
'''simple docstring'''


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 	__lowercase				:

			def __init__(self			,   A			,   A=sys.maxsize      ):
								lowerCamelCase_					:      Tuple     =		'''bilinear'''
								lowerCamelCase_					:      Optional[int]     =		max_size
								lowerCamelCase_					:      List[str]     =		short_edge_length






			def __call__(self			,   A      ):
								lowerCamelCase_					:      Dict     =		[]
								for img in imgs:
													lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		img.shape[:2]
													# later: provide list and randomly choose index for resize
													lowerCamelCase_					:      List[str]     =		np.random.randint(self.short_edge_length[0]			,   self.short_edge_length[1] + 1      )
													if size == 0:
																		return img
													lowerCamelCase_					:      Union[str, Any]     =		size * 1.0 / min(A			,   A      )
													if h < w:
																		lowerCamelCase_,      lowerCamelCase_					:      Dict     =		size, scale * w
													else:
																		lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		scale * h, size
													if max(A			,   A      ) > self.max_size:
																		lowerCamelCase_					:      Optional[int]     =		self.max_size * 1.0 / max(A			,   A      )
																		lowerCamelCase_					:      Union[str, Any]     =		newh * scale
																		lowerCamelCase_					:      Tuple     =		neww * scale
													lowerCamelCase_					:      List[str]     =		int(neww + 0.5      )
													lowerCamelCase_					:      Optional[int]     =		int(newh + 0.5      )

													if img.dtype == np.uinta:
																		lowerCamelCase_					:      Any     =		Image.fromarray(A      )
																		lowerCamelCase_					:      List[str]     =		pil_image.resize((neww, newh)			,   PILImageResampling.BILINEAR      )
																		lowerCamelCase_					:      Any     =		np.asarray(A      )
													else:
																		lowerCamelCase_					:      List[Any]     =		img.permute(2			,   0			,   1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
																		lowerCamelCase_					:      Optional[int]     =		nn.functional.interpolate(
																		    A			,   (newh, neww)			,   mode=self.interp_method			,   align_corners=A      ).squeeze(0      )
													img_augs.append(A      )

								return img_augs

class 	__lowercase				:

			def __init__(self			,   A      ):
								lowerCamelCase_					:      Any     =		ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,   cfg.INPUT.MAX_SIZE_TEST      )
								lowerCamelCase_					:      Optional[int]     =		cfg.INPUT.FORMAT
								lowerCamelCase_					:      Union[str, Any]     =		cfg.SIZE_DIVISIBILITY
								lowerCamelCase_					:      Optional[Any]     =		cfg.PAD_VALUE
								lowerCamelCase_					:      Dict     =		cfg.INPUT.MAX_SIZE_TEST
								lowerCamelCase_					:      List[Any]     =		cfg.MODEL.DEVICE
								lowerCamelCase_					:      Tuple     =		torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,   1			,   1      )
								lowerCamelCase_					:      Tuple     =		torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,   1			,   1      )
								lowerCamelCase_					:      List[str]     =		lambda A      : (x - self.pixel_mean) / self.pixel_std

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Dict     =		tuple(max(A      ) for s in zip(*[img.shape for img in images]      )      )
								lowerCamelCase_					:      List[str]     =		[im.shape[-2:] for im in images]
								lowerCamelCase_					:      int     =		[
								    nn.functional.pad(
								        A			,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,   value=self.pad_value			,   )
								    for size, im in zip(A			,   A      )
								]

								return torch.stack(A      ), torch.tensor(A      )






			def __call__(self			,   A			,   A=False      ):
								with torch.no_grad():
													if not isinstance(A			,   A      ):
																		lowerCamelCase_					:      Tuple     =		[images]
													if single_image:
																		assert len(A      ) == 1
													for i in range(len(A      )      ):
																		if isinstance(images[i]			,   torch.Tensor      ):
																							images.insert(A			,   images.pop(A      ).to(self.device      ).float()      )
																		elif not isinstance(images[i]			,   torch.Tensor      ):
																							images.insert(
																							    A			,   torch.as_tensor(img_tensorize(images.pop(A      )			,   input_format=self.input_format      )      )
																							    .to(self.device      )
																							    .float()			,   )
            # resize smallest edge
													lowerCamelCase_					:      int     =		torch.tensor([im.shape[:2] for im in images]      )
													lowerCamelCase_					:      List[Any]     =		self.aug(A      )
													# transpose images and convert to torch tensors
													# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
													# now normalize before pad to avoid useless arithmetic
													lowerCamelCase_					:      Tuple     =		[self.normalizer(A      ) for x in images]
													# now pad them to do the following operations
													lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		self.pad(A      )
													# Normalize

													if self.size_divisibility > 0:
																		raise NotImplementedError()
													# pad
													lowerCamelCase_					:      Dict     =		torch.true_divide(A			,   A      )
													if single_image:
																		return images[0], sizes[0], scales_yx[0]
													else:
																		return images, sizes, scales_yx




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Optional[int]:
					'''simple docstring'''

					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes






def lowercase_	(   _lowercase			,       _lowercase				)     ->							int:
					'''simple docstring'''

					assert torch.isfinite(_lowercase				).all(), "Box tensor contains infinite or NaN!"
					lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		box_size
					tensor[:, 0].clamp_(min=0			,       max=_lowercase				)
					tensor[:, 1].clamp_(min=0			,       max=_lowercase				)
					tensor[:, 2].clamp_(min=0			,       max=_lowercase				)
					tensor[:, 3].clamp_(min=0			,       max=_lowercase				)




'''simple docstring'''


import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowercase							:   Union[str, Any]					=     logging.get_logger(__name__)




def lowercase_	(   )     ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		os.getenv('''SM_HP_MP_PARAMETERS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										lowerCamelCase_					:      Optional[Any]     =		json.loads(_lowercase				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					lowerCamelCase_					:      List[str]     =		os.getenv('''SM_FRAMEWORK_PARAMS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										lowerCamelCase_					:      List[str]     =		json.loads(_lowercase				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''			,       _lowercase				):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec('''smdistributed'''				) is not None


if is_sagemaker_model_parallel_available():
					import smdistributed.modelparallel.torch as smp

					smp.init()






@dataclass
class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		str	      =			field(
			    default=""      ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}      ,     )

			def        UpperCAmelCase__ (self      ):
								super().__post_init__()
								warnings.warn(
								    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
								    '''`TrainingArguments` instead.'''			,   A			,   )

			@cached_property
			def        UpperCAmelCase__ (self      ):
								logger.info('''PyTorch: setting up devices'''      )
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    '''torch.distributed process group is initialized, but local_rank == -1. '''
													    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''      )
								if self.no_cuda:
													lowerCamelCase_					:      Optional[int]     =		torch.device('''cpu'''      )
													lowerCamelCase_					:      Dict     =		0
								elif is_sagemaker_model_parallel_available():
													lowerCamelCase_					:      int     =		smp.local_rank()
													lowerCamelCase_					:      str     =		torch.device('''cuda'''			,   A      )
													lowerCamelCase_					:      Tuple     =		1
								elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend='''smddp'''			,   timeout=self.ddp_timeout_delta      )
													lowerCamelCase_					:      List[Any]     =		int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''      )      )
													lowerCamelCase_					:      Union[str, Any]     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		1
								elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													lowerCamelCase_					:      List[str]     =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													lowerCamelCase_					:      Any     =		torch.cuda.device_count()
								else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																		torch.distributed.init_process_group(backend='''nccl'''			,   timeout=self.ddp_timeout_delta      )
													lowerCamelCase_					:      Any     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		1

								if device.type == "cuda":
													torch.cuda.set_device(A      )

								return device

			@property
			def        UpperCAmelCase__ (self      ):
								if is_sagemaker_model_parallel_available():
													return smp.dp_size()

								return super().world_size

			@property
			def        UpperCAmelCase__ (self      ):
								return not is_sagemaker_model_parallel_available()






			@property
			def        UpperCAmelCase__ (self      ):
								return False


