
"""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 							A_     (__a				,   __a=10				):

					'''simple docstring'''





					A_				 =						[]
					for _ in range(__a				):
										lrs.append(scheduler.get_lr()[0]				)
										scheduler.step()
					return lrs
def 							A_     (__a				,   __a=10				):

					'''simple docstring'''





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

																				A_				 =						torch.load(__a				)
																				scheduler.load_state_dict(__a				)
					return lrs



@require_torch
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								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						:					List[Any]	)      -> List[Any]:







								"""simple docstring"""
								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(100	):
													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						:					str	)      -> List[str]:







								"""simple docstring"""
								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(1_000	):
													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 __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







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




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Dict       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					List[Any]=None	)      -> Any:







								"""simple docstring"""
								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						:					Dict	)      -> Union[str, Any]:







								"""simple docstring"""
								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, 8.7_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, 10.0, 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, 10.0, 8.5_3, 5.0, 1.4_6, 10.0, 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, 10.0, 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, 10.0, 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():
													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 __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					str       ,   _snake_case						:					Any	)      -> List[str]:







								"""simple docstring"""
								A_				 =						fn




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







								"""simple docstring"""
								return self.fn(*_snake_case       ,   **_snake_case	)






			@classmethod
			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					List[Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						list(map(self       ,   scheduler.lr_lambdas	)	)






"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_					:						str				= {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_					:						Union[str, Any]				= ['''TimmBackbone''']


if TYPE_CHECKING:
		from .configuration_timm_backbone import TimmBackboneConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timm_backbone import TimmBackbone

else:
		import sys

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




