



"""simple docstring"""





import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')




class       lowercase__      :
							'''simple docstring'''







							def __init__(	self						,							snake_case						,							snake_case						,							snake_case = True						,							snake_case = False    )      ->				List[str]:
									_UpperCAmelCase							=		scheduler
									_UpperCAmelCase							=		optimizers if isinstance(_SCREAMING_SNAKE_CASE						,							(list, tuple)    ) else [optimizers]
									_UpperCAmelCase							=		split_batches
									_UpperCAmelCase							=		step_with_optimizer
									_UpperCAmelCase							=		GradientState()






							def 							lowerCamelCase_ (	self						,							*snake_case						,							**snake_case    )      ->				Optional[int]:


									if not self.step_with_optimizer:
											# No link between scheduler and optimizer -> just step
											self.scheduler.step(*_SCREAMING_SNAKE_CASE						,							**_SCREAMING_SNAKE_CASE    )
											return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
											if self.gradient_state.adjust_scheduler:
													self.scheduler._step_count += 1
											return

									for opt in self.optimizers:
											if opt.step_was_skipped:
													return
									if self.split_batches:
											# Split batches -> the training dataloader batch size is not changed so one step per training step
											self.scheduler.step(*_SCREAMING_SNAKE_CASE						,							**_SCREAMING_SNAKE_CASE    )
									else:
											# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
											# num_processes steps per training step
											_UpperCAmelCase							=		AcceleratorState().num_processes
											for _ in range(_SCREAMING_SNAKE_CASE    ):
													# Special case when using OneCycle and `drop_last` was not used
													if hasattr(self.scheduler						,							'total_steps'    ):
															if self.scheduler._step_count <= self.scheduler.total_steps:
																	self.scheduler.step(*_SCREAMING_SNAKE_CASE						,							**_SCREAMING_SNAKE_CASE    )
													else:
															self.scheduler.step(*_SCREAMING_SNAKE_CASE						,							**_SCREAMING_SNAKE_CASE    )






							def 							lowerCamelCase_ (	self    )      ->				Optional[int]:
									return self.scheduler.get_last_lr()






							def 							lowerCamelCase_ (	self    )      ->				Any:
									return self.scheduler.state_dict()






							def 							lowerCamelCase_ (	self						,							snake_case    )      ->				int:
									self.scheduler.load_state_dict(_SCREAMING_SNAKE_CASE    )






							def 							lowerCamelCase_ (	self    )      ->				List[str]:
									return self.scheduler.get_lr()






							def 							lowerCamelCase_ (	self						,							*snake_case						,							**snake_case    )      ->				str:
									return self.scheduler.print_lr(*_SCREAMING_SNAKE_CASE						,							**_SCREAMING_SNAKE_CASE    )







"""simple docstring"""

import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=7     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=3     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=18     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=30     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=400     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       _SCREAMING_SNAKE_CASE				:       str=None     ,       _SCREAMING_SNAKE_CASE				:       Any=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=None     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       _SCREAMING_SNAKE_CASE				:       List[str]=[0.5, 0.5, 0.5]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=[0.5, 0.5, 0.5]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=False     ,       )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     size if size is not None else {'height': 20, 'width': 20}
					SCREAMING_SNAKE_CASE						:							int							=     crop_size if crop_size is not None else {'height': 18, 'width': 18}
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     parent
					SCREAMING_SNAKE_CASE						:							Dict							=     batch_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_channels
					SCREAMING_SNAKE_CASE						:							Tuple							=     image_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     min_resolution
					SCREAMING_SNAKE_CASE						:							List[str]							=     max_resolution
					SCREAMING_SNAKE_CASE						:							Tuple							=     do_resize
					SCREAMING_SNAKE_CASE						:							str							=     size
					SCREAMING_SNAKE_CASE						:							Dict							=     do_center_crop
					SCREAMING_SNAKE_CASE						:							List[Any]							=     crop_size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     do_normalize
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     image_mean
					SCREAMING_SNAKE_CASE						:							Dict							=     image_std
					SCREAMING_SNAKE_CASE						:							Any							=     do_reduce_labels







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	str:





					"""simple docstring"""


					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_reduce_labels": self.do_reduce_labels,
					}




def  __snake_case							(	)     ->       List[str]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Dict							=     load_dataset('hf-internal-testing/fixtures_ade20k'							,		split='test'     )

				SCREAMING_SNAKE_CASE						:							Any							=     Image.open(dataset[0]['file']     )
				SCREAMING_SNAKE_CASE						:							int							=     Image.open(dataset[1]['file']     )

				return image, map
def  __snake_case							(	)     ->       Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							int							=     load_dataset('hf-internal-testing/fixtures_ade20k'							,		split='test'     )

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     Image.open(ds[0]['file']     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     Image.open(ds[1]['file']     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     Image.open(ds[2]['file']     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     Image.open(ds[3]['file']     )

				return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class     lowerCAmelCase__						(	_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= BeitImageProcessor if is_vision_available() else None





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     BeitImageProcessingTester(self    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	int:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def 			_lowerCAmelCase     (      self				:       int    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.image_processing_class(**self.image_processor_dict    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'do_resize'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'size'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'do_center_crop'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'center_crop'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'do_normalize'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'image_mean'    )    )
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'image_std'    )    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.image_processing_class.from_dict(self.image_processor_dict    )
					self.assertEqual(image_processor.size     ,       {'height': 20, 'width': 20}    )
					self.assertEqual(image_processor.crop_size     ,       {'height': 18, 'width': 18}    )
					self.assertEqual(image_processor.do_reduce_labels     ,       _SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.image_processing_class.from_dict(
					    self.image_processor_dict     ,       size=42     ,       crop_size=84     ,       reduce_labels=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(image_processor.size     ,       {'height': 42, 'width': 42}    )
					self.assertEqual(image_processor.crop_size     ,       {'height': 84, 'width': 84}    )
					self.assertEqual(image_processor.do_reduce_labels     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.image_processing_class(**self.image_processor_dict    )
					# create random PIL images
					SCREAMING_SNAKE_CASE						:							List[str]							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE    )
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       Image.Image    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							Any							=     image_processing(image_inputs[0]     ,       return_tensors='pt'    ).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )

					# Test batched
					SCREAMING_SNAKE_CASE						:							str							=     image_processing(_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    ).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.image_processing_class(**self.image_processor_dict    )
					# create random numpy tensors
					SCREAMING_SNAKE_CASE						:							Dict							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE     ,       numpify=_SCREAMING_SNAKE_CASE    )
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       np.ndarray    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							Dict							=     image_processing(image_inputs[0]     ,       return_tensors='pt'    ).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )

					# Test batched
					SCREAMING_SNAKE_CASE						:							str							=     image_processing(_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    ).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.image_processing_class(**self.image_processor_dict    )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE     ,       torchify=_SCREAMING_SNAKE_CASE    )
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       torch.Tensor    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							List[str]							=     image_processing(image_inputs[0]     ,       return_tensors='pt'    ).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )

					# Test batched
					SCREAMING_SNAKE_CASE						:							str							=     image_processing(_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    ).pixel_values
					self.assertEqual(
					    encoded_images.shape     ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )





	def 			_lowerCAmelCase     (      self				:       int    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     self.image_processing_class(**self.image_processor_dict    )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE						:							str							=     prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=_SCREAMING_SNAKE_CASE     ,       torchify=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     []
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       torch.Tensor    )
									maps.append(torch.zeros(image.shape[-2:]    ).long()    )

					# Test not batched input
					SCREAMING_SNAKE_CASE						:							int							=     image_processing(image_inputs[0]     ,       maps[0]     ,       return_tensors='pt'    )
					self.assertEqual(
					    encoding['pixel_values'].shape     ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        1,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(encoding['labels'].dtype     ,       torch.long    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 255    )

					# Test batched
					SCREAMING_SNAKE_CASE						:							List[str]							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertEqual(
					    encoding['pixel_values'].shape     ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(encoding['labels'].dtype     ,       torch.long    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 255    )

					# Test not batched input (PIL images)
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							str							=     prepare_semantic_single_inputs()

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertEqual(
					    encoding['pixel_values'].shape     ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        1,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(encoding['labels'].dtype     ,       torch.long    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 255    )

					# Test batched input (PIL images)
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							int							=     prepare_semantic_batch_inputs()

					SCREAMING_SNAKE_CASE						:							Any							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertEqual(
					    encoding['pixel_values'].shape     ,       (
					        2,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(
					    encoding['labels'].shape     ,       (
					        2,
					        self.image_processor_tester.crop_size['height'],
					        self.image_processor_tester.crop_size['width'],
					    )     ,       )
					self.assertEqual(encoding['labels'].dtype     ,       torch.long    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 255    )







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.image_processing_class(**self.image_processor_dict    )

					# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Any							=     prepare_semantic_single_inputs()
					SCREAMING_SNAKE_CASE						:							int							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 150    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     True
					SCREAMING_SNAKE_CASE						:							Any							=     image_processing(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					self.assertTrue(encoding['labels'].min().item() >= 0    )
					self.assertTrue(encoding['labels'].max().item() <= 255    )





