
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
)
lowercase_ 				=			logging.getLogger(__name__)


def 							a__					(							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[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=lowerCAmelCase_       ,			default='''data/dump.txt'''       ,			help='''The path to the data.'''							)
  parser.add_argument('''--tokenizer_type'''       ,			type=lowerCAmelCase_       ,			default='''bert'''       ,			choices=['''bert''', '''roberta''', '''gpt2''']							)
  parser.add_argument('''--tokenizer_name'''       ,			type=lowerCAmelCase_       ,			default='''bert-base-uncased'''       ,			help='''The tokenizer to use.'''							)
  parser.add_argument('''--dump_file'''       ,			type=lowerCAmelCase_       ,			default='''data/dump'''       ,			help='''The dump file prefix.'''							)
  __SCREAMING_SNAKE_CASE      :	Tuple					=		parser.parse_args()

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

  logger.info('''Start encoding'''							)
  logger.info(F'''{len(lowerCAmelCase_							)} examples to process.'''							)

  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[]
  __SCREAMING_SNAKE_CASE      :	List[str]					=		0
  __SCREAMING_SNAKE_CASE      :	List[str]					=		10_000
  __SCREAMING_SNAKE_CASE      :	Any					=		time.time()
  for text in data:
    __SCREAMING_SNAKE_CASE      :	str					=		F'''{bos} {text.strip()} {sep}'''
    __SCREAMING_SNAKE_CASE      :	str					=		tokenizer.encode(lowerCAmelCase_       ,			add_special_tokens=lowerCAmelCase_							)
    rslt.append(lowerCAmelCase_							)

    iter += 1
    if iter % interval == 0:
      __SCREAMING_SNAKE_CASE      :	List[str]					=		time.time()
      logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''							)
      __SCREAMING_SNAKE_CASE      :	Tuple					=		time.time()
  logger.info('''Finished binarization'''							)
  logger.info(F'''{len(lowerCAmelCase_							)} examples processed.'''							)

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


if __name__ == "__main__":
   main()




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor


class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Dict=1_8     ,					_lowerCAmelCase							:							Tuple=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[Any]=True     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							str	) ->			Optional[int]:




     """simple docstring"""
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]	),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							ImageGPTImageProcessor if is_vision_available() else None



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"clusters"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_resize"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"size"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_normalize"	)	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 1_8, "width": 1_8}	)

     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     snake_case_            =       json.loads(image_processor.to_json_string()	)
     for key, value in self.image_processor_dict.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					obj[key]	)	)
        else:
           self.assertEqual(obj[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"image_processor.json"	)
        image_processor_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_json_file(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_pretrained(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  @unittest.skip("ImageGPT requires clusters at initialization"	)
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     pass






def       _lowerCAmelCase    (				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"				)

   snake_case_            =       Image.open(dataset[4]["file"]				)
   snake_case_            =       Image.open(dataset[5]["file"]				)

   snake_case_            =       [imagea, imagea]

   return images



@require_vision
@require_torch
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  @slow
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

     snake_case_            =       prepare_images()

     # test non-batched
     snake_case_            =       image_processing(images[0]     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(1, 1_0_2_4)	)

     snake_case_            =       [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,					_lowerCAmelCase	)

     # test batched
     snake_case_            =       image_processing(_lowerCAmelCase     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(2, 1_0_2_4)	)

     snake_case_            =       [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,					_lowerCAmelCase	)




