




'''simple docstring'''





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def   UpperCamelCase_(			snake_case							: Dict=2							,       snake_case							: List[str]=True							,       snake_case							: Optional[Any]=False  ):

							'''simple docstring'''






							snake_case_			      =   Accelerator(step_scheduler_with_optimizer=snake_case							,       split_batches=snake_case  )
							snake_case_			      =   torch.nn.Linear(2							,       4  )
							snake_case_			      =   torch.optim.AdamW(model.parameters()							,       lr=1.0  )
							snake_case_			      =   torch.optim.lr_scheduler.OneCycleLR(snake_case							,       max_lr=0.01							,       steps_per_epoch=2							,       epochs=1  )
							snake_case_      ,   snake_case_      ,   snake_case_			      =   accelerator.prepare(snake_case							,       snake_case							,       snake_case  )

							# Optimizer has stepped
							scheduler.step()
							if step_scheduler_with_optimizer or (num_processes == 1):
														assert (
														    scheduler.scheduler.last_epoch == num_processes
														), f'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
							else:
														assert (
														    scheduler.scheduler.last_epoch != num_processes
														), f'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'


def   UpperCamelCase_(			snake_case							: Any=2							,       snake_case							: List[Any]=True							,       snake_case							: str=False  ):

							'''simple docstring'''






							snake_case_			      =   Accelerator(step_scheduler_with_optimizer=snake_case							,       split_batches=snake_case  )
							snake_case_			      =   torch.nn.Linear(2							,       4  )
							snake_case_			      =   torch.optim.AdamW(model.parameters()							,       lr=1.0  )
							snake_case_			      =   torch.optim.lr_scheduler.LambdaLR(snake_case							,       lr_lambda=lambda snake_case  : 1 - n / 1_0  )
							snake_case_      ,   snake_case_      ,   snake_case_			      =   accelerator.prepare(snake_case							,       snake_case							,       snake_case  )

							# Optimizer has stepped
							snake_case_			      =   False
							scheduler.step()
							snake_case_			      =   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
							assert (
							    scheduler.get_last_lr()[0] == expected_lr
							), f'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

							# Optimizer has not stepped
							snake_case_			      =   True
							scheduler.step()
							if not step_scheduler_with_optimizer:
														snake_case_			      =   1 - 2 / 1_0
							assert (
							    scheduler.get_last_lr()[0] == expected_lr
							), f'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'


def   UpperCamelCase_(			snake_case							: int = 2  ):

							'''simple docstring'''






							from transformers import get_linear_schedule_with_warmup

							snake_case_			      =   [1, 2, 4]
							for num_steps in steps:
														snake_case_			      =   GradientAccumulationPlugin(num_steps=snake_case							,       adjust_scheduler=num_steps > 1  )
														snake_case_			      =   Accelerator(gradient_accumulation_plugin=snake_case  )
														snake_case_			      =   torch.nn.Linear(2							,       4  )
														snake_case_			      =   torch.optim.AdamW(model.parameters()							,       lr=10.0  )
														snake_case_			      =   get_linear_schedule_with_warmup(optimizer=snake_case							,       num_warmup_steps=0							,       num_training_steps=2_0  )

														snake_case_      ,   snake_case_      ,   snake_case_			      =   accelerator.prepare(snake_case							,       snake_case							,       snake_case  )

														for i in range(1_0 * num_steps  ):
																					with accelerator.accumulate(snake_case  ):
																												optimizer.step()
																												scheduler.step()

																					if i == (1_0 * num_steps - 2):
																												assert (
																												    scheduler.get_last_lr()[0] != 0
																												), f'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
														assert (
														    scheduler.get_last_lr()[0] == 0
														), f'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
														GradientState._reset_state()




@require_cpu
class 				_snake_case	(			unittest.TestCase							):




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										debug_launcher(partial(a__       ,							num_processes=1							)       ,							num_processes=1							)
										debug_launcher(partial(a__       ,							num_processes=1       ,							split_batches=a__							)       ,							num_processes=1							)




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										debug_launcher(partial(a__       ,							num_processes=1							)       ,							num_processes=1							)
										debug_launcher(partial(a__       ,							num_processes=1       ,							split_batches=a__							)       ,							num_processes=1							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




										debug_launcher(partial(a__       ,							num_processes=1       ,							step_scheduler_with_optimizer=a__							)       ,							num_processes=1							)




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										debug_launcher(partial(a__       ,							num_processes=1       ,							step_scheduler_with_optimizer=a__							)       ,							num_processes=1							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




										AcceleratorState._reset_state(a__							)
										debug_launcher(a__							)
										debug_launcher(partial(a__       ,							num_processes=1       ,							split_batches=a__							)       ,							num_processes=1							)




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										AcceleratorState._reset_state(a__							)
										debug_launcher(a__							)
										debug_launcher(partial(a__       ,							num_processes=1       ,							split_batches=a__							)       ,							num_processes=1							)




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

										'''simple docstring'''




										AcceleratorState._reset_state(a__							)
										debug_launcher(partial(a__       ,							step_scheduler_with_optimizer=a__							)							)




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

										'''simple docstring'''




										AcceleratorState._reset_state(a__							)
										debug_launcher(partial(a__       ,							step_scheduler_with_optimizer=a__							)							)




			@require_huggingface_suite
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										AcceleratorState._reset_state(a__							)
										debug_launcher(partial(a__       ,							num_processes=1							)							)
										debug_launcher(a__							)










'''simple docstring'''





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 ViltImageProcessor




class 				_snake_case	(			unittest.TestCase							):




			def __init__(      self       ,							a__       ,							a__=7       ,							a__=3       ,							a__=18       ,							a__=30       ,							a__=400       ,							a__=True       ,							a__=None       ,							a__=2       ,							a__=True       ,							a__=[0.5, 0.5, 0.5]       ,							a__=[0.5, 0.5, 0.5]       ,							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   size if size is not None else {"shortest_edge": 30}
										snake_case_			      =   parent
										snake_case_			      =   batch_size
										snake_case_			      =   num_channels
										snake_case_			      =   image_size
										snake_case_			      =   min_resolution
										snake_case_			      =   max_resolution
										snake_case_			      =   do_resize
										snake_case_			      =   size
										snake_case_			      =   size_divisor
										snake_case_			      =   do_normalize
										snake_case_			      =   image_mean
										snake_case_			      =   image_std




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "size_divisor": self.size_divisor,
										}




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=False							)       ->       str:

										'''simple docstring'''




										if not batched:
																	snake_case_			      =   self.size["shortest_edge"]
																	snake_case_			      =   image_inputs[0]
																	if isinstance(a__       ,							Image.Image							):
																								snake_case_      ,   snake_case_			      =   image.size
																	else:
																								snake_case_      ,   snake_case_			      =   image.shape[1], image.shape[2]
																	snake_case_			      =   size / min(a__       ,							a__							)
																	if h < w:
																								snake_case_      ,   snake_case_			      =   size, scale * w
																	else:
																								snake_case_      ,   snake_case_			      =   scale * h, size

																	snake_case_			      =   int((1_333 / 800) * size							)
																	if max(a__       ,							a__							) > max_size:
																								snake_case_			      =   max_size / max(a__       ,							a__							)
																								snake_case_			      =   newh * scale
																								snake_case_			      =   neww * scale

																	snake_case_      ,   snake_case_			      =   int(newh + 0.5							), int(neww + 0.5							)
																	snake_case_      ,   snake_case_			      =   (
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

										else:
																	snake_case_			      =   []
																	for image in image_inputs:
																								snake_case_      ,   snake_case_			      =   self.get_expected_values([image]							)
																								expected_values.append((expected_height, expected_width)							)
																	snake_case_			      =   max(a__       ,							key=lambda a__							: item[0]							)[0]
																	snake_case_			      =   max(a__       ,							key=lambda a__							: item[1]							)[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 				_snake_case	(			lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   Union[str, Any]		 =					ViltImageProcessor if is_vision_available() else None




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   ViltImageProcessingTester(self							)




			@property
			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




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

										'''simple docstring'''




										snake_case_			      =   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"							)							)
										self.assertTrue(hasattr(a__       ,							"size_divisor"							)							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 30}							)

										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict       ,							size=42							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 42}							)




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

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							Image.Image							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__       ,							numpify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							np.ndarray							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__       ,							torchify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							torch.Tensor							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




