
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 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__=7					,							UpperCamelCase__=3					,							UpperCamelCase__=18					,							UpperCamelCase__=30					,							UpperCamelCase__=400					,							UpperCamelCase__=True					,							UpperCamelCase__=None					,							UpperCamelCase__=2					,							UpperCamelCase__=True					,							UpperCamelCase__=[0.5, 0.5, 0.5]					,							UpperCamelCase__=[0.5, 0.5, 0.5]					,							):
					A__	:						Optional[Any]								=				size if size is not None else {'''shortest_edge''': 30}
					A__	:						str								=				parent
					A__	:						List[str]								=				batch_size
					A__	:						int								=				num_channels
					A__	:						Optional[int]								=				image_size
					A__	:						List[str]								=				min_resolution
					A__	:						str								=				max_resolution
					A__	:						str								=				do_resize
					A__	:						int								=				size
					A__	:						int								=				size_divisor
					A__	:						Any								=				do_normalize
					A__	:						int								=				image_mean
					A__	:						List[Any]								=				image_std





	def 	__snake_case					(      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,
					    "size_divisor": self.size_divisor,
					}





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__=False		):
					if not batched:
									A__	:						Dict								=				self.size['''shortest_edge''']
									A__	:						Optional[Any]								=				image_inputs[0]
									if isinstance(UpperCamelCase__					,							Image.Image		):
													A__							,				A__	:						Any								=				image.size
									else:
													A__							,				A__	:						Optional[int]								=				image.shape[1], image.shape[2]
									A__	:						Optional[int]								=				size / min(UpperCamelCase__					,							UpperCamelCase__		)
									if h < w:
													A__							,				A__	:						List[str]								=				size, scale * w
									else:
													A__							,				A__	:						int								=				scale * h, size

									A__	:						str								=				int((1333 / 800) * size		)
									if max(UpperCamelCase__					,							UpperCamelCase__		) > max_size:
													A__	:						int								=				max_size / max(UpperCamelCase__					,							UpperCamelCase__		)
													A__	:						str								=				newh * scale
													A__	:						Union[str, Any]								=				neww * scale

									A__							,				A__	:						Tuple								=				int(newh + 0.5		), int(neww + 0.5		)
									A__							,				A__	:						List[str]								=				(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

					else:
									A__	:						str								=				[]
									for image in image_inputs:
													A__							,				A__	:						Tuple								=				self.get_expected_values([image]		)
													expected_values.append((expected_height, expected_width)		)
									A__	:						List[str]								=				max(UpperCamelCase__					,							key=lambda UpperCamelCase__		: item[0]		)[0]
									A__	:						Union[str, Any]								=				max(UpperCamelCase__					,							key=lambda UpperCamelCase__		: item[1]		)[1]

					return expected_height, expected_width







@require_torch
@require_vision
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					ViltImageProcessor if is_vision_available() else None





	def 	__snake_case					(      self		):
					A__	:						Optional[Any]								=				ViltImageProcessingTester(self		)





	@property
	def 	__snake_case					(      self		):
					return self.image_processor_tester.prepare_image_processor_dict()





	def 	__snake_case					(      self		):
					A__	:						str								=				self.image_processing_class(**self.image_processor_dict		)
					self.assertTrue(hasattr(UpperCamelCase__					,							'''image_mean'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__					,							'''image_std'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__					,							'''do_normalize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__					,							'''do_resize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__					,							'''size'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__					,							'''size_divisor'''		)		)





	def 	__snake_case					(      self		):
					A__	:						List[str]								=				self.image_processing_class.from_dict(self.image_processor_dict		)
					self.assertEqual(image_processor.size					,							{'''shortest_edge''': 30}		)

					A__	:						Optional[int]								=				self.image_processing_class.from_dict(self.image_processor_dict					,							size=42		)
					self.assertEqual(image_processor.size					,							{'''shortest_edge''': 42}		)





	def 	__snake_case					(      self		):
					pass





	def 	__snake_case					(      self		):
					# Initialize image_processing
					A__	:						List[Any]								=				self.image_processing_class(**self.image_processor_dict		)
					# create random PIL images
					A__	:						Tuple								=				prepare_image_inputs(self.image_processor_tester					,							equal_resolution=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__					,							Image.Image		)

					# Test not batched input
					A__	:						List[Any]								=				image_processing(image_inputs[0]					,							return_tensors='''pt'''		).pixel_values

					A__							,				A__	:						str								=				self.image_processor_tester.get_expected_values(UpperCamelCase__		)
					self.assertEqual(
					    encoded_images.shape					,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,							)

					# Test batched
					A__	:						Tuple								=				image_processing(UpperCamelCase__					,							return_tensors='''pt'''		).pixel_values

					A__							,				A__	:						Tuple								=				self.image_processor_tester.get_expected_values(UpperCamelCase__					,							batched=UpperCamelCase__		)
					self.assertEqual(
					    encoded_images.shape					,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,							)





	def 	__snake_case					(      self		):
					# Initialize image_processing
					A__	:						Union[str, Any]								=				self.image_processing_class(**self.image_processor_dict		)
					# create random numpy tensors
					A__	:						Dict								=				prepare_image_inputs(self.image_processor_tester					,							equal_resolution=UpperCamelCase__					,							numpify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__					,							np.ndarray		)

					# Test not batched input
					A__	:						List[str]								=				image_processing(image_inputs[0]					,							return_tensors='''pt'''		).pixel_values

					A__							,				A__	:						Dict								=				self.image_processor_tester.get_expected_values(UpperCamelCase__		)
					self.assertEqual(
					    encoded_images.shape					,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,							)

					# Test batched
					A__	:						int								=				image_processing(UpperCamelCase__					,							return_tensors='''pt'''		).pixel_values

					A__							,				A__	:						List[str]								=				self.image_processor_tester.get_expected_values(UpperCamelCase__					,							batched=UpperCamelCase__		)
					self.assertEqual(
					    encoded_images.shape					,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,							)





	def 	__snake_case					(      self		):
					# Initialize image_processing
					A__	:						Union[str, Any]								=				self.image_processing_class(**self.image_processor_dict		)
					# create random PyTorch tensors
					A__	:						Union[str, Any]								=				prepare_image_inputs(self.image_processor_tester					,							equal_resolution=UpperCamelCase__					,							torchify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__					,							torch.Tensor		)

					# Test not batched input
					A__	:						Optional[Any]								=				image_processing(image_inputs[0]					,							return_tensors='''pt'''		).pixel_values

					A__							,				A__	:						int								=				self.image_processor_tester.get_expected_values(UpperCamelCase__		)
					self.assertEqual(
					    encoded_images.shape					,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,							)

					# Test batched
					A__	:						Any								=				image_processing(UpperCamelCase__					,							return_tensors='''pt'''		).pixel_values

					A__							,				A__	:						Tuple								=				self.image_processor_tester.get_expected_values(UpperCamelCase__					,							batched=UpperCamelCase__		)
					self.assertEqual(
					    encoded_images.shape					,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )					,							)

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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any=2				,    __UpperCamelCase			:    str=True				,    __UpperCamelCase			:    Optional[Any]=False							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						List[str]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						str								=				torch.nn.Linear(2				,    4							)
				A__	:						Any								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						Tuple								=				torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase				,    max_lr=0.0_1				,    steps_per_epoch=2				,    epochs=1							)
				A__							,				A__							,				A__	:						str								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# 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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]=2				,    __UpperCamelCase			:    Tuple=True				,    __UpperCamelCase			:    List[Any]=False							)			->							str:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						List[str]								=				torch.nn.Linear(2				,    4							)
				A__	:						int								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						List[Any]								=				torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase				,    lr_lambda=lambda __UpperCamelCase							: 1 - n / 10							)
				A__							,				A__							,				A__	:						int								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# Optimizer has stepped
				A__	:						List[Any]								=				False
				scheduler.step()
				A__	:						str								=				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
				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
				A__	:						str								=				True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								A__	:						Dict								=				1 - 2 / 10
				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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int = 2							)			->							List[Any]:

				"""simple docstring"""







				from transformers import get_linear_schedule_with_warmup

				A__	:						str								=				[1, 2, 4]
				for num_steps in steps:
								A__	:						Any								=				GradientAccumulationPlugin(num_steps=__UpperCamelCase				,    adjust_scheduler=num_steps > 1							)
								A__	:						Tuple								=				Accelerator(gradient_accumulation_plugin=__UpperCamelCase							)
								A__	:						Optional[int]								=				torch.nn.Linear(2				,    4							)
								A__	:						Optional[int]								=				torch.optim.AdamW(model.parameters()				,    lr=1_0.0							)
								A__	:						Optional[int]								=				get_linear_schedule_with_warmup(optimizer=__UpperCamelCase				,    num_warmup_steps=0				,    num_training_steps=20							)

								A__							,				A__							,				A__	:						List[Any]								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

								for i in range(10 * num_steps							):
												with accelerator.accumulate(__UpperCamelCase							):
																optimizer.step()
																scheduler.step()

												if i == (10 * 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 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	@require_huggingface_suite
	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)		)
					debug_launcher(UpperCamelCase__		)