





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
_SCREAMING_SNAKE_CASE 		=						logging.getLogger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     int     =		argparse.ArgumentParser(
	    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'				)
	parser.add_argument('--file_path'				,	type=__a				,	default='data/dump.txt'				,	help='The path to the data.'				)
	parser.add_argument('--tokenizer_type'				,	type=__a				,	default='bert'				,	choices=['bert', 'roberta', 'gpt2']				)
	parser.add_argument('--tokenizer_name'				,	type=__a				,	default='bert-base-uncased'				,	help='The tokenizer to use.'				)
	parser.add_argument('--dump_file'				,	type=__a				,	default='data/dump'				,	help='The dump file prefix.'				)
	snake_case_						:     Union[str, Any]     =		parser.parse_args()

	logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""				)
	if args.tokenizer_type == "bert":
		snake_case_						:     Dict     =		BertTokenizer.from_pretrained(args.tokenizer_name				)
		snake_case_						:     Any     =		tokenizer.special_tokens_map['cls_token']  # `[CLS]`
		snake_case_						:     Dict     =		tokenizer.special_tokens_map['sep_token']  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		snake_case_						:     Union[str, Any]     =		RobertaTokenizer.from_pretrained(args.tokenizer_name				)
		snake_case_						:     Any     =		tokenizer.special_tokens_map['cls_token']  # `<s>`
		snake_case_						:     Any     =		tokenizer.special_tokens_map['sep_token']  # `</s>`
	elif args.tokenizer_type == "gpt2":
		snake_case_						:     Any     =		GPTaTokenizer.from_pretrained(args.tokenizer_name				)
		snake_case_						:     Union[str, Any]     =		tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
		snake_case_						:     List[Any]     =		tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

	logger.info(f"""Loading text from {args.file_path}"""				)
	with open(args.file_path				,	'r'				,	encoding='utf8'				) as fp:
		snake_case_						:     Dict     =		fp.readlines()

	logger.info('Start encoding'				)
	logger.info(f"""{len(__a				)} examples to process."""				)

	snake_case_						:     Optional[Any]     =		[]
	snake_case_						:     Optional[int]     =		0
	snake_case_						:     Dict     =		1_00_00
	snake_case_						:     List[str]     =		time.time()
	for text in data:
		snake_case_						:     List[str]     =		f"""{bos} {text.strip()} {sep}"""
		snake_case_						:     Dict     =		tokenizer.encode(__a				,	add_special_tokens=__a				)
		rslt.append(__a				)

		iter += 1
		if iter % interval == 0:
			snake_case_						:     Any     =		time.time()
			logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""				)
			snake_case_						:     Any     =		time.time()
	logger.info('Finished binarization'				)
	logger.info(f"""{len(__a				)} examples processed."""				)

	snake_case_						:     List[Any]     =		f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	snake_case_						:     int     =		tokenizer.vocab_size
	if vocab_size < (1 << 16):
		snake_case_						:     Any     =		[np.uintaa(__a				) for d in rslt]
	else:
		snake_case_						:     Optional[Any]     =		[np.intaa(__a				) for d in rslt]
	random.shuffle(rslt_				)
	logger.info(f"""Dump to {dp_file}"""				)
	with open(__a				,	'wb'				) as handle:
		pickle.dump(rslt_				,	__a				,	protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
						main()








import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE 		=						"""docs/source/en/_toctree.yml"""


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[Any]     =		defaultdict(__a				)
	snake_case_						:     Optional[Any]     =		[]
	snake_case_						:     Optional[Any]     =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
		else:
			new_doc_list.append(__a				)

	snake_case_						:     Any     =		new_doc_list
	snake_case_						:     str     =		[key for key, value in counts.items() if value > 1]

	snake_case_						:     Any     =		[]
	for duplicate_key in duplicates:
		snake_case_						:     Any     =		list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
		if len(__a				) > 1:
			raise ValueError(
			    f"""{duplicate_key} is present several times in the documentation table of content at """
			    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
			    'others.'				)
		# Only add this once
		new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
	snake_case_						:     str     =		sorted(__a				,	key=lambda __a				: s["title"].lower()				)

	# "overview" gets special treatment and is always first
	if len(__a				) > 1:
		raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

	overview_doc.extend(__a				)

	# Sort
	return overview_doc


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     int     =		yaml.safe_load(f.read()				)

	# Get to the API doc
	snake_case_						:     str     =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	snake_case_						:     Dict     =		content[api_idx]['sections']

	# Then to the model doc
	snake_case_						:     Tuple     =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	snake_case_						:     Union[str, Any]     =		api_doc[scheduler_idx]['sections']
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	snake_case_						:     int     =		False
	if new_scheduler_doc != scheduler_doc:
		snake_case_						:     int     =		True
		if overwrite:
			snake_case_						:     Union[str, Any]     =		new_scheduler_doc

	if diff:
		if overwrite:
			snake_case_						:     Optional[int]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		yaml.safe_load(f.read()				)

	# Get to the API doc
	snake_case_						:     Any     =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	snake_case_						:     str     =		content[api_idx]['sections']

	# Then to the model doc
	snake_case_						:     List[Any]     =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	snake_case_						:     Dict     =		False
	snake_case_						:     Union[str, Any]     =		api_doc[pipeline_idx]['sections']
	snake_case_						:     Union[str, Any]     =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			snake_case_						:     Optional[Any]     =		pipeline_doc['section']
			snake_case_						:     Optional[int]     =		clean_doc_toc(__a				)
			if overwrite:
				snake_case_						:     Tuple     =		new_sub_pipeline_doc
		new_pipeline_docs.append(__a				)

	# sort overall pipeline doc
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	if new_pipeline_docs != pipeline_docs:
		snake_case_						:     List[str]     =		True
		if overwrite:
			snake_case_						:     List[str]     =		new_pipeline_docs

	if diff:
		if overwrite:
			snake_case_						:     List[Any]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

						check_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)

