'''simple docstring'''

import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				List[Any]											= PhobertTokenizer
  __A :				Optional[int]											= False





  def 						UpperCAmelCase_	(	self ):
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCAmelCase_			=      ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
     lowerCAmelCase_			=      dict(zip(_lowerCamelCase    ,     range(len(_lowerCamelCase ) ) ) )
     lowerCAmelCase_			=      ['''#version: 0.2''', '''l à</w>''']
     lowerCAmelCase_			=      {'''unk_token''': '''<unk>'''}

     lowerCAmelCase_			=      os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['''vocab_file'''] )
     lowerCAmelCase_			=      os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['''merges_file'''] )

     with open(self.vocab_file    ,     '''w'''    ,     encoding='''utf-8''' ) as fp:
        for token in vocab_tokens:
           fp.write(F'''{token} {vocab_tokens[token]}\n''' )
     with open(self.merges_file    ,     '''w'''    ,     encoding='''utf-8''' ) as fp:
        fp.write('''\n'''.join(_lowerCamelCase ) )





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     kwargs.update(self.special_tokens_map )
     return PhobertTokenizer.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      '''Tôi là VinAI Research'''
     lowerCAmelCase_			=      '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
     return input_text, output_text







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      PhobertTokenizer(self.vocab_file    ,     self.merges_file    ,     **self.special_tokens_map )
     lowerCAmelCase_			=      '''Tôi là VinAI Research'''
     lowerCAmelCase_			=      '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
     lowerCAmelCase_			=      tokenizer.tokenize(_lowerCamelCase )
     print(_lowerCamelCase )
     self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      tokens + [tokenizer.unk_token]

     lowerCAmelCase_			=      [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )    ,     _lowerCamelCase )






'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

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


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['''bs''']    ,     model_result['''ss'''] ):
           lowerCAmelCase_			=      model_result['''result'''][batch_size][sequence_length]
           self.assertIsNotNone(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sgugger/tiny-distilbert-classification'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     only_pretrain_model=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     torchscript=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Cant do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     fpaa=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     # set architectures equal to `None`
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Can\'t do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     fpaa=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     save_to_csv=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     inference_time_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' )    ,     train_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' )    ,     inference_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' )    ,     train_time_csv_file=os.path.join(_lowerCamelCase    ,     '''train_time.csv''' )    ,     env_info_csv_file=os.path.join(_lowerCamelCase    ,     '''env.csv''' )    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        benchmark.run()
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''env.csv''' ) ).exists() )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase ):
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''sequential''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''cumulative''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''current''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''total''' ) )

     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     log_filename=os.path.join(_lowerCamelCase    ,     '''log.txt''' )    ,     log_print=_lowerCamelCase    ,     trace_memory_line_by_line=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        lowerCAmelCase_			=      benchmark.run()
        _check_summary_is_not_empty(result.inference_summary )
        _check_summary_is_not_empty(result.train_summary )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''log.txt''' ) ).exists() )





