    )
							_lowercase       :	Any           =   encode_line(snake_case      ,	snake_case      ,	self.max_target_length      ,	"right"    )

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






	@staticmethod
	def       _a(snake_case				: str    ) ->			Any:
							return [len(snake_case    ) for x in Path(snake_case    ).open().readlines()]






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


UpperCamelCase_						:     int	       =							getLogger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[List]  )   ->			List[str]:






						'''simple docstring'''

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			None:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   get_git_info()
						save_json(_UpperCAmelCase ,			os.path.join(_UpperCAmelCase ,			"git_log.json"  )  )



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






						'''simple docstring'''

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict  )   ->			Union[str, Any]:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   git.Repo(search_parent_directories=_UpperCAmelCase  )
						_lowercase       :	Any           =   {
						    "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 		UpperCamelCase     (       _UpperCAmelCase  :   Callable ,			_UpperCAmelCase  :   Iterable  )   ->			List:






						'''simple docstring'''

						return list(map(_UpperCAmelCase ,			_UpperCAmelCase  )  )



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






						'''simple docstring'''

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int]  )   ->			Optional[Any]:






						'''simple docstring'''

						def remove_articles(_UpperCAmelCase  :   Tuple  ):
												return re.sub(R"\b(a|an|the)\b" ,			" " ,			_UpperCAmelCase  )

						def white_space_fix(_UpperCAmelCase  :   Union[str, Any]  ):
												return " ".join(text.split()  )

						def remove_punc(_UpperCAmelCase  :   Union[str, Any]  ):
												_lowercase       :	Dict           =   set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_UpperCAmelCase  :   Tuple  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase  )  )  )  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   str  )   ->			int:






						'''simple docstring'''

						_lowercase       :	Dict           =   normalize_answer(_UpperCAmelCase  ).split()
						_lowercase       :	Optional[int]           =   normalize_answer(_UpperCAmelCase  ).split()
						_lowercase       :	str           =   Counter(_UpperCAmelCase  ) & Counter(_UpperCAmelCase  )
						_lowercase       :	Optional[int]           =   sum(common.values()  )
						if num_same == 0:
												return 0
						_lowercase       :	Optional[Any]           =   1.0 * num_same / len(_UpperCAmelCase  )
						_lowercase       :	Dict           =   1.0 * num_same / len(_UpperCAmelCase  )
						_lowercase       :	Any           =   (2 * precision * recall) / (precision + recall)
						return fa



def 		UpperCamelCase     (       _UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Optional[Any]  )   ->			Optional[int]:






						'''simple docstring'''

						return normalize_answer(_UpperCAmelCase  ) == normalize_answer(_UpperCAmelCase  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   List[str]  )   ->			Dict:






						'''simple docstring'''

						assert len(_UpperCAmelCase  ) == len(_UpperCAmelCase  )
						_lowercase       :	int           =   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 		UpperCamelCase     (       _UpperCAmelCase  :   Dict  )   ->			str:






						'''simple docstring'''

						return model_prefix.startswith("rag"  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Tuple ,			_UpperCAmelCase  :   List[Any]  )   ->			Dict:






						'''simple docstring'''

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






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCamelCase_						:     Union[str, Any]	       =							datasets.utils.logging.get_logger(__name__)




@dataclass
class   __lowercase						(						datasets.BuilderConfig       ):
	_A       =   None
	_A       =   "utf-8"
	_A       =   None
	_A       =   None
	_A       =   True  # deprecated
	_A       =   None  # deprecated
	_A       =   10 << 20  # 10MB
	_A       =   None




class   __lowercase						(						datasets.ArrowBasedBuilder       ):
	_A       =   JsonConfig






	def       _a(self				: List[Any]    ) ->			Dict:
							if self.config.block_size is not None:
													logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"    )
													_lowercase       :	Union[str, Any]           =   self.config.block_size
							if self.config.use_threads is not True:
													logger.warning(
													    "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."    )
							if self.config.newlines_in_values is not None:
													raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"    )
							return datasets.DatasetInfo(features=self.config.features    )






	def       _a(self				: Any      ,	snake_case				: Union[str, Any]    ) ->			List[str]:
							if not self.config.data_files:
													raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""    )
							_lowercase       :	str           =   dl_manager.download_and_extract(self.config.data_files    )
							if isinstance(snake_case      ,	(str, list, tuple)    ):
													_lowercase       :	int           =   data_files
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	Union[str, Any]           =   [files]
													_lowercase       :	Union[str, Any]           =   [dl_manager.iter_files(snake_case    ) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,	gen_kwargs={"files": files}    )]
							_lowercase       :	Union[str, Any]           =   []
							for split_name, files in data_files.items():
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	List[Any]           =   [files]
													_lowercase       :	Optional[Any]           =   [dl_manager.iter_files(snake_case    ) for file in files]
													splits.append(datasets.SplitGenerator(name=snake_case      ,	gen_kwargs={"files": files}    )    )
							return splits






	def       _a(self				: List[Any]      ,	snake_case				: pa.Table    ) ->			pa.Table:
							if self.config.features is not None:
													# adding missing columns
													for column_name in set(self.config.features    ) - set(pa_table.column_names    ):
																			_lowercase       :	Any           =   self.config.features.arrow_schema.field(snake_case    ).type
																			_lowercase       :	Optional[Any]           =   pa_table.append_column(snake_case      ,	pa.array([None] * len(snake_case    )      ,	type=snake_case    )    )
													# more expensive cast to support nested structures with keys in a different order
													# allows str <-> int/float or str to Audio for example
													_lowercase       :	str           =   table_cast(snake_case      ,	self.config.features.arrow_schema    )
							return pa_table






	def       _a(self				: str      ,	snake_case				: Tuple    ) ->			Tuple:
							for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case    )    ):
													# If the file is one json object and if we need to look at the list of items in one specific field
													if self.config.field is not None:
																			with open(snake_case      ,	encoding=self.config.encoding      ,	errors=self.config.encoding_errors    ) as f:
																									_lowercase       :	Any           =   json.load(snake_case    )

																			# We keep only the field we are interested in
																			_lowercase       :	List[Any]           =   dataset[self.config.field]

																			# We accept two format: a list of dicts or a dict of lists
																			if isinstance(snake_case      ,	(list, tuple)    ):
																									_lowercase       :	Optional[int]           =   set().union(*[row.keys() for row in dataset]    )
																									_lowercase       :	Union[str, Any]           =   {col: [row.get(snake_case    ) for row in dataset] for col in keys}
																			else:
																									_lowercase       :	List[Any]           =   dataset
																			_lowercase       :	int           =   pa.Table.from_pydict(snake_case    )
																			yield file_idx, self._cast_table(snake_case    )

													# If the file has one json object per line
													else:
																			with open(snake_case      ,	"rb"    ) as f:
																									_lowercase       :	Union[str, Any]           =   0
																									# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																									# Set a default minimum value of 16kB if the chunk size is really small
																									_lowercase       :	Dict           =   max(self.config.chunksize // 32      ,	16 << 10    )
																									_lowercase       :	int           =   (
																									    self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
																									)
																									while True:
																															_lowercase       :	Tuple           =   f.read(self.config.chunksize    )
																															if not batch:
																																					break
																															# Finish current line
																															try:
																																					batch += f.readline()
																															except (AttributeError, io.UnsupportedOperation):
																																					batch += readline(snake_case    )
																															# PyArrow only accepts utf-8 encoded bytes
																															if self.config.encoding != "utf-8":
																																					_lowercase       :	Union[str, Any]           =   batch.decode(self.config.encoding      ,	errors=snake_case    ).encode("utf-8"    )
																															try:
																																					while True:
																																											try:
																																																	_lowercase       :	Optional[Any]           =   paj.read_json(
																																																	    io.BytesIO(snake_case    )      ,	read_options=paj.ReadOptions(block_size=snake_case    )    )
																																																	break
																																											except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																	if (
																																																	    isinstance(snake_case      ,	pa.ArrowInvalid    )
																																																	    and "straddling" not in str(snake_case    )
																																																	    or block_size > len(snake_case    )
																																																	):
																																																							raise
																																																	else:
																																																							# Increase the block size in case it was too small.
																																																							# The block size will be reset for the next file.
																																																							logger.debug(
																																																							    F"""Batch of {len(snake_case    )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""    )
																																																							block_size *= 2
																															except pa.ArrowInvalid as e:
																																					try:
																																											with open(
																																											    snake_case      ,	encoding=self.config.encoding      ,	errors=self.config.encoding_errors    ) as f:
																																																	_lowercase       :	Any           =   json.load(snake_case    )
																																					except json.JSONDecodeError:
																																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case    )}: {e}"""    )
																																											raise e
																																					# If possible, parse the file as a list of json objects and exit the loop
																																					if isinstance(snake_case      ,	snake_case    ):  # list is the only sequence type supported in JSON
																																											try:
																																																	_lowercase       :	int           =   set().union(*[row.keys() for row in dataset]    )
																																																	_lowercase       :	Dict           =   {col: [row.get(snake_case    ) for row in dataset] for col in keys}
																																																	_lowercase       :	str           =   pa.Table.from_pydict(snake_case    )
																																											except (pa.ArrowInvalid, AttributeError) as e:
																																																	logger.error(F"""Failed to read file '{file}' with error {type(snake_case    )}: {e}"""    )
																																																	raise ValueError(F"""Not able to read records in the JSON file at {file}."""    ) from None
																																											yield file_idx, self._cast_table(snake_case    )
																																											break
																																					else:
																																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case    )}: {e}"""    )
																																											raise ValueError(
																																											    F"""Not able to read records in the JSON file at {file}. """
																																											    F"""You should probably indicate the field of the JSON file containing your records. """
																																											    F"""This JSON file contain the following fields: {str(list(dataset.keys()    )    )}. """
																																											    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """    ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																															yield (file_idx, batch_idx), self._cast_table(snake_case    )
																															batch_idx += 1




