




"""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     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							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(_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a )
							lowerCamelCase			      =					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 ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sgugger/tiny-distilbert-classification'''
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, only_pretrain_model=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a )
							lowerCamelCase			      =					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 ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, torchscript=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a )
							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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, fpaa=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a )
							lowerCamelCase			      =					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 ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					AutoConfig.from_pretrained(_a )
							# set architectures equal to `None`
							lowerCamelCase			      =					None
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a		, 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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a )
							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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, fpaa=_a		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a )
							lowerCamelCase			      =					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 ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					AutoConfig.from_pretrained(_a )
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a		, 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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tinier_bart'''
							lowerCamelCase			      =					AutoConfig.from_pretrained(_a )
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a		, 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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''
							lowerCamelCase			      =					AutoConfig.from_pretrained(_a )
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a		, 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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tinier_bart'''
							lowerCamelCase			      =					AutoConfig.from_pretrained(_a )
							lowerCamelCase			      =					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]		, training=_a		, inference=_a		, sequence_lengths=[8]		, batch_sizes=[1]		, multi_process=_a		, )
							lowerCamelCase			      =					PyTorchBenchmark(_a		, 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    _lowerCAmelCase				(       self ):





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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					'''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(_a ):
										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:
										lowerCamelCase			      =					PyTorchBenchmarkArguments(
										    models=[MODEL_ID]		, training=_a		, 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		, multi_process=_a		, )
										lowerCamelCase			      =					PyTorchBenchmark(_a )
										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(_a		, """log.txt""" ) ).exists() )











"""simple docstring"""



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



