

import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCAmelCase_			      =      logging.getLogger(__name__)


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      argparse.ArgumentParser(
						    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''			)
						parser.add_argument(
						    '''--dataset_name'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default='''wikitext'''    ,						help='''Name of the training. Explore datasets at: hf.co/datasets.'''    ,						)
						parser.add_argument(
						    '''--dataset_config'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default='''wikitext-103-raw-v1'''    ,						help='''Configuration name of the dataset.'''			)
						parser.add_argument(
						    '''--tokenizer_name_or_path'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default='''sayakpaul/unigram-tokenizer-wikitext'''    ,						help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''    ,						)
						parser.add_argument(
						    '''--shard_size'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default=1000    ,						help='''Number of entries to go in a single shard.'''    ,						)
						parser.add_argument('''--split'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default='''train'''    ,						choices=['''train''', '''test''', '''validation''']			)
						parser.add_argument(
						    '''--limit'''    ,						default=SCREAMING_SNAKE_CASE__    ,						type=SCREAMING_SNAKE_CASE__    ,						help='''Limit the number of shards (used for debugging).'''    ,						)
						parser.add_argument(
						    '''--max_length'''    ,						type=SCREAMING_SNAKE_CASE__    ,						default=512    ,						help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
						    ''' sequence length that is a multiple of 8.'''    ,						)
						parser.add_argument(
						    '''--output_dir'''    ,						default='''tf-tpu'''    ,						type=SCREAMING_SNAKE_CASE__    ,						help='''Output directory where the TFRecord shards will be saved. If the'''
						    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
						    ''' shards will be directly saved to a Google Cloud Storage bucket.'''    ,						)

						snake_case_  =      parser.parse_args()
						return args


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						def fn(SCREAMING_SNAKE_CASE__			):
												return tokenizer(examples['''text''']			)

						return fn


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      []
						for i in range(len(tokenized_data['''input_ids''']			)			):
												snake_case_  =      {
												    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]			)			),
												    '''attention_mask''': tf.train.Feature(
												        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]			)			),
												}
												snake_case_  =      tf.train.Features(feature=SCREAMING_SNAKE_CASE__			)
												snake_case_  =      tf.train.Example(features=SCREAMING_SNAKE_CASE__			)
												snake_case_  =      example.SerializeToString()
												records.append(SCREAMING_SNAKE_CASE__			)
						return records


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      datasets.load_dataset(args.dataset_name    ,						args.dataset_config    ,						split=args.split			)

						if args.limit is not None:
												snake_case_  =      min(len(SCREAMING_SNAKE_CASE__			)    ,						args.limit			)
												snake_case_  =      dataset.select(range(SCREAMING_SNAKE_CASE__			)			)
												print(F'''Limiting the dataset to {args.limit} entries.'''			)

						snake_case_  =      AutoTokenizer.from_pretrained(args.tokenizer_name_or_path			)

						# Handle output directory creation.
						# For serializing into a Google Cloud Storage Bucket, one needs to first
						# create a bucket.
						if "gs" not in args.output_dir:
												if not os.path.exists(args.output_dir			):
																		os.makedirs(args.output_dir			)
												snake_case_  =      os.path.join(args.output_dir    ,						args.split			)
												if not os.path.exists(SCREAMING_SNAKE_CASE__			):
																		os.makedirs(SCREAMING_SNAKE_CASE__			)
						else:
												snake_case_  =      os.path.join(args.output_dir    ,						args.split			)

						# Tokenize the whole dataset at once.
						snake_case_  =      tokenize_function(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      dataset.map(SCREAMING_SNAKE_CASE__    ,						batched=SCREAMING_SNAKE_CASE__    ,						num_proc=4    ,						remove_columns=['''text''']			)

						# We need to concatenate all our texts together, and then split the result
						# into chunks of a fixed size, which we will call block_size. To do this, we
						# will use the map method again, with the option batched=True. When we use batched=True,
						# the function we pass to map() will be passed multiple inputs at once, allowing us
						# to group them into more or fewer examples than we had in the input.
						# This allows us to create our new fixed-length samples. The advantage of this
						# method is that we don't lose a whole lot of content from the dataset compared to the
						# case where we simply tokenize with a pre-defined max_length.

						def group_texts(SCREAMING_SNAKE_CASE__			):
												# Concatenate all texts.
												snake_case_  =      {k: sum(examples[k]    ,						[]			) for k in examples.keys()}
												snake_case_  =      len(concatenated_examples[list(examples.keys()			)[0]]			)
												# We drop the small remainder, though you could add padding instead if the model supports it
												# In this, as in all things, we advise you to follow your heart 🫀
												snake_case_  =      (total_length // args.max_length) * args.max_length
												# Split by chunks of max_len.
												snake_case_  =      {
												    k: [t[i : i + args.max_length] for i in range(0    ,						SCREAMING_SNAKE_CASE__    ,						args.max_length			)]
												    for k, t in concatenated_examples.items()
												}
												return result

						snake_case_  =      dataset_tokenized.map(SCREAMING_SNAKE_CASE__    ,						batched=SCREAMING_SNAKE_CASE__    ,						batch_size=1000    ,						num_proc=4			)

						snake_case_  =      0
						snake_case_  =      0
						for shard in range(0    ,						len(SCREAMING_SNAKE_CASE__			)    ,						args.shard_size			):
												snake_case_  =      grouped_dataset[shard : shard + args.shard_size]
												snake_case_  =      len(dataset_snapshot['''input_ids''']			)
												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						F'''dataset-{shard_count}-{records_containing}.tfrecord'''			)
												snake_case_  =      get_serialized_examples(SCREAMING_SNAKE_CASE__			)

												with tf.io.TFRecordWriter(SCREAMING_SNAKE_CASE__			) as out_file:
																		for i in range(len(SCREAMING_SNAKE_CASE__			)			):
																								snake_case_  =      serialized_examples[i]
																								out_file.write(SCREAMING_SNAKE_CASE__			)
																		print('''Wrote file {} containing {} records'''.format(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)			)

												shard_count += 1
												total_records += records_containing

						with open(F'''split-{args.split}-records-count.txt'''    ,						'''w'''			) as f:
												print(F'''Total {args.split} records: {total_records}'''    ,						file=SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
					lowerCAmelCase_			      =      parse_args()
					main(args)



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 						a__			(				A__,      A__,      A__,      A__,      A__=True,      A__="pt"				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					{'add_prefix_space': True} if isinstance(A__,      A__				) and not line.startswith(' '				) else {}
			SCREAMING_SNAKE_CASE_		:     Dict					=					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 						a__			(				A__,      A__,      A__=None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__="train"			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=""			,						):





										"""simple docstring"""




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






							def __len__(      self       ):





										"""simple docstring"""




										return len(self.src_lens       )






							def __getitem__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     Dict					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_source_length			,						'right'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_target_length			,						'right'       )

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






							@staticmethod
							def 					UpperCamelCase__     (      lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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







lowerCAmelCase__			:      Tuple								=getLogger(__name__)






def 						a__			(				A__				):
			return list(itertools.chain.from_iterable(A__				)				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_git_info()
			save_json(A__,      os.path.join(A__,      'git_log.json'				)				)






def 						a__			(				A__,      A__,      A__=4,      **A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__,      indent=A__,      **A__				)






def 						a__			(				A__				):
			with open(A__				) as f:
						return json.load(A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					git.Repo(search_parent_directories=A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					{
			    '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 						a__			(				A__,      A__				):
			return list(map(A__,      A__				)				)






def 						a__			(				A__,      A__				):
			with open(A__,      'wb'				) as f:
						return pickle.dump(A__,      A__				)






def 						a__			(				A__				):

			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__				):
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					normalize_answer(A__				).split()
			SCREAMING_SNAKE_CASE_		:     List[str]					=					normalize_answer(A__				).split()
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					Counter(A__				) & Counter(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					sum(common.values()				)
			if num_same == 0:
						return 0
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1.0 * num_same / len(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					1.0 * num_same / len(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					(2 * precision * recall) / (precision + recall)
			return fa






def 						a__			(				A__,      A__				):
			return normalize_answer(A__				) == normalize_answer(A__				)






def 						a__			(				A__,      A__				):
			assert len(A__				) == len(A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					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 						a__			(				A__				):
			return model_prefix.startswith('rag'				)






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





