





'''simple docstring'''



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int=None						,    lowerCamelCase_     :					Optional[Any]=None			):
			return field(default_factory=lambda: default						,    metadata=lowerCamelCase_			)






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			List[str]     =    list_field(
	    default=[]	, metadata={
	        "help": (
	            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
	            " of all available models"
	        )
	    }	, )

	a      :			List[int]     =    list_field(
	    default=[8]	, metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}  )

	a      :			List[int]     =    list_field(
	    default=[8, 32, 128, 512]	, metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}	, )

	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}	, )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}	, )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}  )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Use FP16 to accelerate inference."}  )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Benchmark training of model"}  )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Verbose memory tracing"}  )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}	, )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }	, )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Trace memory line by line"}  )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Save result to a CSV file"}  )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Save all print statements in a log file"}  )
	a      :			bool     =    field(default=lowerCAmelCase__	, metadata={"help": "Whether to print environment information"}  )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={
	        "help": (
	            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
	            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
	            " for debugging / testing and on TPU."
	        )
	    }	, )
	a      :			str     =    field(
	    default=F"""inference_time_{round(time()  )}.csv"""	, metadata={"help": "CSV filename used if saving time results to csv."}	, )
	a      :			str     =    field(
	    default=F"""inference_memory_{round(time()  )}.csv"""	, metadata={"help": "CSV filename used if saving memory results to csv."}	, )
	a      :			str     =    field(
	    default=F"""train_time_{round(time()  )}.csv"""	, metadata={"help": "CSV filename used if saving time results to csv for training."}	, )
	a      :			str     =    field(
	    default=F"""train_memory_{round(time()  )}.csv"""	, metadata={"help": "CSV filename used if saving memory results to csv for training."}	, )
	a      :			str     =    field(
	    default=F"""env_info_{round(time()  )}.csv"""	, metadata={"help": "CSV filename used if saving environment information."}	, )
	a      :			str     =    field(
	    default=F"""log_{round(time()  )}.csv"""	, metadata={"help": "Log filename used if print statements are saved in log."}	, )
	a      :			int     =    field(default=3	, metadata={"help": "Times an experiment will be run."}  )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={
	        "help": (
	            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
	            " model weights."
	        )
	    }	, )


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				warnings.warn(
				    f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
				    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
				    ''' to benchmark Transformer models.'''       ,_lowerCamelCase       ,)


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				return json.dumps(dataclasses.asdict(self	)       ,indent=2	)


	@property
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				if len(self.models	) <= 0:
							raise ValueError(
							    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
							    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''	)
				return self.models


	@property
	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				if not self.multi_process:
							return False
				elif self.is_tpu:
							logger.info('''Multiprocessing is currently not possible on TPU.'''	)
							return False
				else:
							return True












'''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    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=sys.maxsize	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							'''bilinear'''
				__lowercase			  =							max_size
				__lowercase			  =							short_edge_length


	def __call__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





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

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

				return img_augs






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,cfg.INPUT.MAX_SIZE_TEST	)
				__lowercase			  =							cfg.INPUT.FORMAT
				__lowercase			  =							cfg.SIZE_DIVISIBILITY
				__lowercase			  =							cfg.PAD_VALUE
				__lowercase			  =							cfg.INPUT.MAX_SIZE_TEST
				__lowercase			  =							cfg.MODEL.DEVICE
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							lambda _lowerCamelCase	: (x - self.pixel_mean) / self.pixel_std


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							tuple(max(_lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
				__lowercase			  =							[im.shape[-2:] for im in images]
				__lowercase			  =							[
				    nn.functional.pad(
				        _lowerCamelCase       ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,value=self.pad_value       ,)
				    for size, im in zip(_lowerCamelCase       ,_lowerCamelCase	)
				]

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


	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Tuple:







				'''simple docstring'''





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

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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Tuple			):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Tuple[int, int]			):
			assert torch.isfinite(lowerCamelCase_			).all(), "Box tensor contains infinite or NaN!"
			__lowercase  ,						__lowercase			  =							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_			)





