



"""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 			snake_case_						:






							def __init__(   self			,     a_			,     a_=sys.maxsize      ):
									a_     :      List[str] 						=     "bilinear"
									a_     :      Dict 						=     max_size
									a_     :      List[str] 						=     short_edge_length






							def __call__(   self			,     a_      ):
									a_     :      Optional[Any] 						=     []
									for img in imgs:
											a_						,				a_     :      Optional[int] 						=     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_     :      str 						=     size * 1.0 / min(a_			,     a_      )
											if h < w:
													a_						,				a_     :      Dict 						=     size, scale * w
											else:
													a_						,				a_     :      Optional[int] 						=     scale * h, size
											if max(a_			,     a_      ) > self.max_size:
													a_     :      List[Any] 						=     self.max_size * 1.0 / max(a_			,     a_      )
													a_     :      Any 						=     newh * scale
													a_     :      Dict 						=     neww * scale
											a_     :      List[Any] 						=     int(neww + 0.5      )
											a_     :      Optional[int] 						=     int(newh + 0.5      )

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

									return img_augs




class 			snake_case_						:






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






							def snake_case_  (   self			,     a_      ):
									a_     :      str 						=     tuple(max(a_      ) for s in zip(*[img.shape for img in images]      )      )
									a_     :      Optional[int] 						=     [im.shape[-2:] for im in images]
									a_     :      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_      ):
													a_     :      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
											a_     :      List[Any] 						=     torch.tensor([im.shape[:2] for im in images]      )
											a_     :      Tuple 						=     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
											a_     :      Optional[int] 						=     [self.normalizer(a_      ) for x in images]
											# now pad them to do the following operations
											a_						,				a_     :      str 						=     self.pad(a_      )
											# Normalize

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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Any:
		assert torch.isfinite(SCREAMING_SNAKE_CASE__	).all(), "Box tensor contains infinite or NaN!"
		a_						,				a_     :      int 						=     box_size
		tensor[:, 0].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)
		tensor[:, 1].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)
		tensor[:, 2].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)
		tensor[:, 3].clamp_(min=0, max=SCREAMING_SNAKE_CASE__	)




"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, help=(
		        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
		        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
		        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
		        "with 'huggingface'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)