

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    (  UpperCAmelCase__		,					UpperCAmelCase__=10      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					for _ in range(UpperCAmelCase__      ):
										lrs.append(scheduler.get_lr()[0]      )
										scheduler.step()
					return lrs


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=10      ):

					"""simple docstring"""






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

																				__lowerCAmelCase					=							torch.load(UpperCAmelCase__      )
																				scheduler.load_state_dict(UpperCAmelCase__      )
					return lrs





@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





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




							def        A__  ( self							):
												__lowerCAmelCase					=							torch.tensor([0.1, -0.2, -0.1]					,						requires_grad=_A							)
												__lowerCAmelCase					=							torch.tensor([0.4, 0.2, -0.5]							)
												__lowerCAmelCase					=							nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												__lowerCAmelCase					=							AdamW(params=[w]					,						lr=2e-1					,						weight_decay=0.0							)
												for _ in range(1_0_0							):
																	__lowerCAmelCase					=							criterion(_A					,						_A							)
																	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							):
												__lowerCAmelCase					=							torch.tensor([0.1, -0.2, -0.1]					,						requires_grad=_A							)
												__lowerCAmelCase					=							torch.tensor([0.4, 0.2, -0.5]							)
												__lowerCAmelCase					=							nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												__lowerCAmelCase					=							Adafactor(
												    params=[w]					,						lr=1e-2					,						eps=(1e-3_0, 1e-3)					,						clip_threshold=1.0					,						decay_rate=-0.8					,						betaa=_A					,						weight_decay=0.0					,						relative_step=_A					,						scale_parameter=_A					,						warmup_init=_A					,						)
												for _ in range(1_0_0_0							):
																	__lowerCAmelCase					=							criterion(_A					,						_A							)
																	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     snake_case_			(				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        A__  ( self					,						_A					,						_A					,						_A					,						_A=None							):
												self.assertEqual(len(_A							)					,						len(_A							)							)
												for a, b in zip(_A					,						_A							):
																	self.assertAlmostEqual(_A					,						_A					,						delta=_A					,						msg=_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							{'num_warmup_steps': 2, 'num_training_steps': 1_0}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												__lowerCAmelCase					=							{
												    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():
																	__lowerCAmelCase,	__lowerCAmelCase					=							data

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

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





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A							):
												__lowerCAmelCase					=							fn




							def __call__( self					,						*_A					,						**_A							):
												return self.fn(*_A					,						**_A							)




							@classmethod
							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							list(map(self					,						scheduler.lr_lambdas							)							)









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3					,						_A=2_2_4					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( self							):
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}





@require_torch
@require_vision
class     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =ViTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							EfficientFormerImageProcessorTester(self							)




							@property
							def        A__  ( self							):
												return self.image_proc_tester.prepare_image_processor_dict()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												self.assertTrue(hasattr(_A					,						'image_mean'							)							)
												self.assertTrue(hasattr(_A					,						'image_std'							)							)
												self.assertTrue(hasattr(_A					,						'do_normalize'							)							)
												self.assertTrue(hasattr(_A					,						'do_resize'							)							)
												self.assertTrue(hasattr(_A					,						'size'							)							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						Image.Image							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						numpify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						np.ndarray							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						torchify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						torch.Tensor							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)






