






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 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Any:
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['bs']		,		model_result['ss'] ):
           UpperCamelCase_									=     model_result['result'][batch_size][sequence_length]
           self.assertIsNotNone(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		eager_mode=_UpperCAmelCase		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> str:
     UpperCamelCase_									=     'sgugger/tiny-distilbert-classification'
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		only_pretrain_model=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> Optional[int]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> str:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		eager_mode=_UpperCAmelCase		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase		,		[config] )
     UpperCamelCase_									=     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 )			-> Union[str, Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase		,		[config] )
     UpperCamelCase_									=     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 )			-> Tuple:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> str:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase		,		[config] )
     UpperCamelCase_									=     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 )			-> Optional[Any]:
     UpperCamelCase_									=     'patrickvonplaten/t5-tiny-random'
     UpperCamelCase_									=     AutoConfig.from_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase		,		configs=[config] )
     UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     UpperCamelCase_									=     TensorFlowBenchmarkArguments(
         models=[MODEL_ID]		,		training=_UpperCAmelCase		,		inference=_UpperCAmelCase		,		sequence_lengths=[8]		,		batch_sizes=[1]		,		use_xla=_UpperCAmelCase		,		multi_process=_UpperCAmelCase		,		)
     UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase )
     UpperCamelCase_									=     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 )			-> Optional[Any]:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'
     with tempfile.TemporaryDirectory() as tmp_dir:
        UpperCamelCase_									=     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		,		)
        UpperCamelCase_									=     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    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     'sshleifer/tiny-gpt2'

     def _check_summary_is_not_empty(_UpperCAmelCase ):
        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:
        UpperCamelCase_									=     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		,		)
        UpperCamelCase_									=     TensorFlowBenchmark(_UpperCAmelCase )
        UpperCamelCase_									=     benchmark.run()
        _check_summary_is_not_empty(result.inference_summary )
        self.assertTrue(Path(os.path.join(_UpperCAmelCase		,		'log.txt' ) ).exists() )













import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


snake_case__      :	Optional[int]       	=      pytest.mark.integration






@pytest.mark.parametrize('path'    ,      ['paws', 'csv'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_dataset(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.parametrize('path'    ,      ['accuracy'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_metric(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_info(__lowercase    ,      config_name=__lowercase)
   assert info.config_name == config_name
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_config_info(__lowercase    ,      config_name=__lowercase)






@pytest.mark.parametrize(
    'path, expected'    ,      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_names(__lowercase)
   assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'    ,      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert list(infos.keys()) == expected_configs
   UpperCamelCase_									=     expected_configs[0]
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_split_names(__lowercase    ,      config_name=__lowercase)





