



'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers.generation import (
			    MaxLengthCriteria,
			    MaxNewTokensCriteria,
			    MaxTimeCriteria,
			    StoppingCriteriaList,
			    validate_stopping_criteria,
			)

@require_torch
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =3
					snake_case__	     =250

					snake_case__	     =ids_tensor((batch_size, length)					,   __snake_case						)
					snake_case__	     =torch.ones((batch_size, length)					,   device=__snake_case					,   dtype=torch.float						) / length
					return input_ids, scores






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__     ,	snake_case__	     =self._get_tensors(5						)

					snake_case__	     =StoppingCriteriaList(
					    [
					        MaxLengthCriteria(max_length=10						),
					        MaxTimeCriteria(max_time=0.1						),
					    ]						)

					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__     ,	snake_case__	     =self._get_tensors(9						)
					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__     ,	snake_case__	     =self._get_tensors(10						)
					self.assertTrue(criteria(__snake_case					,   __snake_case						)						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =MaxLengthCriteria(max_length=10						)

					snake_case__     ,	snake_case__	     =self._get_tensors(5						)
					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__     ,	snake_case__	     =self._get_tensors(9						)
					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__     ,	snake_case__	     =self._get_tensors(10						)
					self.assertTrue(criteria(__snake_case					,   __snake_case						)						)






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =MaxNewTokensCriteria(start_length=5					,   max_new_tokens=5						)

					snake_case__     ,	snake_case__	     =self._get_tensors(5						)
					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__     ,	snake_case__	     =self._get_tensors(9						)
					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__     ,	snake_case__	     =self._get_tensors(10						)
					self.assertTrue(criteria(__snake_case					,   __snake_case						)						)

					snake_case__	     =StoppingCriteriaList([criteria]						)
					self.assertEqual(criteria_list.max_length					,   10						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__     ,	snake_case__	     =self._get_tensors(5						)

					snake_case__	     =MaxTimeCriteria(max_time=0.1						)
					self.assertFalse(criteria(__snake_case					,   __snake_case						)						)

					snake_case__	     =MaxTimeCriteria(max_time=0.1					,   initial_timestamp=time.time() - 0.2						)
					self.assertTrue(criteria(__snake_case					,   __snake_case						)						)






			def 						_lowercase  (     self						)							->							Tuple:
					validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)					,   10						)

					with self.assertWarns(__snake_case						):
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)					,   11						)

					snake_case__	     =validate_stopping_criteria(StoppingCriteriaList()					,   11						)

					self.assertEqual(len(__snake_case						)					,   1						)











'''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
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				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."""    )
      _lowerCAmelCase					=				parser.parse_args()

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

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

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

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

      _lowerCAmelCase					=				f"{args.dump_file}.{args.tokenizer_name}.pickle"
      _lowerCAmelCase					=				tokenizer.vocab_size
      if vocab_size < (1 << 16):
            _lowerCAmelCase					=				[np.uintaa(lowerCAmelCase    ) for d in rslt]
      else:
            _lowerCAmelCase					=				[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()






