



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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       str   )						->					str:
									"""simple docstring"""







									for model_result in results.values():
															for batch_size, sequence_length in zip(model_result["""bs"""]			,     model_result["""ss"""]   ):
																					__lowercase					=				model_result["""result"""][batch_size][sequence_length]
																					self.assertIsNotNone(lowercase   )





			def 		snake_case__			(				self	:       str   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       List[Any]   )						->					int:
									"""simple docstring"""







									__lowercase					=				"""sgugger/tiny-distilbert-classification"""
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     only_pretrain_model=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       Dict   )						->					str:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     torchscript=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase   )
									__lowercase					=				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 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     fpaa=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									# set architectures equal to `None`
									__lowercase					=				None
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase			,     configs=[config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase   )
									__lowercase					=				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 		snake_case__			(				self	:       List[str]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     fpaa=lowercase			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result   )
									self.check_results_dict_not_empty(results.memory_train_result   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase			,     configs=[config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       str   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tinier_bart"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase			,     configs=[config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )





			def 		snake_case__			(				self	:       List[str]   )						->					str:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase			,     configs=[config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result   )
									self.check_results_dict_not_empty(results.memory_train_result   )





			def 		snake_case__			(				self	:       int   )						->					str:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tinier_bart"""
									__lowercase					=				AutoConfig.from_pretrained(lowercase   )
									__lowercase					=				PyTorchBenchmarkArguments(
									    models=[MODEL_ID]			,     training=lowercase			,     inference=lowercase			,     sequence_lengths=[8]			,     batch_sizes=[1]			,     multi_process=lowercase			,     )
									__lowercase					=				PyTorchBenchmark(lowercase			,     configs=[config]   )
									__lowercase					=				benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result   )
									self.check_results_dict_not_empty(results.memory_train_result   )





			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															__lowercase					=				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			,     )
															__lowercase					=				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 		snake_case__			(				self	:       Optional[Any]   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				"""sshleifer/tiny-gpt2"""

									def _check_summary_is_not_empty(lowercase	:       Optional[Any]   ):
															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:
															__lowercase					=				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			,     )
															__lowercase					=				PyTorchBenchmark(lowercase   )
															__lowercase					=				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()   )










import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


UpperCamelCase__ =						"scheduler_config.json"
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  List[Any]      =			1
			lowercase__      :  Tuple      =			2
			lowercase__      :  Union[str, Any]      =			3
			lowercase__      :  Union[str, Any]      =			4
			lowercase__      :  str      =			5
			lowercase__      :  Any      =			6
			lowercase__      :  Any      =			7
			lowercase__      :  List[str]      =			8
			lowercase__      :  Union[str, Any]      =			9
			lowercase__      :  int      =			10
			lowercase__      :  List[str]      =			11
			lowercase__      :  List[Any]      =			12
			lowercase__      :  str      =			13
			lowercase__      :  Optional[int]      =			14





@dataclass
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  torch.FloatTensor





class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  Optional[int]      =			SCHEDULER_CONFIG_NAME
			lowercase__      :  int      =			[]
			lowercase__      :  Dict      =			True





			@classmethod
			def 		snake_case__			(				cls	:       str			,     lowercase	:       Dict[str, Any] = None			,     lowercase	:       Optional[str] = None			,     lowercase	:       Any=False			,     **lowercase	:       List[str]			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase	,			__lowercase	,			__lowercase					=				cls.load_config(
									    pretrained_model_name_or_path=lowercase			,     subfolder=lowercase			,     return_unused_kwargs=lowercase			,     return_commit_hash=lowercase			,     **lowercase			,     )
									return cls.from_config(lowercase			,     return_unused_kwargs=lowercase			,     **lowercase   )





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       Union[str, os.PathLike]			,     lowercase	:       bool = False			,     **lowercase	:       List[str]   )						->					Optional[Any]:
									"""simple docstring"""







									self.save_config(save_directory=lowercase			,     push_to_hub=lowercase			,     **lowercase   )





			@property
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Dict:
									"""simple docstring"""







									return self._get_compatibles()







			@classmethod
			def 		snake_case__			(				cls	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				list(set([cls.__name__] + cls._compatibles   )   )
									__lowercase					=				importlib.import_module(__name__.split("""."""   )[0]   )
									__lowercase					=				[
									    getattr(lowercase			,     lowercase   ) for c in compatible_classes_str if hasattr(lowercase			,     lowercase   )
									]
									return compatible_classes





