






'''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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						str ,      lowercase							:						Optional[int]			)							->    Optional[Any]:

									'''simple docstring'''







									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(lowercase			)






			def 				A      (   self							:						Optional[Any]			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = 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							:						Any			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """sgugger/tiny-distilbert-classification"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      only_pretrain_model=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = 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							:						Union[str, Any]			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      torchscript=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									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 				A      (   self							:						int			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      fpaa=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = 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							:						List[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									# set architectures equal to `None`
									UpperCamelCase__         = None
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      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 				A      (   self							:						Dict			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									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 				A      (   self							:						Tuple			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      fpaa=lowercase ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase			)
									UpperCamelCase__         = 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							:						List[str]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      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 				A      (   self							:						Optional[Any]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tinier_bart"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      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 				A      (   self							:						List[str]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      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 				A      (   self							:						Union[str, Any]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tinier_bart"""
									UpperCamelCase__         = AutoConfig.from_pretrained(lowercase			)
									UpperCamelCase__         = PyTorchBenchmarkArguments(
									    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=lowercase ,      )
									UpperCamelCase__         = PyTorchBenchmark(lowercase ,      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 				A      (   self							:						Any			)							->    Any:

									'''simple docstring'''







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







			def 				A      (   self							:						int			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = """sshleifer/tiny-gpt2"""

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

									with tempfile.TemporaryDirectory() as tmp_dir:
															UpperCamelCase__         = PyTorchBenchmarkArguments(
															    models=[MODEL_ID] ,      training=lowercase ,      inference=lowercase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      log_filename=os.path.join(lowercase ,      """log.txt"""			) ,      log_print=lowercase ,      trace_memory_line_by_line=lowercase ,      multi_process=lowercase ,      )
															UpperCamelCase__         = PyTorchBenchmark(lowercase			)
															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(lowercase ,      """log.txt"""			)			).exists()			)








'''simple docstring'''


from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowerCamelCase_				: List[Any]    	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
					from .config import (
					    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
					    OnnxConfig,
					    OnnxConfigWithPast,
					    OnnxSeqaSeqConfigWithPast,
					    PatchingSpec,
					)
					from .convert import export, validate_model_outputs
					from .features import FeaturesManager
					from .utils import ParameterFormat, compute_serialized_parameters_size

else:
					import sys

					lowerCamelCase_				: List[Any]    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
