
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase	:    Tuple          =      get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[str]          = ReformerTokenizer
	_UpperCAmelCase	:		Tuple          = ReformerTokenizerFast
	_UpperCAmelCase	:		List[Any]          = True
	_UpperCAmelCase	:		int          = False
	_UpperCAmelCase	:		Optional[Any]          = True



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				super().setUp()

				_snake_case          =      ReformerTokenizer(lowercase      ,  keep_accents=lowercase     )
				tokenizer.save_pretrained(self.tmpdirname     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      '<s>'
				_snake_case          =      1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase     )      ,  lowercase     )
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase     )      ,  lowercase     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      list(self.get_tokenizer().get_vocab().keys()     )

				self.assertEqual(vocab_keys[0]      ,  '<unk>'     )
				self.assertEqual(vocab_keys[1]      ,  '<s>'     )
				self.assertEqual(vocab_keys[-1]      ,  'j'     )
				self.assertEqual(len(lowercase     )      ,  1_000     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				self.assertEqual(self.get_tokenizer().vocab_size      ,  1_000     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				if not self.test_rust_tokenizer:
							return

				_snake_case          =      self.get_tokenizer()
				_snake_case          =      self.get_rust_tokenizer()

				_snake_case          =      'I was born in 92000, and this is falsé.'

				_snake_case          =      tokenizer.tokenize(lowercase     )
				_snake_case          =      rust_tokenizer.tokenize(lowercase     )
				self.assertListEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase     )
				_snake_case          =      rust_tokenizer.encode(lowercase      ,  add_special_tokens=lowercase     )
				self.assertListEqual(lowercase      ,  lowercase     )

				_snake_case          =      self.get_rust_tokenizer()
				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      rust_tokenizer.encode(lowercase     )
				self.assertListEqual(lowercase      ,  lowercase     )



	def 			A							(  self     :   Any      ,  lowercase     :   Any=15     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      self.rust_tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )

										# Simple input
										_snake_case          =      'This is a simple input'
										_snake_case          =      ['This is a simple input 1', 'This is a simple input 2']
										_snake_case          =      ('This is a simple input', 'This is a pair')
										_snake_case          =      [
										    ('This is a simple input 1', 'This is a simple input 2'),
										    ('This is a simple pair 1', 'This is a simple pair 2'),
										]

										# Simple input tests
										self.assertRaises(lowercase      ,  tokenizer_r.encode      ,  lowercase      ,  max_length=lowercase      ,  padding='max_length'     )

										# Simple input
										self.assertRaises(lowercase      ,  tokenizer_r.encode_plus      ,  lowercase      ,  max_length=lowercase      ,  padding='max_length'     )

										# Simple input
										self.assertRaises(
										    lowercase      ,  tokenizer_r.batch_encode_plus      ,  lowercase      ,  max_length=lowercase      ,  padding='max_length'      ,  )

										# Pair input
										self.assertRaises(lowercase      ,  tokenizer_r.encode      ,  lowercase      ,  max_length=lowercase      ,  padding='max_length'     )

										# Pair input
										self.assertRaises(lowercase      ,  tokenizer_r.encode_plus      ,  lowercase      ,  max_length=lowercase      ,  padding='max_length'     )

										# Pair input
										self.assertRaises(
										    lowercase      ,  tokenizer_r.batch_encode_plus      ,  lowercase      ,  max_length=lowercase      ,  padding='max_length'      ,  )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      ReformerTokenizer(lowercase      ,  keep_accents=lowercase     )

				_snake_case          =      tokenizer.tokenize('This is a test'     )
				self.assertListEqual(lowercase      ,  ['▁This', '▁is', '▁a', '▁t', 'est']     )

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowercase     )      ,  [285, 46, 10, 170, 382]      ,  )

				_snake_case          =      tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
				self.assertListEqual(
				    lowercase      ,  [
				        SPIECE_UNDERLINE + 'I',
				        SPIECE_UNDERLINE + 'was',
				        SPIECE_UNDERLINE + 'b',
				        'or',
				        'n',
				        SPIECE_UNDERLINE + 'in',
				        SPIECE_UNDERLINE + '',
				        '9',
				        '2',
				        '0',
				        '0',
				        '0',
				        ',',
				        SPIECE_UNDERLINE + 'and',
				        SPIECE_UNDERLINE + 'this',
				        SPIECE_UNDERLINE + 'is',
				        SPIECE_UNDERLINE + 'f',
				        'al',
				        's',
				        'é',
				        '.',
				    ]      ,  )
				_snake_case          =      tokenizer.convert_tokens_to_ids(lowercase     )
				self.assertListEqual(
				    lowercase      ,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,  )

				_snake_case          =      tokenizer.convert_ids_to_tokens(lowercase     )
				self.assertListEqual(
				    lowercase      ,  [
				        SPIECE_UNDERLINE + 'I',
				        SPIECE_UNDERLINE + 'was',
				        SPIECE_UNDERLINE + 'b',
				        'or',
				        'n',
				        SPIECE_UNDERLINE + 'in',
				        SPIECE_UNDERLINE + '',
				        '<unk>',
				        '2',
				        '0',
				        '0',
				        '0',
				        ',',
				        SPIECE_UNDERLINE + 'and',
				        SPIECE_UNDERLINE + 'this',
				        SPIECE_UNDERLINE + 'is',
				        SPIECE_UNDERLINE + 'f',
				        'al',
				        's',
				        '<unk>',
				        '.',
				    ]      ,  )



	@cached_property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'     )



	@slow
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      'Hello World!'
				_snake_case          =      [126, 32, 262, 152, 38, 72, 287]

				self.assertListEqual(lowercase      ,  self.big_tokenizer.encode(lowercase     )     )



	@slow
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      (
				    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
				    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
				)
				_snake_case          =      [
				    108,
				    265,
				    24,
				    111,
				    4,
				    258,
				    156,
				    35,
				    28,
				    275,
				    3,
				    259,
				    297,
				    260,
				    84,
				    4,
				    35,
				    110,
				    44,
				    8,
				    259,
				    91,
				    268,
				    21,
				    11,
				    209,
				    274,
				    109,
				    266,
				    277,
				    117,
				    86,
				    93,
				    315,
				    258,
				    278,
				    258,
				    277,
				    258,
				    0,
				    258,
				    288,
				    258,
				    319,
				    258,
				    0,
				    258,
				    0,
				    258,
				    0,
				    258,
				    0,
				    258,
				    287,
				    258,
				    315,
				    258,
				    289,
				    258,
				    278,
				    99,
				    269,
				    266,
				    262,
				    8,
				    259,
				    241,
				    4,
				    217,
				    230,
				    268,
				    266,
				    55,
				    168,
				    106,
				    75,
				    193,
				    266,
				    223,
				    27,
				    49,
				    26,
				    282,
				    25,
				    264,
				    299,
				    19,
				    26,
				    0,
				    258,
				    277,
				    117,
				    86,
				    93,
				    176,
				    183,
				    270,
				    11,
				    262,
				    42,
				    61,
				    265,
				]

				self.assertListEqual(lowercase      ,  self.big_tokenizer.encode(lowercase     )     )



	@require_torch
	@slow
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				import torch

				from transformers import ReformerConfig, ReformerModel

				# Build sequence
				_snake_case          =      list(self.big_tokenizer.get_vocab().keys()     )[:10]
				_snake_case          =      ' '.join(lowercase     )
				_snake_case          =      self.big_tokenizer.encode_plus(lowercase      ,  return_tensors='pt'     )
				_snake_case          =      self.big_tokenizer.batch_encode_plus([sequence, sequence]      ,  return_tensors='pt'     )

				_snake_case          =      ReformerConfig()
				# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
				_snake_case          =      encoded_sequence['input_ids'].shape
				_snake_case          =      ReformerModel(lowercase     )

				# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

				with torch.no_grad():
							model(**lowercase     )
							model(**lowercase     )





	@slow
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
				# fmt: on

				# This tokenizer does not know some characters like ")".
				# That is the reason why we use very simple texts here.
				# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
				_snake_case          =      [
				    'This is a very simple sentence.',
				    'The quick brown fox jumps over the lazy dog.',
				]

				self.tokenizer_integration_test_util(
				    expected_encoding=lowercase      ,  model_name='google/reformer-crime-and-punishment'      ,  revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'      ,  padding=lowercase      ,  sequences=lowercase      ,  )

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
)
_lowerCamelCase	:    Tuple          =      logging.getLogger(__name__)






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

			logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''			)
			if args.tokenizer_type == "bert":
						_snake_case          =      BertTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `[CLS]`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						_snake_case          =      RobertaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `<s>`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						_snake_case          =      GPTaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
						_snake_case          =      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          =      fp.readlines()

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

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

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

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


if __name__ == "__main__":
			main()