


import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


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       UpperCamelCase__     (  ):
 __lowerCamelCase		:							int							=						argparse.ArgumentParser(description='PyTorch Transformer Language Model'						)
 parser.add_argument('--model_name'			,       type=SCREAMING_SNAKE_CASE__			,       default='transfo-xl-wt103'			,       help='pretrained model name'						)
 parser.add_argument(
     '--split'			,       type=SCREAMING_SNAKE_CASE__			,       default='test'			,       choices=['all', 'valid', 'test']			,       help='which split to evaluate'						)
 parser.add_argument('--batch_size'			,       type=SCREAMING_SNAKE_CASE__			,       default=10			,       help='batch size'						)
 parser.add_argument('--tgt_len'			,       type=SCREAMING_SNAKE_CASE__			,       default=128			,       help='number of tokens to predict'						)
 parser.add_argument('--ext_len'			,       type=SCREAMING_SNAKE_CASE__			,       default=0			,       help='length of the extended context'						)
 parser.add_argument('--mem_len'			,       type=SCREAMING_SNAKE_CASE__			,       default=1_600			,       help='length of the retained previous heads'						)
 parser.add_argument('--clamp_len'			,       type=SCREAMING_SNAKE_CASE__			,       default=1_000			,       help='max positional embedding index'						)
 parser.add_argument('--no_cuda'			,       action='store_true'			,       help='Do not use CUDA even though CUA is available'						)
 parser.add_argument('--work_dir'			,       type=SCREAMING_SNAKE_CASE__			,       required=SCREAMING_SNAKE_CASE__			,       help='path to the work_dir'						)
 parser.add_argument('--no_log'			,       action='store_true'			,       help='do not log the eval result'						)
 parser.add_argument('--same_length'			,       action='store_true'			,       help='set same length attention with masking'						)
 parser.add_argument('--server_ip'			,       type=SCREAMING_SNAKE_CASE__			,       default=''			,       help='Can be used for distant debugging.'						)
 parser.add_argument('--server_port'			,       type=SCREAMING_SNAKE_CASE__			,       default=''			,       help='Can be used for distant debugging.'						)
 __lowerCamelCase		:							List[Any]							=						parser.parse_args()
 assert args.ext_len >= 0, "extended context length must be non-negative"

 if args.server_ip and args.server_port:
  # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
  import ptvsd

  print('Waiting for debugger attach'						)
  ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,       redirect_output=SCREAMING_SNAKE_CASE__						)
  ptvsd.wait_for_attach()

 __lowerCamelCase		:							Union[str, Any]							=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'						)
 logger.info('device: {}'.format(SCREAMING_SNAKE_CASE__						)						)

 # Load a pre-processed dataset
 # You can also build the corpus yourself using TransfoXLCorpus methods
 # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
 # and tokenizing the dataset
 # The pre-processed corpus is a convertion (using the conversion script )
 __lowerCamelCase		:							List[str]							=						TransfoXLCorpus.from_pretrained(args.model_name						)

 __lowerCamelCase		:							Union[str, Any]							=						corpus.get_iterator('valid'			,       args.batch_size			,       args.tgt_len			,       device=SCREAMING_SNAKE_CASE__			,       ext_len=args.ext_len						)
 __lowerCamelCase		:							int							=						corpus.get_iterator('test'			,       args.batch_size			,       args.tgt_len			,       device=SCREAMING_SNAKE_CASE__			,       ext_len=args.ext_len						)

 # Load a pre-trained model
 __lowerCamelCase		:							Dict							=						TransfoXLLMHeadModel.from_pretrained(args.model_name						)
 model.to(SCREAMING_SNAKE_CASE__						)

 logger.info(
     'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
         args.batch_size			,       args.tgt_len			,       args.ext_len			,       args.mem_len			,       args.clamp_len						)						)

 model.reset_memory_length(args.mem_len						)
 if args.clamp_len > 0:
  __lowerCamelCase		:							Any							=						args.clamp_len
 if args.same_length:
  __lowerCamelCase		:							Any							=						True

 ###############################################################################
 # Evaluation code
 ###############################################################################
 def evaluate(SCREAMING_SNAKE_CASE__						):
  # Turn on evaluation mode which disables dropout.
  model.eval()
  __lowerCamelCase					,     __lowerCamelCase		:							List[Any]							=						0, 0.0
  __lowerCamelCase		:							Any							=						time.time()
  with torch.no_grad():
   __lowerCamelCase		:							List[str]							=						None
   for idx, (data, target, seq_len) in enumerate(SCREAMING_SNAKE_CASE__						):
    __lowerCamelCase		:							str							=						model(SCREAMING_SNAKE_CASE__			,       lm_labels=SCREAMING_SNAKE_CASE__			,       mems=SCREAMING_SNAKE_CASE__						)
    __lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase		:							Optional[Any]							=						ret
    __lowerCamelCase		:							List[str]							=						loss.mean()
    total_loss += seq_len * loss.item()
    total_len += seq_len
   __lowerCamelCase		:							Tuple							=						time.time() - start_time
  logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(SCREAMING_SNAKE_CASE__			,       1_000 * total_time / (idx + 1)						)						)
  return total_loss / total_len

 # Run on test data.
 if args.split == "all":
  __lowerCamelCase		:							List[Any]							=						evaluate(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							str							=						evaluate(SCREAMING_SNAKE_CASE__						)
 elif args.split == "valid":
  __lowerCamelCase		:							Optional[Any]							=						evaluate(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Dict							=						None
 elif args.split == "test":
  __lowerCamelCase		:							List[str]							=						evaluate(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Union[str, Any]							=						None

 def format_log(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							List[str]							=						'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       math.exp(SCREAMING_SNAKE_CASE__						)						)
  return log_str

 __lowerCamelCase		:							List[Any]							=						''
 if valid_loss is not None:
  log_str += format_log(SCREAMING_SNAKE_CASE__			,       'valid'						)
 if test_loss is not None:
  log_str += format_log(SCREAMING_SNAKE_CASE__			,       'test'						)

 logger.info('=' * 100						)
 logger.info(SCREAMING_SNAKE_CASE__						)
 logger.info('=' * 100						)


if __name__ == "__main__":
       main()





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_           =	get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class        A_					(       __UpperCamelCase					,      unittest.TestCase    ):




      '''simple docstring'''


      __snake_case					       =   ErnieMTokenizer
      __snake_case					       =   False
      __snake_case					       =   True
      __snake_case					       =   False
      __snake_case					       =   False




      def      _snake_case	(				self:       Optional[int]				):
       super().setUp()

       # We have a SentencePiece fixture for testing
       __lowerCamelCase		:							List[str]							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)
       tokenizer.save_pretrained(self.tmpdirname				)




      def      _snake_case	(				self:       Dict						,      a:       List[Any]				):
       __lowerCamelCase		:							Optional[Any]							=						'this is a test'
       __lowerCamelCase		:							Optional[int]							=						'this is a test'
       return input_text, output_text




      def      _snake_case	(				self:       Tuple				):
       __lowerCamelCase		:							List[str]							=						'<pad>'
       __lowerCamelCase		:							Union[str, Any]							=						0

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




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]						,      '<pad>'				)
       self.assertEqual(vocab_keys[1]						,      '<unk>'				)
       self.assertEqual(vocab_keys[-1]						,      '▁eloquent'				)
       self.assertEqual(len(a				)						,      3_0000				)




      def      _snake_case	(				self:       Tuple				):
       self.assertEqual(self.get_tokenizer().vocab_size						,      3_0000				)




      def      _snake_case	(				self:       int				):
       if not self.test_rust_tokenizer:
        return

       __lowerCamelCase		:							Any							=						self.get_tokenizer()
       __lowerCamelCase		:							Optional[Any]							=						self.get_rust_tokenizer()

       __lowerCamelCase		:							Optional[int]							=						'I was born in 92000, and this is falsé.'

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize(a				)
       __lowerCamelCase		:							Dict							=						rust_tokenizer.tokenize(a				)
       self.assertListEqual(a						,      a				)

       __lowerCamelCase		:							List[str]							=						tokenizer.encode(a						,      add_special_tokens=a				)
       __lowerCamelCase		:							List[Any]							=						rust_tokenizer.encode(a						,      add_special_tokens=a				)
       self.assertListEqual(a						,      a				)

       __lowerCamelCase		:							str							=						self.get_rust_tokenizer()
       __lowerCamelCase		:							str							=						tokenizer.encode(a				)
       __lowerCamelCase		:							List[str]							=						rust_tokenizer.encode(a				)
       self.assertListEqual(a						,      a				)




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Union[str, Any]							=						ErnieMTokenizer(a						,      do_lower_case=a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							List[str]							=						tokenizer.tokenize('This is a test'				)
       self.assertListEqual(a						,      ['▁this', '▁is', '▁a', '▁test']				)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)						,      [48, 25, 21, 1289]				)

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           a						,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']				)
       __lowerCamelCase		:							Any							=						tokenizer.convert_tokens_to_ids(a				)
       self.assertListEqual(a						,      [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

       __lowerCamelCase		:							int							=						tokenizer.convert_ids_to_tokens(a				)
       self.assertListEqual(
           a						,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']						,      )




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							Tuple							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							Tuple							=						tokenizer.encode('sequence builders'				)
       __lowerCamelCase		:							List[Any]							=						tokenizer.encode('multi-sequence build'				)

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.build_inputs_with_special_tokens(a				)
       __lowerCamelCase		:							Tuple							=						tokenizer.build_inputs_with_special_tokens(a						,      a				)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]




      @slow
      def      _snake_case	(				self:       List[Any]				):
       # fmt: off
       __lowerCamelCase		:							int							=						{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a						,      model_name='susnato/ernie-m-base_pytorch'						,      sequences=[
               'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
               'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
               'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
               'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
               'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
               'conditioning on both left and right context in all layers.',
               'The quick brown fox jumps over the lazy dog.',
           ]						,      )

