






"""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 		lowercase       (							__snake_case							:					int						,   __snake_case							:					Any=1_0      ):
		lowercase_					:					List[Any]							=  []
		for _ in range(UpperCAmelCase_      ):
				lrs.append(scheduler.get_lr()[0]      )
				scheduler.step()
		return lrs



def 		lowercase       (							__snake_case							:					Optional[Any]						,   __snake_case							:					int=1_0      ):
		lowercase_					:					Dict							=  []
		for step in range(UpperCAmelCase_      ):
				lrs.append(scheduler.get_lr()[0]      )
				scheduler.step()
				if step == num_steps // 2:
						with tempfile.TemporaryDirectory() as tmpdirname:
								lowercase_					:					Optional[int]							=  os.path.join(UpperCAmelCase_						,   '''schedule.bin'''      )
								torch.save(scheduler.state_dict()						,   UpperCAmelCase_      )

								lowercase_					:					Any							=  torch.load(UpperCAmelCase_      )
								scheduler.load_state_dict(UpperCAmelCase_      )
		return lrs





@require_torch
class    _UpperCAmelCase			(					unittest.TestCase				):




					def     A    (							self	:		List[str]       ,		A	:		Any       ,		A	:		Any       ,		A	:		Tuple  )    ->    Optional[int]:
							self.assertEqual(len(UpperCamelCase_  )       ,		len(UpperCamelCase_  )  )
							for a, b in zip(UpperCamelCase_       ,		UpperCamelCase_  ):
									self.assertAlmostEqual(UpperCamelCase_       ,		UpperCamelCase_       ,		delta=UpperCamelCase_  )




					def     A    (							self	:		Any  )    ->    Tuple:
							lowercase_					:					Optional[Any]							=  torch.tensor([0.1, -0.2, -0.1]       ,		requires_grad=UpperCamelCase_  )
							lowercase_					:					List[str]							=  torch.tensor([0.4, 0.2, -0.5]  )
							lowercase_					:					int							=  nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							lowercase_					:					List[str]							=  AdamW(params=[w]       ,		lr=2e-1       ,		weight_decay=0.0  )
							for _ in range(1_00  ):
									lowercase_					:					Optional[int]							=  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     A    (							self	:		int  )    ->    Optional[int]:
							lowercase_					:					Dict							=  torch.tensor([0.1, -0.2, -0.1]       ,		requires_grad=UpperCamelCase_  )
							lowercase_					:					int							=  torch.tensor([0.4, 0.2, -0.5]  )
							lowercase_					:					List[str]							=  nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							lowercase_					:					Optional[Any]							=  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(10_00  ):
									lowercase_					:					Optional[Any]							=  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				):
					SCREAMING_SNAKE_CASE_						:							Tuple				       =  nn.Linear(50  ,		50				) if is_torch_available() else None
					SCREAMING_SNAKE_CASE_						:							List[str]				       =  AdamW(m.parameters()  ,		lr=10.0				) if is_torch_available() else None
					SCREAMING_SNAKE_CASE_						:							Optional[int]				       =  10




					def     A    (							self	:		Any       ,		A	:		List[Any]       ,		A	:		int       ,		A	:		Any       ,		A	:		int=None  )    ->    Union[str, Any]:
							self.assertEqual(len(UpperCamelCase_  )       ,		len(UpperCamelCase_  )  )
							for a, b in zip(UpperCamelCase_       ,		UpperCamelCase_  ):
									self.assertAlmostEqual(UpperCamelCase_       ,		UpperCamelCase_       ,		delta=UpperCamelCase_       ,		msg=UpperCamelCase_  )






					def     A    (							self	:		Any  )    ->    str:
							lowercase_					:					Optional[Any]							=  {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							lowercase_					:					Union[str, Any]							=  {
							    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, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
							    ),
							    get_inverse_sqrt_schedule: (
							        {'''num_warmup_steps''': 2},
							        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
							    ),
							}

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

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

									lowercase_					:					Tuple							=  scheduler_func(self.optimizer       ,		**UpperCamelCase_  )
									if scheduler_func.__name__ != "get_constant_schedule":
											LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase_  )  # wrap to test picklability of the schedule
									lowercase_					:					Any							=  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			:




					def __init__(							self	:		Union[str, Any]       ,		A	:		List[Any]  )    ->    List[str]:
							lowercase_					:					Tuple							=  fn




					def __call__(							self	:		Union[str, Any]       ,		*A	:		str       ,		**A	:		Tuple  )    ->    Optional[Any]:
							return self.fn(*UpperCamelCase_       ,		**UpperCamelCase_  )






					@classmethod
					def     A    (							self	:		str       ,		A	:		Tuple  )    ->    Tuple:
							lowercase_					:					List[str]							=  list(map(self       ,		scheduler.lr_lambdas  )  )










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



