





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			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=True     ,							UpperCamelCase__="pt"   ):







							'''simple docstring'''






							snake_case_           =					{'add_prefix_space': True} if isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and not line.startswith(' '   ) else {}
							snake_case_           =					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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None     ,							):







							'''simple docstring'''






							snake_case_           =					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       ( lowercase_    ):




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




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




				def __getitem__(      self   ,     snake_case ):
											snake_case_           =					index + 1  # linecache starts at 1
											snake_case_           =					self.prefix + linecache.getline(str(self.src_file )   ,     snake_case ).rstrip('\n' )
											snake_case_           =					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
											snake_case_           =					(
											    self.tokenizer.question_encoder if isinstance(self.tokenizer   ,     snake_case ) else self.tokenizer
											)
											snake_case_           =					self.tokenizer.generator if isinstance(self.tokenizer   ,     snake_case ) else self.tokenizer

											snake_case_           =					encode_line(snake_case   ,     snake_case   ,     self.max_source_length   ,     'right' )
											snake_case_           =					encode_line(snake_case   ,     snake_case   ,     self.max_target_length   ,     'right' )

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




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




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					torch.stack([x['input_ids'] for x in batch] )
											snake_case_           =					torch.stack([x['attention_mask'] for x in batch] )
											snake_case_           =					torch.stack([x['decoder_input_ids'] for x in batch] )
											snake_case_           =					(
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer   ,     snake_case )
											    else self.tokenizer.pad_token_id
											)
											snake_case_           =					(
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer   ,     snake_case )
											    else self.tokenizer.pad_token_id
											)
											snake_case_           =					trim_batch(snake_case   ,     snake_case )
											snake_case_       ,					snake_case_           =					trim_batch(snake_case   ,     snake_case   ,     attention_mask=snake_case )
											snake_case_           =					{
											    'input_ids': source_ids,
											    'attention_mask': source_mask,
											    'decoder_input_ids': y,
											}
											return batch



_UpperCAmelCase	:     Any		       = getLogger(__name__)





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					get_git_info()
							save_json(UpperCamelCase__     ,							os.path.join(UpperCamelCase__     ,							'git_log.json'   )   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=4     ,							**UpperCamelCase__   ):







							'''simple docstring'''






							with open(UpperCamelCase__     ,							'w'   ) as f:
														json.dump(UpperCamelCase__     ,							UpperCamelCase__     ,							indent=UpperCamelCase__     ,							**UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					git.Repo(search_parent_directories=UpperCamelCase__   )
							snake_case_           =					{
							    '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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return list(map(UpperCamelCase__     ,							UpperCamelCase__   )   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							with open(UpperCamelCase__     ,							'wb'   ) as f:
														return pickle.dump(UpperCamelCase__     ,							UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							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__   ):
														snake_case_           =					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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return normalize_answer(UpperCamelCase__   ) == normalize_answer(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							assert len(UpperCamelCase__   ) == len(UpperCamelCase__   )
							snake_case_           =					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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return model_prefix.startswith('rag'   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.inf

							def set_batch_size(UpperCamelCase__   ) -> None:
														nonlocal batch_size
														if isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ):
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
														elif isinstance(UpperCamelCase__     ,							UpperCamelCase__   ) and feature.dtype == "binary":
																					snake_case_           =					min(UpperCamelCase__     ,							config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

							_visit(UpperCamelCase__     ,							UpperCamelCase__   )

							return None if batch_size is np.inf else batch_size







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = None   ,     **snake_case   ,     ):
											super().__init__(
											    snake_case   ,     split=snake_case   ,     features=snake_case   ,     cache_dir=snake_case   ,     keep_in_memory=snake_case   ,     streaming=snake_case   ,     num_proc=snake_case   ,     **snake_case   ,     )
											snake_case_           =					path_or_paths if isinstance(snake_case   ,     snake_case ) else {self.split: path_or_paths}
											snake_case_           =					_PACKAGED_DATASETS_MODULES['parquet'][1]
											snake_case_           =					Parquet(
											    cache_dir=snake_case   ,     data_files=snake_case   ,     features=snake_case   ,     hash=snake_case   ,     **snake_case   ,     )




				def 					a							(      self ):
											# Build iterable dataset
											if self.streaming:
																		snake_case_           =					self.builder.as_streaming_dataset(split=self.split )
											# Build regular (map-style) dataset
											else:
																		snake_case_           =					None
																		snake_case_           =					None
																		snake_case_           =					None
																		snake_case_           =					None

																		self.builder.download_and_prepare(
																		    download_config=snake_case   ,     download_mode=snake_case   ,     verification_mode=snake_case   ,     base_path=snake_case   ,     num_proc=self.num_proc   ,     )
																		snake_case_           =					self.builder.as_dataset(
																		    split=self.split   ,     verification_mode=snake_case   ,     in_memory=self.keep_in_memory )
											return dataset







class 				lowercase       :




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					dataset
											snake_case_           =					path_or_buf
											snake_case_           =					batch_size or get_writer_batch_size(dataset.features )
											snake_case_           =					parquet_writer_kwargs




				def 					a							(      self ):
											snake_case_           =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

											if isinstance(self.path_or_buf   ,     (str, bytes, os.PathLike) ):
																		with open(self.path_or_buf   ,     'wb+' ) as buffer:
																									snake_case_           =					self._write(file_obj=snake_case   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											else:
																		snake_case_           =					self._write(file_obj=self.path_or_buf   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											return written




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     **snake_case ):
											snake_case_           =					0
											snake_case_           =					parquet_writer_kwargs.pop('path_or_buf'   ,     snake_case )
											snake_case_           =					self.dataset.features.arrow_schema

											snake_case_           =					pq.ParquetWriter(snake_case   ,     schema=snake_case   ,     **snake_case )

											for offset in logging.tqdm(
											    range(0   ,     len(self.dataset )   ,     snake_case )   ,     unit='ba'   ,     disable=not logging.is_progress_bar_enabled()   ,     desc='Creating parquet from Arrow format'   ,     ):
																		snake_case_           =					query_table(
																		    table=self.dataset._data   ,     key=slice(snake_case   ,     offset + batch_size )   ,     indices=self.dataset._indices if self.dataset._indices is not None else None   ,     )
																		writer.write_table(snake_case )
																		written += batch.nbytes
											writer.close()
											return written




