


'''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 			__lowerCamelCase			:
    """simple docstring"""


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


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

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

           return img_augs




class 			__lowerCamelCase			:
    """simple docstring"""


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


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

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


    def __call__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							int=False):
           with torch.no_grad():
                  if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         _A					:     Optional[Any]     = [images]
                  if single_image:
                         assert len(SCREAMING_SNAKE_CASE) == 1
                  for i in range(len(SCREAMING_SNAKE_CASE)):
                         if isinstance(images[i]				,							torch.Tensor):
                                images.insert(SCREAMING_SNAKE_CASE				,							images.pop(SCREAMING_SNAKE_CASE).to(self.device).float())
                         elif not isinstance(images[i]				,							torch.Tensor):
                                images.insert(
                                    SCREAMING_SNAKE_CASE				,							torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE)				,							input_format=self.input_format))
                                    .to(self.device)
                                    .float()				,							)
            # resize smallest edge
                  _A					:     Optional[int]     = torch.tensor([im.shape[:2] for im in images])
                  _A					:     str     = self.aug(SCREAMING_SNAKE_CASE)
                  # 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
                  _A					:     Any     = [self.normalizer(SCREAMING_SNAKE_CASE) for x in images]
                  # now pad them to do the following operations
                  _A					:     int     = self.pad(SCREAMING_SNAKE_CASE)
                  # Normalize

                  if self.size_divisibility > 0:
                         raise NotImplementedError()
                  # pad
                  _A					:     Optional[Any]     = torch.true_divide(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  if single_image:
                         return images[0], sizes[0], scales_yx[0]
                  else:
                         return images, sizes, scales_yx






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[int]       ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes






def 				lowerCAmelCase__	(    lowerCamelCase					:      Dict  ,lowerCamelCase					:      Tuple[int, int]       ):
       assert torch.isfinite(lowerCamelCase       ).all(), "Box tensor contains infinite or NaN!"
       _A					:     Union[str, Any]     = box_size
       tensor[:, 0].clamp_(min=0  ,max=lowerCamelCase       )
       tensor[:, 1].clamp_(min=0  ,max=lowerCamelCase       )
       tensor[:, 2].clamp_(min=0  ,max=lowerCamelCase       )
       tensor[:, 3].clamp_(min=0  ,max=lowerCamelCase       )










'''simple docstring'''







import operator as op


A				:					List[str]    						=			'''scaler.pt'''
A				:					Optional[int]    						=			'''pytorch_model'''
A				:					Union[str, Any]    						=			'''random_states'''
A				:					List[Any]    						=			'''optimizer'''
A				:					Optional[Any]    						=			'''scheduler'''
A				:					Optional[int]    						=			'''pytorch_model.bin'''
A				:					List[str]    						=			'''pytorch_model.bin.index.json'''
A				:					List[Any]    						=			'''model.safetensors'''
A				:					int    						=			'''model.safetensors.index.json'''
A				:					List[str]    						=			'''1.10.2'''
A				:					str    						=			'''py38'''
A				:					int    						=			'''4.17.0'''
A				:					Tuple    						=			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
A				:					Union[str, Any]    						=			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
A				:					Optional[Any]    						=			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
A				:					Dict    						=			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
A				:					Any    						=			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
A				:					Tuple    						=			'''2.0.1'''
A				:					List[str]    						=			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
A				:					Union[str, Any]    						=			['''default''', '''reduce-overhead''', '''max-autotune''']

A				:					List[Any]    						=			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
A				:					Tuple    						=			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

A				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






