






'''simple docstring'''




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 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=10						)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       []
			for _ in range(__magic_name__						):
						lrs.append(scheduler.get_lr()[0]						)
						scheduler.step()
			return lrs

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=10						)->    Any:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       []
			for step in range(__magic_name__						):
						lrs.append(scheduler.get_lr()[0]						)
						scheduler.step()
						if step == num_steps // 2:
									with tempfile.TemporaryDirectory() as tmpdirname:
												snake_case_				:					List[Any]   =       os.path.join(__magic_name__     ,"schedule.bin"						)
												torch.save(scheduler.state_dict()     ,__magic_name__						)

												snake_case_				:					Dict   =       torch.load(__magic_name__						)
												scheduler.load_state_dict(__magic_name__						)
			return lrs







@require_torch
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:str       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:int				)				->  Optional[int]:


										'''simple docstring'''




										self.assertEqual(len(lowerCAmelCase__				)       ,							len(lowerCAmelCase__				)				)
										for a, b in zip(lowerCAmelCase__       ,							lowerCAmelCase__				):
													self.assertAlmostEqual(lowerCAmelCase__       ,							lowerCAmelCase__       ,							delta=lowerCAmelCase__				)

							def 							_A	(		self			:Any				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       torch.tensor([0.1, -0.2, -0.1]       ,							requires_grad=lowerCAmelCase__				)
										snake_case_				:					Optional[int]   =       torch.tensor([0.4, 0.2, -0.5]				)
										snake_case_				:					Any   =       nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										snake_case_				:					str   =       AdamW(params=[w]       ,							lr=2E-1       ,							weight_decay=0.0				)
										for _ in range(100				):
													snake_case_				:					List[str]   =       criterion(lowerCAmelCase__       ,							lowerCAmelCase__				)
													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 							_A	(		self			:Optional[int]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       torch.tensor([0.1, -0.2, -0.1]       ,							requires_grad=lowerCAmelCase__				)
										snake_case_				:					Any   =       torch.tensor([0.4, 0.2, -0.5]				)
										snake_case_				:					str   =       nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										snake_case_				:					int   =       Adafactor(
										    params=[w]       ,							lr=1E-2       ,							eps=(1E-3_0, 1E-3)       ,							clip_threshold=1.0       ,							decay_rate=-0.8       ,							betaa=lowerCAmelCase__       ,							weight_decay=0.0       ,							relative_step=lowerCAmelCase__       ,							scale_parameter=lowerCAmelCase__       ,							warmup_init=lowerCAmelCase__       ,							)
										for _ in range(1_000				):
													snake_case_				:					Union[str, Any]   =       criterion(lowerCAmelCase__       ,							lowerCAmelCase__				)
													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    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	nn.Linear(50						,					50							) if is_torch_available() else None
							a__   		=	AdamW(m.parameters()						,					lr=10.0							) if is_torch_available() else None
							a__   		=	10

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:List[str]=None				)				->  Dict:


										'''simple docstring'''




										self.assertEqual(len(lowerCAmelCase__				)       ,							len(lowerCAmelCase__				)				)
										for a, b in zip(lowerCAmelCase__       ,							lowerCAmelCase__				):
													self.assertAlmostEqual(lowerCAmelCase__       ,							lowerCAmelCase__       ,							delta=lowerCAmelCase__       ,							msg=lowerCAmelCase__				)






							def 							_A	(		self			:str				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					str   =       {"num_warmup_steps": 2, "num_training_steps": 10}
										# schedulers doct format
										# function: (sched_args_dict, expected_learning_rates)
										snake_case_				:					Optional[Any]   =       {
										    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
										    get_constant_schedule_with_warmup: (
										        {"num_warmup_steps": 4},
										        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
										    ),
										    get_linear_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
										    ),
										    get_cosine_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
										    ),
										    get_cosine_with_hard_restarts_schedule_with_warmup: (
										        {**common_kwargs, "num_cycles": 2},
										        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
										    ),
										    get_polynomial_decay_schedule_with_warmup: (
										        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
										        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
										    ),
										    get_inverse_sqrt_schedule: (
										        {"num_warmup_steps": 2},
										        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
										    ),
										}

										for scheduler_func, data in scheds.items():
													snake_case_,	snake_case_				:					Optional[int]   =       data

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

													snake_case_				:					Dict   =       scheduler_func(self.optimizer       ,							**lowerCAmelCase__				)
													if scheduler_func.__name__ != "get_constant_schedule":
																LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__				)  # wrap to test picklability of the schedule
													snake_case_				:					Union[str, Any]   =       unwrap_and_save_reload_schedule(lowerCAmelCase__       ,							self.num_steps				)
													self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__       ,							msg=F'''failed for {scheduler_func} in save and reload'''				)







class    A_	:
							"""simple docstring"""




							def __init__(		self			:str       ,							lowerCAmelCase__			:Union[str, Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Dict   =       fn

							def __call__(		self			:Tuple       ,							*lowerCAmelCase__			:str       ,							**lowerCAmelCase__			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										return self.fn(*lowerCAmelCase__       ,							**lowerCAmelCase__				)






							@classmethod
							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:int				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Any   =       list(map(self       ,							scheduler.lr_lambdas				)				)












'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




