


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


snake_case							: str      =   logging.get_logger(__name__)






def 							__lowercase     (				__lowerCAmelCase							:       List[Any]=None      ,					__lowerCAmelCase							:       Optional[Any]=None     ):
							return field(default_factory=lambda: default      ,					metadata=__lowerCAmelCase     )







@dataclass
class  snake_case_     :
					UpperCAmelCase__      :					List[str]					   =    list_field(
					    default=[]			,		metadata={
					        '''help''': (
					            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
					            ''' of all available models'''
					        )
					    }			,		)

					UpperCAmelCase__      :					List[int]					   =    list_field(
					    default=[8]			,		metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}   )

					UpperCAmelCase__      :					List[int]					   =    list_field(
					    default=[8, 3_2, 1_2_8, 5_1_2]			,		metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}			,		)

					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}			,		)
					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}			,		)
					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}   )
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Use FP16 to accelerate inference.'''}   )
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Benchmark training of model'''}   )
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Verbose memory tracing'''}   )
					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}			,		)
					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={
					        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
					    }			,		)
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Trace memory line by line'''}   )
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Save result to a CSV file'''}   )
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Save all print statements in a log file'''}   )
					UpperCAmelCase__      :					bool					   =    field(default=lowerCamelCase_			,		metadata={'''help''': '''Whether to print environment information'''}   )
					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={
					        '''help''': (
					            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
					            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
					            ''' for debugging / testing and on TPU.'''
					        )
					    }			,		)
					UpperCAmelCase__      :					str					   =    field(
					    default=f'inference_time_{round(time()   )}.csv'			,		metadata={'''help''': '''CSV filename used if saving time results to csv.'''}			,		)
					UpperCAmelCase__      :					str					   =    field(
					    default=f'inference_memory_{round(time()   )}.csv'			,		metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}			,		)
					UpperCAmelCase__      :					str					   =    field(
					    default=f'train_time_{round(time()   )}.csv'			,		metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}			,		)
					UpperCAmelCase__      :					str					   =    field(
					    default=f'train_memory_{round(time()   )}.csv'			,		metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}			,		)
					UpperCAmelCase__      :					str					   =    field(
					    default=f'env_info_{round(time()   )}.csv'			,		metadata={'''help''': '''CSV filename used if saving environment information.'''}			,		)
					UpperCAmelCase__      :					str					   =    field(
					    default=f'log_{round(time()   )}.csv'			,		metadata={'''help''': '''Log filename used if print statements are saved in log.'''}			,		)
					UpperCAmelCase__      :					int					   =    field(default=3			,		metadata={'''help''': '''Times an experiment will be run.'''}   )
					UpperCAmelCase__      :					bool					   =    field(
					    default=lowerCamelCase_			,		metadata={
					        '''help''': (
					            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
					            ''' model weights.'''
					        )
					    }			,		)
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												warnings.warn(
												    F'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
												    ' are deprecated in general and it is advised to use external Benchmarking libraries '
												    ' to benchmark Transformer models.'    ,__snake_case    ,)
					def 				lowerCamelCase__(   self     :Dict   )      ->					Dict:
												return json.dumps(dataclasses.asdict(self   )    ,indent=2   )
					@property
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					List[str]:
												if len(self.models   ) <= 0:
																			raise ValueError(
																			    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
																			    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'   )
												return self.models



					@property
					def 				lowerCamelCase__(   self     :Dict   )      ->					Optional[int]:
												if not self.multi_process:
																			return False
												elif self.is_tpu:
																			logger.info('Multiprocessing is currently not possible on TPU.'   )
																			return False
												else:
																			return True








import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Any=1_0     ):
							a__    =  []
							for _ in range(__lowerCAmelCase     ):
														lrs.append(scheduler.get_lr()[0]     )
														scheduler.step()
							return lrs






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       List[str]=1_0     ):
							a__    =  []
							for step in range(__lowerCAmelCase     ):
														lrs.append(scheduler.get_lr()[0]     )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												a__    =  os.path.join(__lowerCAmelCase      ,					'schedule.bin'     )
																												torch.save(scheduler.state_dict()      ,					__lowerCAmelCase     )

																												a__    =  torch.load(__lowerCAmelCase     )
																												scheduler.load_state_dict(__lowerCAmelCase     )
							return lrs







@require_torch
class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :List[Any]    ,__snake_case     :int    ,__snake_case     :Union[str, Any]   )      ->					int:
												self.assertEqual(len(__snake_case   )    ,len(__snake_case   )   )
												for a, b in zip(__snake_case    ,__snake_case   ):
																			self.assertAlmostEqual(__snake_case    ,__snake_case    ,delta=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					str:
												a__    =  torch.tensor([0.1, -0.2, -0.1]    ,requires_grad=__snake_case   )
												a__    =  torch.tensor([0.4, 0.2, -0.5]   )
												a__    =  nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												a__    =  AdamW(params=[w]    ,lr=2E-1    ,weight_decay=0.0   )
												for _ in range(1_00   ):
																			a__    =  criterion(__snake_case    ,__snake_case   )
																			loss.backward()
																			optimizer.step()
																			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																			w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()    ,[0.4, 0.2, -0.5]    ,tol=1E-2   )



					def 				lowerCamelCase__(   self     :Tuple   )      ->					int:
												a__    =  torch.tensor([0.1, -0.2, -0.1]    ,requires_grad=__snake_case   )
												a__    =  torch.tensor([0.4, 0.2, -0.5]   )
												a__    =  nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												a__    =  Adafactor(
												    params=[w]    ,lr=1E-2    ,eps=(1E-30, 1E-3)    ,clip_threshold=1.0    ,decay_rate=-0.8    ,betaa=__snake_case    ,weight_decay=0.0    ,relative_step=__snake_case    ,scale_parameter=__snake_case    ,warmup_init=__snake_case    ,)
												for _ in range(10_00   ):
																			a__    =  criterion(__snake_case    ,__snake_case   )
																			loss.backward()
																			optimizer.step()
																			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																			w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()    ,[0.4, 0.2, -0.5]    ,tol=1E-2   )







@require_torch
class  snake_case_     (unittest.TestCase   ):
					UpperCAmelCase__      :					str					   =    nn.Linear(5_0			,		5_0   ) if is_torch_available() else None
					UpperCAmelCase__      :					Dict					   =    AdamW(m.parameters()			,		lr=1_0.0   ) if is_torch_available() else None
					UpperCAmelCase__      :					Optional[Any]					   =    1_0
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple    ,__snake_case     :int    ,__snake_case     :Any=None   )      ->					Optional[Any]:
												self.assertEqual(len(__snake_case   )    ,len(__snake_case   )   )
												for a, b in zip(__snake_case    ,__snake_case   ):
																			self.assertAlmostEqual(__snake_case    ,__snake_case    ,delta=__snake_case    ,msg=__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple   )      ->					List[Any]:
												a__    =  {'num_warmup_steps': 2, 'num_training_steps': 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												a__    =  {
												    get_constant_schedule: ({}, [10.0] * self.num_steps),
												    get_constant_schedule_with_warmup: (
												        {'num_warmup_steps': 4},
												        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
												    ),
												    get_linear_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
												    ),
												    get_cosine_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
												    ),
												    get_cosine_with_hard_restarts_schedule_with_warmup: (
												        {**common_kwargs, 'num_cycles': 2},
												        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
												    ),
												    get_polynomial_decay_schedule_with_warmup: (
												        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
												        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
												    ),
												    get_inverse_sqrt_schedule: (
												        {'num_warmup_steps': 2},
												        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
												    ),
												}

												for scheduler_func, data in scheds.items():
																			a__   ,      a__    =  data

																			a__    =  scheduler_func(self.optimizer    ,**__snake_case   )
																			self.assertEqual(len([scheduler.get_lr()[0]]   )    ,1   )
																			a__    =  unwrap_schedule(__snake_case    ,self.num_steps   )
																			self.assertListAlmostEqual(
																			    __snake_case    ,__snake_case    ,tol=1E-2    ,msg=F'failed for {scheduler_func} in normal scheduler'    ,)

																			a__    =  scheduler_func(self.optimizer    ,**__snake_case   )
																			if scheduler_func.__name__ != "get_constant_schedule":
																										LambdaScheduleWrapper.wrap_scheduler(__snake_case   )  # wrap to test picklability of the schedule
																			a__    =  unwrap_and_save_reload_schedule(__snake_case    ,self.num_steps   )
																			self.assertListEqual(__snake_case    ,__snake_case    ,msg=F'failed for {scheduler_func} in save and reload'   )







class  snake_case_     :
					def __init__(   self     :Tuple    ,__snake_case     :str   )      ->					Any:
												a__    =  fn
					def __call__(   self     :List[str]    ,*__snake_case     :Optional[Any]    ,**__snake_case     :Optional[int]   )      ->					Union[str, Any]:
												return self.fn(*__snake_case    ,**__snake_case   )



					@classmethod
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union[str, Any]   )      ->					Dict:
												a__    =  list(map(self    ,scheduler.lr_lambdas   )   )




