





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      List[Any]				,			a__       :      Union[str, Any]      ):





         """simple docstring"""



         for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['''bs''']				,			model_result['''ss''']      ):
               __snake_case							       =   model_result['''result'''][batch_size][sequence_length]
               self.assertIsNotNone(a__      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			eager_mode=a__				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   '''sgugger/tiny-distilbert-classification'''
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=a__				,			only_pretrain_model=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   AutoConfig.from_pretrained(a__      )
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			eager_mode=a__				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__				,			[config]      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   AutoConfig.from_pretrained(a__      )
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__				,			[config]      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result      )
         self.check_results_dict_not_empty(results.memory_train_result      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   AutoConfig.from_pretrained(a__      )
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__				,			[config]      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result      )
         self.check_results_dict_not_empty(results.memory_train_result      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   '''patrickvonplaten/t5-tiny-random'''
         __snake_case							       =   AutoConfig.from_pretrained(a__      )
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__				,			configs=[config]      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''      )      ) == 0				,			'''Cannot do xla on CPU.'''      )
      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         __snake_case							       =   TensorFlowBenchmarkArguments(
             models=[MODEL_ID]				,			training=a__				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			use_xla=a__				,			multi_process=a__				,			)
         __snake_case							       =   TensorFlowBenchmark(a__      )
         __snake_case							       =   benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result      )
         self.check_results_dict_not_empty(results.memory_inference_result      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''
         with tempfile.TemporaryDirectory() as tmp_dir:
            __snake_case							       =   TensorFlowBenchmarkArguments(
                models=[MODEL_ID]				,			inference=a__				,			save_to_csv=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			inference_time_csv_file=os.path.join(a__				,			'''inf_time.csv'''      )				,			inference_memory_csv_file=os.path.join(a__				,			'''inf_mem.csv'''      )				,			env_info_csv_file=os.path.join(a__				,			'''env.csv'''      )				,			multi_process=a__				,			)
            __snake_case							       =   TensorFlowBenchmark(a__      )
            benchmark.run()
            self.assertTrue(Path(os.path.join(a__				,			'''inf_time.csv'''      )      ).exists()      )
            self.assertTrue(Path(os.path.join(a__				,			'''inf_mem.csv'''      )      ).exists()      )
            self.assertTrue(Path(os.path.join(a__				,			'''env.csv'''      )      ).exists()      )


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''sshleifer/tiny-gpt2'''

         def _check_summary_is_not_empty(a__       :      List[str]      ):
            self.assertTrue(hasattr(a__				,			'''sequential'''      )      )
            self.assertTrue(hasattr(a__				,			'''cumulative'''      )      )
            self.assertTrue(hasattr(a__				,			'''current'''      )      )
            self.assertTrue(hasattr(a__				,			'''total'''      )      )

         with tempfile.TemporaryDirectory() as tmp_dir:
            __snake_case							       =   TensorFlowBenchmarkArguments(
                models=[MODEL_ID]				,			inference=a__				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			log_filename=os.path.join(a__				,			'''log.txt'''      )				,			log_print=a__				,			trace_memory_line_by_line=a__				,			eager_mode=a__				,			multi_process=a__				,			)
            __snake_case							       =   TensorFlowBenchmark(a__      )
            __snake_case							       =   benchmark.run()
            _check_summary_is_not_empty(result.inference_summary      )
            self.assertTrue(Path(os.path.join(a__				,			'''log.txt'''      )      ).exists()      )








import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_				=			get_tests_dir('fixtures/test_sentencepiece_bpe.model')

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    BartphoTokenizer
      A_						:				List[str]							=    False
      A_						:				Optional[Any]							=    True


      def    a (self       :      Tuple      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
         __snake_case							       =   dict(zip(a__				,			range(len(a__      )      )      )      )
         __snake_case							       =   {'''unk_token''': '''<unk>'''}

         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''monolingual_vocab_file''']      )
         with open(self.monolingual_vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as fp:
            for token in vocab_tokens:
               fp.write(f"""{token} {vocab_tokens[token]}\n"""      )

         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         tokenizer.save_pretrained(self.tmpdirname      )


      def    a (self       :      str				,			**a__       :      str      ):





         """simple docstring"""



         kwargs.update(self.special_tokens_map      )
         return BartphoTokenizer.from_pretrained(self.tmpdirname				,			**a__      )


      def    a (self       :      str				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''This is a<unk><unk> test'''
         return input_text, output_text


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   BartphoTokenizer(a__				,			self.monolingual_vocab_file				,			**self.special_tokens_map      )
         __snake_case							       =   '''This is a là test'''
         __snake_case							       =   '''▁This ▁is ▁a ▁l à ▁t est'''.split()
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )

         __snake_case							       =   tokens + [tokenizer.unk_token]
         __snake_case							       =   [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

