


"""simple docstring"""





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 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Dict				):
           for model_result in results.values():
                 for batch_size, sequence_length in zip(model_result['''bs''']					,				model_result['''ss''']				):
                       lowerCAmelCase					:	Dict       =   model_result['''result'''][batch_size][sequence_length]
                       self.assertIsNotNone(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Optional[int]       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	int       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				eager_mode=UpperCamelCase_					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	int       =   TensorFlowBenchmark(UpperCamelCase_				)
           lowerCAmelCase					:	List[Any]       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result				)
           self.check_results_dict_not_empty(results.memory_inference_result				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Dict       =   '''sgugger/tiny-distilbert-classification'''
           lowerCAmelCase					:	int       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				multi_process=UpperCamelCase_					,				only_pretrain_model=UpperCamelCase_					,				)
           lowerCAmelCase					:	Tuple       =   TensorFlowBenchmark(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result				)
           self.check_results_dict_not_empty(results.memory_inference_result				)






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	List[str]       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	Optional[int]       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	str       =   TensorFlowBenchmark(UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result				)
           self.check_results_dict_not_empty(results.memory_inference_result				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Optional[int]       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	Union[str, Any]       =   AutoConfig.from_pretrained(UpperCamelCase_				)
           lowerCAmelCase					:	str       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				eager_mode=UpperCamelCase_					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	List[str]       =   TensorFlowBenchmark(UpperCamelCase_					,				[config]				)
           lowerCAmelCase					:	Optional[int]       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result				)
           self.check_results_dict_not_empty(results.memory_inference_result				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	int       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	List[Any]       =   AutoConfig.from_pretrained(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	List[Any]       =   TensorFlowBenchmark(UpperCamelCase_					,				[config]				)
           lowerCAmelCase					:	Dict       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result				)
           self.check_results_dict_not_empty(results.memory_inference_result				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	Optional[int]       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	int       =   TensorFlowBenchmark(UpperCamelCase_				)
           lowerCAmelCase					:	str       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result				)
           self.check_results_dict_not_empty(results.memory_train_result				)






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	str       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	List[str]       =   AutoConfig.from_pretrained(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	Dict       =   TensorFlowBenchmark(UpperCamelCase_					,				[config]				)
           lowerCAmelCase					:	Tuple       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result				)
           self.check_results_dict_not_empty(results.memory_train_result				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Optional[int]       =   '''patrickvonplaten/t5-tiny-random'''
           lowerCAmelCase					:	List[Any]       =   AutoConfig.from_pretrained(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	Union[str, Any]       =   TensorFlowBenchmark(UpperCamelCase_					,				configs=[config]				)
           lowerCAmelCase					:	List[str]       =   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      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	List[Any]       =   '''sshleifer/tiny-gpt2'''
           lowerCAmelCase					:	List[str]       =   TensorFlowBenchmarkArguments(
               models=[MODEL_ID]					,				training=UpperCamelCase_					,				inference=UpperCamelCase_					,				sequence_lengths=[8]					,				batch_sizes=[1]					,				use_xla=UpperCamelCase_					,				multi_process=UpperCamelCase_					,				)
           lowerCAmelCase					:	Tuple       =   TensorFlowBenchmark(UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result				)
           self.check_results_dict_not_empty(results.memory_inference_result				)






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






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Any       =   '''sshleifer/tiny-gpt2'''

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

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






"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected


