


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin






def 	a__ (							A__=2,			A__=True,			A__=False							):
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				Accelerator(step_scheduler_with_optimizer=A__,			split_batches=A__							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.nn.Linear(2,			4							)
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				torch.optim.AdamW(model.parameters(),			lr=1.0							)
			SCREAMING_SNAKE_CASE_		:    Any 		=				torch.optim.lr_scheduler.OneCycleLR(A__,			max_lr=0.01,			steps_per_epoch=2,			epochs=1							)
			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Any 		=				accelerator.prepare(A__,			A__,			A__							)

			# Optimizer has stepped
			scheduler.step()
			if step_scheduler_with_optimizer or (num_processes == 1):
						assert (
						    scheduler.scheduler.last_epoch == num_processes
						), F'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
			else:
						assert (
						    scheduler.scheduler.last_epoch != num_processes
						), F'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''






def 	a__ (							A__=2,			A__=True,			A__=False							):
			SCREAMING_SNAKE_CASE_		:    str 		=				Accelerator(step_scheduler_with_optimizer=A__,			split_batches=A__							)
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				torch.nn.Linear(2,			4							)
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				torch.optim.AdamW(model.parameters(),			lr=1.0							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.optim.lr_scheduler.LambdaLR(A__,			lr_lambda=lambda A__							: 1 - n / 1_0							)
			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				accelerator.prepare(A__,			A__,			A__							)

			# Optimizer has stepped
			SCREAMING_SNAKE_CASE_		:    int 		=				False
			scheduler.step()
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), F'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

			# Optimizer has not stepped
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						SCREAMING_SNAKE_CASE_		:    Any 		=				1 - 2 / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), F'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''






def 	a__ (							A__ = 2							):
			from transformers import get_linear_schedule_with_warmup

			SCREAMING_SNAKE_CASE_		:    Dict 		=				[1, 2, 4]
			for num_steps in steps:
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				GradientAccumulationPlugin(num_steps=A__,			adjust_scheduler=num_steps > 1							)
						SCREAMING_SNAKE_CASE_		:    List[Any] 		=				Accelerator(gradient_accumulation_plugin=A__							)
						SCREAMING_SNAKE_CASE_		:    Tuple 		=				torch.nn.Linear(2,			4							)
						SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				torch.optim.AdamW(model.parameters(),			lr=10.0							)
						SCREAMING_SNAKE_CASE_		:    str 		=				get_linear_schedule_with_warmup(optimizer=A__,			num_warmup_steps=0,			num_training_steps=2_0							)

						SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				accelerator.prepare(A__,			A__,			A__							)

						for i in range(1_0 * num_steps							):
									with accelerator.accumulate(A__							):
												optimizer.step()
												scheduler.step()

									if i == (1_0 * num_steps - 2):
												assert (
												    scheduler.get_last_lr()[0] != 0
												), F'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
						assert (
						    scheduler.get_last_lr()[0] == 0
						), F'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
						GradientState._reset_state()







@require_cpu
class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1					) ,	num_processes=1					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1 ,	split_batches=__SCREAMING_SNAKE_CASE					) ,	num_processes=1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1					) ,	num_processes=1					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1 ,	split_batches=__SCREAMING_SNAKE_CASE					) ,	num_processes=1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1 ,	step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE					) ,	num_processes=1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1 ,	step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE					) ,	num_processes=1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE					)
										debug_launcher(__SCREAMING_SNAKE_CASE					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1 ,	split_batches=__SCREAMING_SNAKE_CASE					) ,	num_processes=1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE					)
										debug_launcher(__SCREAMING_SNAKE_CASE					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1 ,	split_batches=__SCREAMING_SNAKE_CASE					) ,	num_processes=1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE					)					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE					)					)






							@require_huggingface_suite
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE					)
										debug_launcher(partial(__SCREAMING_SNAKE_CASE ,	num_processes=1					)					)
										debug_launcher(__SCREAMING_SNAKE_CASE					)









from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										return True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				'Hugging Face大法好，谁用谁知道。'
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				'Hugging Face大法好,谁用谁知道。<unk>'
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tokenizer.tokenize(lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer.decode(lowerCAmelCase__					)
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)





