


"""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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=True     ):




					'''simple docstring'''


					model.train()
					__SCREAMING_SNAKE_CASE		     =       model(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       F.mse_loss(lowerCAmelCase_						,		target.to(output.device     )     )
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


					set_seed(42     )
					__SCREAMING_SNAKE_CASE		     =       RegressionModel()
					__SCREAMING_SNAKE_CASE		     =       deepcopy(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       RegressionDataset(length=80     )
					__SCREAMING_SNAKE_CASE		     =       DataLoader(lowerCAmelCase_						,		batch_size=16     )
					model.to(accelerator.device     )
					if sched:
										__SCREAMING_SNAKE_CASE		     =       AdamW(params=model.parameters()						,		lr=1E-3     )
										__SCREAMING_SNAKE_CASE		     =       AdamW(params=ddp_model.parameters()						,		lr=1E-3     )
										__SCREAMING_SNAKE_CASE		     =       LambdaLR(lowerCAmelCase_						,		lr_lambda=lambda lowerCAmelCase_     : epoch**0.65     )
										__SCREAMING_SNAKE_CASE		     =       LambdaLR(lowerCAmelCase_						,		lr_lambda=lambda lowerCAmelCase_     : epoch**0.65     )
					# Make a copy of `model`
					if sched:
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       accelerator.prepare(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       accelerator.prepare(lowerCAmelCase_						,		lowerCAmelCase_     )
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										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     )
										__SCREAMING_SNAKE_CASE		     =       ddp_input[torch.randperm(len(lowerCAmelCase_     )     )]




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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

										# 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     )
										__SCREAMING_SNAKE_CASE		     =       ddp_input[torch.randperm(len(lowerCAmelCase_     )     )]




def      UpperCAmelCase__ (lowerCAmelCase_=False						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


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

										# 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(lowerCAmelCase_     ) - 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     )
										__SCREAMING_SNAKE_CASE		     =       ddp_input[torch.randperm(len(lowerCAmelCase_     )     )]
					GradientState._reset_state()




def      UpperCAmelCase__ (lowerCAmelCase_=False						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


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

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase_     )):
															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(lowerCAmelCase_     ):
															step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
															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"""
										__SCREAMING_SNAKE_CASE		     =       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCAmelCase_     ))
										if accelerator.num_processes > 1:
															check_model_parameters(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration     )
					GradientState._reset_state()




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Accelerator()

					__SCREAMING_SNAKE_CASE		     =       RegressionDataset(length=80     )
					__SCREAMING_SNAKE_CASE		     =       DataLoader(lowerCAmelCase_						,		batch_size=16     )
					__SCREAMING_SNAKE_CASE		     =       RegressionDataset(length=96     )
					__SCREAMING_SNAKE_CASE		     =       DataLoader(lowerCAmelCase_						,		batch_size=16     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       accelerator.prepare(lowerCAmelCase_						,		lowerCAmelCase_     )
					assert accelerator.gradient_state.active_dataloader is None
					for iteration, _ in enumerate(lowerCAmelCase_     ):
										assert id(accelerator.gradient_state.active_dataloader     ) == id(lowerCAmelCase_     )
										if iteration < len(lowerCAmelCase_     ) - 1:
															assert not accelerator.gradient_state.end_of_dataloader
															if iteration == 1:
																				for batch_num, _ in enumerate(lowerCAmelCase_     ):
																									assert id(accelerator.gradient_state.active_dataloader     ) == id(lowerCAmelCase_     )
																									if batch_num < len(lowerCAmelCase_     ) - 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      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Accelerator()
					__SCREAMING_SNAKE_CASE		     =       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(lowerCAmelCase_     )
					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(lowerCAmelCase_     )
					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(lowerCAmelCase_						,		lowerCAmelCase_     )

    # 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(lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''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


a__       :							Dict        =  logging.get_logger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					def constraint_to_multiple_of(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=0						,		lowerCAmelCase_=None     ):
										__SCREAMING_SNAKE_CASE		     =       round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															__SCREAMING_SNAKE_CASE		     =       math.floor(val / multiple     ) * multiple

										if x < min_val:
															__SCREAMING_SNAKE_CASE		     =       math.ceil(val / multiple     ) * multiple

										return x

					__SCREAMING_SNAKE_CASE		     =       (output_size, output_size) if isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) else output_size

					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_image_size(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       output_size

					# determine new height and width
					__SCREAMING_SNAKE_CASE		     =       output_height / input_height
					__SCREAMING_SNAKE_CASE		     =       output_width / input_width

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

					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_height * input_height						,		multiple=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_width * input_width						,		multiple=lowerCAmelCase_     )

					return (new_height, new_width)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    ["pixel_values"]




	def __init__(				self    :       int    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BILINEAR    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[int, float] = 1 / 2_5_5    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			None:
						super().__init__(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"height": 3_8_4, "width": 3_8_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Dict    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       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()}"""     )
						__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[int, float]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			Any:
						return rescale(UpperCAmelCase__    ,    scale=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			np.ndarray:
						return normalize(UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       ImageInput    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       PILImageResampling = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[str, TensorType]] = None    ,    UpperCAmelCase__    :       ChannelDimension = ChannelDimension.FIRST    ,    **UpperCAmelCase__    :       int    ,    ) ->			PIL.Image.Image:
						__SCREAMING_SNAKE_CASE		     =       do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		     =       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.
						__SCREAMING_SNAKE_CASE		     =       [to_numpy_array(UpperCAmelCase__     ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE		     =       [self.resize(image=UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__     ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE		     =       [self.rescale(image=UpperCAmelCase__    ,    scale=UpperCAmelCase__     ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE		     =       [self.normalize(image=UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       [to_channel_dimension_format(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__    ,    tensor_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       List[Tuple] = None     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       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__     ):
																__SCREAMING_SNAKE_CASE		     =       target_sizes.numpy()

											__SCREAMING_SNAKE_CASE		     =       []

											for idx in range(len(UpperCAmelCase__     )     ):
																__SCREAMING_SNAKE_CASE		     =       torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0     )    ,    size=target_sizes[idx]    ,    mode="bilinear"    ,    align_corners=UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       resized_logits[0].argmax(dim=0     )
																semantic_segmentation.append(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       logits.argmax(dim=1     )
											__SCREAMING_SNAKE_CASE		     =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

						return semantic_segmentation
