



"""simple docstring"""


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__A   :							str										=   logging.getLogger()







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    argparse.ArgumentParser()
				parser.add_argument('''-f'''				)
				_UpperCAmelCase					=    parser.parse_args()
				return args.f
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Optional[Any]   )->None:
							_UpperCAmelCase					=    logging.StreamHandler(sys.stdout   )
							logger.addHandler(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[Any]   )->List[str]:
							_UpperCAmelCase					=    get_gpu_count()

							if n_gpu > 1:
											pass
											# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
											# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
											# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
											# cmd = [sys.executable] + distributed_args + args
											# execute_subprocess_async(cmd, env=self.get_env())
											# XXX: test the results - need to save them first into .json file
							else:
											args.insert(0				,   '''run_glue_deebert.py'''   )
											with patch.object(__UpperCamelCase				,   '''argv'''				,   __UpperCamelCase   ):
															_UpperCAmelCase					=    run_glue_deebert.main()
															for value in result.values():
																			self.assertGreaterEqual(__UpperCamelCase				,   0.6_6_6   )



			@slow
			@require_torch_non_multi_gpu
			def  lowercase__						(		self     :    int   )->Tuple:
							_UpperCAmelCase					=    '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
							self.run_and_check(__UpperCamelCase   )

							_UpperCAmelCase					=    '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
							self.run_and_check(__UpperCamelCase   )

							_UpperCAmelCase					=    '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
							self.run_and_check(__UpperCamelCase   )








"""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 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
							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  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							_UpperCAmelCase					=    '''sgugger/tiny-distilbert-classification'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   only_pretrain_model=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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  lowercase__						(		self     :    Tuple   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   torchscript=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_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(torch_device == '''cpu'''				,   '''Cant do half precision'''   )
			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   fpaa=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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  lowercase__						(		self     :    Tuple   )->Any:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							# set architectures equal to `None`
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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   )



			def  lowercase__						(		self     :    List[Any]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    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  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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  lowercase__						(		self     :    str   )->Dict:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__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   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[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  lowercase__						(		self     :    Optional[int]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[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  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   save_to_csv=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   inference_time_csv_file=os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )				,   train_memory_csv_file=os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )				,   inference_memory_csv_file=os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )				,   train_time_csv_file=os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )				,   env_info_csv_file=os.path.join(__UpperCamelCase				,   '''env.csv'''   )				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											benchmark.run()
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''env.csv'''   )   ).exists()   )



			def  lowercase__						(		self     :    Tuple   )->List[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(__UpperCamelCase     :    str   ):
											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					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   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				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											_UpperCAmelCase					=    benchmark.run()
											_check_summary_is_not_empty(result.inference_summary   )
											_check_summary_is_not_empty(result.train_summary   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''log.txt'''   )   ).exists()   )



