






'''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		)     ->				List[Any]:
					_lowercase			= []
					for _ in range(lowercase__		):
										lrs.append(scheduler.get_lr()[0]		)
										scheduler.step()
					return lrs



def     A__						(	A_    ,      A_=10		)     ->				List[Any]:
					_lowercase			= []
					for step in range(lowercase__		):
										lrs.append(scheduler.get_lr()[0]		)
										scheduler.step()
										if step == num_steps // 2:
															with tempfile.TemporaryDirectory() as tmpdirname:
																				_lowercase			= os.path.join(lowercase__    ,      "schedule.bin"		)
																				torch.save(scheduler.state_dict()    ,      lowercase__		)

																				_lowercase			= torch.load(lowercase__		)
																				scheduler.load_state_dict(lowercase__		)
					return lrs







@require_torch
class 	UpperCamelCase__					(     unittest.TestCase			):

		"""simple docstring"""




		def       snake_case     ( self						:						List[Any]							,    __A						:						Tuple							,    __A						:						int							,    __A						:						Optional[int]    ):







							"""simple docstring"""
							self.assertEqual(len(__UpperCamelCase    )							,    len(__UpperCamelCase    )    )
							for a, b in zip(__UpperCamelCase							,    __UpperCamelCase    ):
												self.assertAlmostEqual(__UpperCamelCase							,    __UpperCamelCase							,    delta=__UpperCamelCase    )




		def       snake_case     ( self						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= torch.tensor([0.1, -0.2, -0.1]							,    requires_grad=__UpperCamelCase    )
							_lowercase			= torch.tensor([0.4, 0.2, -0.5]    )
							_lowercase			= nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							_lowercase			= AdamW(params=[w]							,    lr=2e-1							,    weight_decay=0.0    )
							for _ in range(1_0_0    ):
												_lowercase			= criterion(__UpperCamelCase							,    __UpperCamelCase    )
												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       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= torch.tensor([0.1, -0.2, -0.1]							,    requires_grad=__UpperCamelCase    )
							_lowercase			= torch.tensor([0.4, 0.2, -0.5]    )
							_lowercase			= nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							_lowercase			= Adafactor(
							    params=[w]							,    lr=1e-2							,    eps=(1e-30, 1e-3)							,    clip_threshold=1.0							,    decay_rate=-0.8							,    betaa=__UpperCamelCase							,    weight_decay=0.0							,    relative_step=__UpperCamelCase							,    scale_parameter=__UpperCamelCase							,    warmup_init=__UpperCamelCase							,    )
							for _ in range(1_0_0_0    ):
												_lowercase			= criterion(__UpperCamelCase							,    __UpperCamelCase    )
												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 	UpperCamelCase__					(     unittest.TestCase			):

		"""simple docstring"""
		UpperCAmelCase__     			=						nn.Linear(50      ,     50			) if is_torch_available() else None
		UpperCAmelCase__     			=						AdamW(m.parameters()      ,     lr=10.0			) if is_torch_available() else None
		UpperCAmelCase__     			=						10




		def       snake_case     ( self						:						Dict							,    __A						:						str							,    __A						:						List[Any]							,    __A						:						Dict							,    __A						:						List[str]=None    ):







							"""simple docstring"""
							self.assertEqual(len(__UpperCamelCase    )							,    len(__UpperCamelCase    )    )
							for a, b in zip(__UpperCamelCase							,    __UpperCamelCase    ):
												self.assertAlmostEqual(__UpperCamelCase							,    __UpperCamelCase							,    delta=__UpperCamelCase							,    msg=__UpperCamelCase    )




		def       snake_case     ( self						:						Dict    ):







							"""simple docstring"""
							_lowercase			= {"num_warmup_steps": 2, "num_training_steps": 1_0}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							_lowercase			= {
							    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():
												_lowercase	,			_lowercase			= data

												_lowercase			= scheduler_func(self.optimizer							,    **__UpperCamelCase    )
												self.assertEqual(len([scheduler.get_lr()[0]]    )							,    1    )
												_lowercase			= unwrap_schedule(__UpperCamelCase							,    self.num_steps    )
												self.assertListAlmostEqual(
												    __UpperCamelCase							,    __UpperCamelCase							,    tol=1e-2							,    msg=f"""failed for {scheduler_func} in normal scheduler"""							,    )

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







class 	UpperCamelCase__					:

		"""simple docstring"""




		def __init__( self						:						List[str]							,    __A						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= fn




		def __call__( self						:						Union[str, Any]							,    *__A						:						Union[str, Any]							,    **__A						:						Optional[Any]    ):







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




		@classmethod
		def       snake_case     ( self						:						Dict							,    __A						:						Dict    ):







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




# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
