


'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		Tuple       =			[
      '[UNK]',
      '[CLS]',
      '[SEP]',
      '[PAD]',
      '[MASK]',
      'want',
      '##want',
      '##ed',
      'wa',
      'un',
      'runn',
      '##ing',
      ',',
      'low',
      'lowest',
  ]
  __A  :		Optional[int]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  with open(self.vocab_file      ,      'w'      ,      encoding='utf-8'						) as vocab_writer:
   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

  __A  :		Optional[int]       =			{
      'do_resize': True,
      'size': 20,
      'do_center_crop': True,
      'crop_size': 18,
      'do_normalize': True,
      'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
      'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
  }
  __A  :		str       =			os.path.join(self.tmpdirname      ,      __lowercase						)
  with open(self.image_processor_file      ,      'w'      ,      encoding='utf-8'						) as fp:
   json.dump(__lowercase      ,      __lowercase						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return BertTokenizer.from_pretrained(self.tmpdirname      ,      **__lowercase						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return BertTokenizerFast.from_pretrained(self.tmpdirname      ,      **__lowercase						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]
  __A  :		List[Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]
  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_tokenizer()
  __A  :		int       =			self.get_rust_tokenizer()
  __A  :		Tuple       =			self.get_image_processor()

  __A  :		List[Any]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)
  processor_slow.save_pretrained(self.tmpdirname						)
  __A  :		Dict       =			AlignProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__lowercase						)

  __A  :		Optional[Any]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)
  processor_fast.save_pretrained(self.tmpdirname						)
  __A  :		str       =			AlignProcessor.from_pretrained(self.tmpdirname						)

  self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()						)
  self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()						)
  self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()						)
  self.assertIsInstance(processor_slow.tokenizer      ,      __lowercase						)
  self.assertIsInstance(processor_fast.tokenizer      ,      __lowercase						)

  self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()						)
  self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()						)
  self.assertIsInstance(processor_slow.image_processor      ,      __lowercase						)
  self.assertIsInstance(processor_fast.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			AlignProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Dict       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Any       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		int       =			AlignProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

  self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()						)
  self.assertIsInstance(processor.tokenizer      ,      __lowercase						)

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Optional[Any]       =			self.get_tokenizer()

  __A  :		Optional[int]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		str       =			processor(images=__lowercase      ,      return_tensors='np'						)

  for key in input_image_proc.keys():
   self.assertAlmostEqual(input_image_proc[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		int       =			self.get_tokenizer()

  __A  :		List[str]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Tuple       =			'lower newer'

  __A  :		Optional[int]       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      padding='max_length'      ,      max_length=64						)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.get_image_processor()
  __A  :		Tuple       =			self.get_tokenizer()

  __A  :		List[str]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		Any       =			self.prepare_image_inputs()

  __A  :		Tuple       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Optional[int]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Dict       =			processor.batch_decode(__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		List[str]       =			AlignProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Tuple       =			'lower newer'
  __A  :		List[Any]       =			self.prepare_image_inputs()

  __A  :		Any       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      processor.model_input_names						)










'''simple docstring'''
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
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


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

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

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

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

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

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


if __name__ == "__main__":
 main()






