


'''simple docstring'''
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def     a   (    __a				,      __a				,      __a				,      __a     )						->			List[Any]:



					'''simple docstring'''






					for param, grad_param in zip(model_a.parameters()				,      model_b.parameters()     ):
										if not param.requires_grad:
															continue
										if not did_step:
															# Grads should not be in sync
															assert (
															    torch.allclose(param.grad				,      grad_param.grad     ) is False
															), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
										else:
															# Grads should be in sync
															assert (
															    torch.allclose(param.grad				,      grad_param.grad     ) is True
															), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''


def     a   (    __a				,      __a				,      __a				,      __a				,      __a=True     )						->			Optional[Any]:



					'''simple docstring'''






					model.train()
					UpperCamelCase__     :Any							      =				model(__a     )
					UpperCamelCase__     :List[Any]							      =				F.mse_loss(__a				,      target.to(output.device     )     )
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(__a     )


def     a   (    __a				,      __a=False     )						->			Optional[Any]:



					'''simple docstring'''






					set_seed(42     )
					UpperCamelCase__     :Tuple							      =				RegressionModel()
					UpperCamelCase__     :str							      =				deepcopy(__a     )
					UpperCamelCase__     :Optional[int]							      =				RegressionDataset(length=80     )
					UpperCamelCase__     :List[str]							      =				DataLoader(__a				,      batch_size=16     )
					model.to(accelerator.device     )
					if sched:
										UpperCamelCase__     :Optional[int]							      =				AdamW(params=model.parameters()				,      lr=1e-3     )
										UpperCamelCase__     :Dict							      =				AdamW(params=ddp_model.parameters()				,      lr=1e-3     )
										UpperCamelCase__     :Union[str, Any]							      =				LambdaLR(__a				,      lr_lambda=lambda __a     : epoch**0.6_5     )
										UpperCamelCase__     :str							      =				LambdaLR(__a				,      lr_lambda=lambda __a     : epoch**0.6_5     )
					# Make a copy of `model`
					if sched:
										UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				accelerator.prepare(__a				,      __a				,      __a				,      __a     )
					else:
										UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				accelerator.prepare(__a				,      __a     )
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader


def     a   (    __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				get_training_setup(__a     )
					# Use a single batch
					UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				next(iter(__a     )     ).values()
					for iteration in range(3     ):
										# Gather the distributed inputs and targs for the base model
										UpperCamelCase__      ,       UpperCamelCase__     :int							      =				accelerator.gather((ddp_input, ddp_target)     )
										UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				input.to(accelerator.device     ), target.to(accelerator.device     )
										# Perform our initial ground truth step in non "DDP"
										step_model(__a				,      __a				,      __a				,      __a     )
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(__a     ):
																				step_model(__a				,      __a				,      __a				,      __a     )
										else:
															# Sync grads
															step_model(__a				,      __a				,      __a				,      __a     )

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(__a				,      __a				,      __a				,      __a     )
										for param, ddp_param in zip(model.parameters()				,      ddp_model.parameters()     ):
															if not param.requires_grad:
																				continue
															assert torch.allclose(
															    param.grad				,      ddp_param.grad     ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration     )
										UpperCamelCase__     :Any							      =				ddp_input[torch.randperm(len(__a     )     )]


def     a   (    __a     )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				get_training_setup(__a     )
					# Use a single batch
					UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				next(iter(__a     )     ).values()
					for iteration in range(3     ):
										# Gather the distributed inputs and targs for the base model
										UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				accelerator.gather((ddp_input, ddp_target)     )
										UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				input.to(accelerator.device     ), target.to(accelerator.device     )
										# Perform our initial ground truth step in non "DDP"
										step_model(__a				,      __a				,      __a				,      __a     )
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(__a     ):
																				step_model(__a				,      __a				,      __a				,      __a     )
										else:
															# Sync grads
															step_model(__a				,      __a				,      __a				,      __a     )

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters()				,      ddp_model.parameters()     ):
															if not param.requires_grad:
																				continue
															if iteration % 2 == 0:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad				,      ddp_param.grad     ) is False
																				), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
															else:
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad				,      ddp_param.grad     ) is True
																				), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration     )
										UpperCamelCase__     :List[Any]							      =				ddp_input[torch.randperm(len(__a     )     )]


def     a   (    __a=False				,      __a=False     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				Accelerator(
					    split_batches=__a				,      dispatch_batches=__a				,      gradient_accumulation_steps=2     )
					# Test that context manager behaves properly
					UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				get_training_setup(__a     )
					for iteration, batch in enumerate(__a     ):
										UpperCamelCase__      ,       UpperCamelCase__     :int							      =				batch.values()
										# Gather the distributed inputs and targs for the base model
										UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				accelerator.gather((ddp_input, ddp_target)     )
										UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				input.to(accelerator.device     ), target.to(accelerator.device     )
										# Perform our initial ground truth step in non "DDP"
										step_model(__a				,      __a				,      __a				,      __a				,      __a     )
										# Do "gradient accumulation" (noop)
										with accelerator.accumulate(__a     ):
															step_model(__a				,      __a				,      __a				,      __a     )

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters()				,      ddp_model.parameters()     ):
															if not param.requires_grad:
																				continue
															if ((iteration + 1) % 2 == 0) or (iteration == len(__a     ) - 1):
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad				,      ddp_param.grad     ) is True
																				), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
															else:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad				,      ddp_param.grad     ) is False
																				), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration     )
										UpperCamelCase__     :Tuple							      =				ddp_input[torch.randperm(len(__a     )     )]
					GradientState._reset_state()


def     a   (    __a=False				,      __a=False     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				Accelerator(
					    split_batches=__a				,      dispatch_batches=__a				,      gradient_accumulation_steps=2     )
					# Test that context manager behaves properly
					UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				get_training_setup(__a				,      __a     )
					for iteration, batch in enumerate(__a     ):
										UpperCamelCase__      ,       UpperCamelCase__     :int							      =				batch.values()
										# Gather the distributed inputs and targs for the base model
										UpperCamelCase__      ,       UpperCamelCase__     :str							      =				accelerator.gather((ddp_input, ddp_target)     )
										UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				input.to(accelerator.device     ), target.to(accelerator.device     )
										# Perform our initial ground truth step in non "DDP"
										model.train()
										ddp_model.train()
										step_model(__a				,      __a				,      __a				,      __a				,      __a     )
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__a     )):
															if split_batches:
																				sched.step()
															else:
																				for _ in range(accelerator.num_processes     ):
																									sched.step()
										opt.zero_grad()
										# Perform gradient accumulation under wrapper
										with accelerator.accumulate(__a     ):
															step_model(__a				,      __a				,      __a				,      __a     )
															ddp_opt.step()
															ddp_sched.step()
															ddp_opt.zero_grad()

										# Learning rates should be the same
										assert (
										    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
										), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
										UpperCamelCase__     :List[str]							      =				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__a     ))
										if accelerator.num_processes > 1:
															check_model_parameters(__a				,      __a				,      __a				,      __a     )
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration     )
					GradientState._reset_state()


def     a   (    )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				Accelerator()

					UpperCamelCase__     :Dict							      =				RegressionDataset(length=80     )
					UpperCamelCase__     :int							      =				DataLoader(__a				,      batch_size=16     )
					UpperCamelCase__     :Union[str, Any]							      =				RegressionDataset(length=96     )
					UpperCamelCase__     :str							      =				DataLoader(__a				,      batch_size=16     )
					UpperCamelCase__      ,       UpperCamelCase__     :int							      =				accelerator.prepare(__a				,      __a     )
					assert accelerator.gradient_state.active_dataloader is None
					for iteration, _ in enumerate(__a     ):
										assert id(accelerator.gradient_state.active_dataloader     ) == id(__a     )
										if iteration < len(__a     ) - 1:
															assert not accelerator.gradient_state.end_of_dataloader
															if iteration == 1:
																				for batch_num, _ in enumerate(__a     ):
																									assert id(accelerator.gradient_state.active_dataloader     ) == id(__a     )
																									if batch_num < len(__a     ) - 1:
																														assert not accelerator.gradient_state.end_of_dataloader
																									else:
																														assert accelerator.gradient_state.end_of_dataloader
										else:
															assert accelerator.gradient_state.end_of_dataloader
					assert accelerator.gradient_state.active_dataloader is None


def     a   (    )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				Accelerator()
					UpperCamelCase__     :str							      =				accelerator.state
					if state.local_process_index == 0:
										print('''**Test `accumulate` gradient accumulation with dataloader break**'''     )
					test_dataloader_break()
					if state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print('''**Test NOOP `no_sync` context manager**'''     )
										test_noop_sync(__a     )
					if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
										if state.local_process_index == 0:
															print('''**Test Distributed `no_sync` context manager**'''     )
										test_distributed_sync(__a     )
					if state.distributed_type == DistributedType.MULTI_GPU:
										for split_batch in [True, False]:
															for dispatch_batches in [True, False]:
																				if state.local_process_index == 0:
																									print(
																									    '''**Test `accumulate` gradient accumulation, '''				,      f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''				,      )
																				test_gradient_accumulation(__a				,      __a     )

    # Currently will break on torch 2.0 +, need to investigate why
					if is_torch_version('''<'''				,      '''2.0'''     ) or state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print(
															    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''				,      '''`split_batches=False`, `dispatch_batches=False`**'''				,      )
										test_gradient_accumulation_with_opt_and_scheduler()
										if state.distributed_type == DistributedType.MULTI_GPU:
															for split_batch in [True, False]:
																				for dispatch_batches in [True, False]:
																									if not split_batch and not dispatch_batches:
																														continue
																									if state.local_process_index == 0:
																														print(
																														    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''				,      f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''				,      )
																									test_gradient_accumulation_with_opt_and_scheduler(__a				,      __a     )


def     a   (    __a     )						->			List[Any]:



					'''simple docstring'''






					main()


if __name__ == "__main__":
		main()



'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a				,      __a				,      __a				,      __a     )						->			Tuple[int, int]:



					'''simple docstring'''






					def constraint_to_multiple_of(__a				,      __a				,      __a=0				,      __a=None     ):
										UpperCamelCase__     :Union[str, Any]							      =				round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															UpperCamelCase__     :List[str]							      =				math.floor(val / multiple     ) * multiple

										if x < min_val:
															UpperCamelCase__     :Union[str, Any]							      =				math.ceil(val / multiple     ) * multiple

										return x

					UpperCamelCase__     :str							      =				(output_size, output_size) if isinstance(__a				,      __a     ) else output_size

					UpperCamelCase__      ,       UpperCamelCase__     :int							      =				get_image_size(__a     )
					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				output_size

					# determine new height and width
					UpperCamelCase__     :List[str]							      =				output_height / input_height
					UpperCamelCase__     :str							      =				output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width     ) < abs(1 - scale_height     ):
															# fit width
															UpperCamelCase__     :str							      =				scale_width
										else:
															# fit height
															UpperCamelCase__     :int							      =				scale_height

					UpperCamelCase__     :Optional[int]							      =				constraint_to_multiple_of(scale_height * input_height				,      multiple=__a     )
					UpperCamelCase__     :List[Any]							      =				constraint_to_multiple_of(scale_width * input_width				,      multiple=__a     )

					return (new_height, new_width)







class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['pixel_values']




		def __init__(	self					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = PILImageResampling.BILINEAR					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = True					,     UpperCamelCase_ = 1 / 255					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				size if size is not None else {'''height''': 384, '''width''': 384}
							UpperCamelCase__     :Dict							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				do_resize
							UpperCamelCase__     :Union[str, Any]							      =				size
							UpperCamelCase__     :List[Any]							      =				keep_aspect_ratio
							UpperCamelCase__     :Optional[int]							      =				ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample
							UpperCamelCase__     :str							      =				do_rescale
							UpperCamelCase__     :Union[str, Any]							      =				rescale_factor
							UpperCamelCase__     :List[str]							      =				do_normalize
							UpperCamelCase__     :List[str]							      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__     :Dict							      =				image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				get_size_dict(UpperCamelCase_							)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
							UpperCamelCase__     :int							      =				get_resize_output_image_size(
							    UpperCamelCase_					,     output_size=(size['''height'''], size['''width'''])					,     keep_aspect_ratio=UpperCamelCase_					,     multiple=UpperCamelCase_					,     )
							return resize(UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							return rescale(UpperCamelCase_					,     scale=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							return normalize(UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_					,     data_format=UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = ChannelDimension.FIRST					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__     :Any							      =				size if size is not None else self.size
							UpperCamelCase__     :List[str]							      =				get_size_dict(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							UpperCamelCase__     :str							      =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							UpperCamelCase__     :Union[str, Any]							      =				resample if resample is not None else self.resample
							UpperCamelCase__     :str							      =				do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__     :int							      =				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__     :Optional[Any]							      =				do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__     :Optional[int]							      =				image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__     :List[str]							      =				image_std if image_std is not None else self.image_std

							UpperCamelCase__     :List[str]							      =				make_list_of_images(UpperCamelCase_							)

							if not valid_images(UpperCamelCase_							):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

							if do_resize and size is None or resample is None:
												raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

							if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

							# All transformations expect numpy arrays.
							UpperCamelCase__     :str							      =				[to_numpy_array(UpperCamelCase_							) for image in images]

							if do_resize:
												UpperCamelCase__     :str							      =				[self.resize(image=UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_							) for image in images]

							if do_rescale:
												UpperCamelCase__     :int							      =				[self.rescale(image=UpperCamelCase_					,     scale=UpperCamelCase_							) for image in images]

							if do_normalize:
												UpperCamelCase__     :str							      =				[self.normalize(image=UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_							) for image in images]

							UpperCamelCase__     :Optional[int]							      =				[to_channel_dimension_format(UpperCamelCase_					,     UpperCamelCase_							) for image in images]

							UpperCamelCase__     :List[str]							      =				{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_					,     tensor_type=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
												if len(UpperCamelCase_							) != len(UpperCamelCase_							):
																	raise ValueError(
																	    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''							)

												if is_torch_tensor(UpperCamelCase_							):
																	UpperCamelCase__     :Union[str, Any]							      =				target_sizes.numpy()

												UpperCamelCase__     :int							      =				[]

												for idx in range(len(UpperCamelCase_							)							):
																	UpperCamelCase__     :str							      =				torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0							)					,     size=target_sizes[idx]					,     mode='''bilinear'''					,     align_corners=UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				resized_logits[0].argmax(dim=0							)
																	semantic_segmentation.append(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Any							      =				logits.argmax(dim=1							)
												UpperCamelCase__     :Optional[Any]							      =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

							return semantic_segmentation