



import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			CONFIG_NAME


						@register_to_config
						def __init__(			self    :	Any   ,							_a    :	bool = True   ,							_a    :	int = 8   ,							_a    :	str = "lanczos"   ,							_a    :	bool = True   ,							_a    :	bool = False   ,							):
													super().__init__()


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	np.ndarray      ):
													if images.ndim == 3:
																				a__:							str									=images[None, ...]
													a__:							Optional[int]									=(images * 2_5_5).round().astype("uint8"      )
													if images.shape[-1] == 1:
																				# special case for grayscale (single channel) images
																				a__:							Dict									=[Image.fromarray(image.squeeze()   ,							mode="L"      ) for image in images]
													else:
																				a__:							int									=[Image.fromarray(_a      ) for image in images]

													return pil_images


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Union[List[PIL.Image.Image], PIL.Image.Image]      ):
													if not isinstance(_a   ,							_a      ):
																				a__:							List[Any]									=[images]
													a__:							Tuple									=[np.array(_a      ).astype(np.floataa      ) / 2_5_5.0 for image in images]
													a__:							List[Any]									=np.stack(_a   ,							axis=0      )

													return images


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	np.ndarray      ):
													if images.ndim == 3:
																				a__:							Optional[Any]									=images[..., None]

													a__:							int									=torch.from_numpy(images.transpose(0   ,							3   ,							1   ,							2      )      )
													return images


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	torch.FloatTensor      ):
													a__:							int									=images.cpu().permute(0   ,							2   ,							3   ,							1      ).float().numpy()
													return images


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	List[Any]      ):
													return 2.0 * images - 1.0


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	List[Any]      ):
													return (images / 2 + 0.5).clamp(0   ,							1      )


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	PIL.Image.Image      ):
													a__:							Union[str, Any]									=image.convert("RGB"      )
													return image


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	PIL.Image.Image   ,							_a    :	Optional[int] = None   ,							_a    :	Optional[int] = None   ,							):
													if height is None:
																				a__:							str									=image.height
													if width is None:
																				a__:							Optional[Any]									=image.width

													a__				,      a__:							Any									=(
													    x - x % self.config.vae_scale_factor for x in (width, height)
													)  # resize to integer multiple of vae_scale_factor
													a__:							Any									=image.resize((width, height)   ,							resample=PIL_INTERPOLATION[self.config.resample]      )
													return image


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]   ,							_a    :	Optional[int] = None   ,							_a    :	Optional[int] = None   ,							):
													a__:							List[Any]									=(PIL.Image.Image, np.ndarray, torch.Tensor)
													if isinstance(_a   ,							_a      ):
																				a__:							Optional[Any]									=[image]
													elif not (isinstance(_a   ,							_a      ) and all(isinstance(_a   ,							_a      ) for i in image      )):
																				raise ValueError(
																				    F"Input is in incorrect format: {[type(_a      ) for i in image]}. Currently, we only support {', '.join(_a      )}"      )

													if isinstance(image[0]   ,							PIL.Image.Image      ):
																				if self.config.do_convert_rgb:
																											a__:							Dict									=[self.convert_to_rgb(_a      ) for i in image]
																				if self.config.do_resize:
																											a__:							str									=[self.resize(_a   ,							_a   ,							_a      ) for i in image]
																				a__:							List[str]									=self.pil_to_numpy(_a      )  # to np
																				a__:							Union[str, Any]									=self.numpy_to_pt(_a      )  # to pt

													elif isinstance(image[0]   ,							np.ndarray      ):
																				a__:							Dict									=np.concatenate(_a   ,							axis=0      ) if image[0].ndim == 4 else np.stack(_a   ,							axis=0      )
																				a__:							int									=self.numpy_to_pt(_a      )
																				a__				,      a__				,      a__				,      a__:							Dict									=image.shape
																				if self.config.do_resize and (
																				    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																				):
																											raise ValueError(
																											    F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
																											    F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"      )

													elif isinstance(image[0]   ,							torch.Tensor      ):
																				a__:							str									=torch.cat(_a   ,							axis=0      ) if image[0].ndim == 4 else torch.stack(_a   ,							axis=0      )
																				a__				,      a__				,      a__				,      a__:							Tuple									=image.shape

																				# don't need any preprocess if the image is latents
																				if channel == 4:
																											return image

																				if self.config.do_resize and (
																				    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																				):
																											raise ValueError(
																											    F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
																											    F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"      )

        # expected range [0,1], normalize to [-1,1]
													a__:							Tuple									=self.config.do_normalize
													if image.min() < 0:
																				warnings.warn(
																				    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
																				    F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"   ,							_a   ,							)
																				a__:							Union[str, Any]									=False

													if do_normalize:
																				a__:							int									=self.normalize(_a      )

													return image







						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	torch.FloatTensor   ,							_a    :	str = "pil"   ,							_a    :	Optional[List[bool]] = None   ,							):
													if not isinstance(_a   ,							torch.Tensor      ):
																				raise ValueError(
																				    F"Input for postprocessing is in incorrect format: {type(_a      )}. We only support pytorch tensor"      )
													if output_type not in ["latent", "pt", "np", "pil"]:
																				a__:							Tuple									=(
																				    F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
																				    "`pil`, `np`, `pt`, `latent`"
																				)
																				deprecate("Unsupported output_type"   ,							"1.0.0"   ,							_a   ,							standard_warn=_a      )
																				a__:							Tuple									="np"

													if output_type == "latent":
																				return image

													if do_denormalize is None:
																				a__:							Optional[Any]									=[self.config.do_normalize] * image.shape[0]

													a__:							Optional[Any]									=torch.stack(
													    [self.denormalize(image[i]      ) if do_denormalize[i] else image[i] for i in range(image.shape[0]      )]      )

													if output_type == "pt":
																				return image

													a__:							Any									=self.pt_to_numpy(_a      )

													if output_type == "np":
																				return image

													if output_type == "pil":
																				return self.numpy_to_pil(_a      )


class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			CONFIG_NAME


						@register_to_config
						def __init__(			self    :	Optional[int]   ,							_a    :	bool = True   ,							_a    :	int = 8   ,							_a    :	str = "lanczos"   ,							_a    :	bool = True   ,							):
													super().__init__()


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	str      ):
													if images.ndim == 3:
																				a__:							Any									=images[None, ...]
													a__:							Optional[Any]									=(images * 2_5_5).round().astype("uint8"      )
													if images.shape[-1] == 1:
																				# special case for grayscale (single channel) images
																				a__:							Dict									=[Image.fromarray(image.squeeze()   ,							mode="L"      ) for image in images]
													else:
																				a__:							Union[str, Any]									=[Image.fromarray(image[:, :, :3]      ) for image in images]

													return pil_images


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	List[Any]      ):
													return image[:, :, 1] * 2**8 + image[:, :, 2]


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	Optional[Any]      ):
													if images.ndim == 3:
																				a__:							Dict									=images[None, ...]
													a__:							int									=images[:, :, :, 3:]
													if images.shape[-1] == 6:
																				a__:							Dict									=(images_depth * 2_5_5).round().astype("uint8"      )
																				a__:							Optional[int]									=[
																				    Image.fromarray(self.rgblike_to_depthmap(_a      )   ,							mode="I;16"      ) for image_depth in images_depth
																				]
													elif images.shape[-1] == 4:
																				a__:							Any									=(images_depth * 6_5_5_3_5.0).astype(np.uintaa      )
																				a__:							Optional[int]									=[Image.fromarray(_a   ,							mode="I;16"      ) for image_depth in images_depth]
													else:
																				raise Exception("Not supported"      )

													return pil_images







						def 	_lowerCamelCase      (			self    :	Optional[int]   ,							_a    :	torch.FloatTensor   ,							_a    :	str = "pil"   ,							_a    :	Optional[List[bool]] = None   ,							):
													if not isinstance(_a   ,							torch.Tensor      ):
																				raise ValueError(
																				    F"Input for postprocessing is in incorrect format: {type(_a      )}. We only support pytorch tensor"      )
													if output_type not in ["latent", "pt", "np", "pil"]:
																				a__:							int									=(
																				    F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
																				    "`pil`, `np`, `pt`, `latent`"
																				)
																				deprecate("Unsupported output_type"   ,							"1.0.0"   ,							_a   ,							standard_warn=_a      )
																				a__:							Any									="np"

													if do_denormalize is None:
																				a__:							Dict									=[self.config.do_normalize] * image.shape[0]

													a__:							str									=torch.stack(
													    [self.denormalize(image[i]      ) if do_denormalize[i] else image[i] for i in range(image.shape[0]      )]      )

													a__:							int									=self.pt_to_numpy(_a      )

													if output_type == "np":
																				if image.shape[-1] == 6:
																											a__:							Optional[int]									=np.stack([self.rgblike_to_depthmap(im[:, :, 3:]      ) for im in image]   ,							axis=0      )
																				else:
																											a__:							Any									=image[:, :, :, 3:]
																				return image[:, :, :, :3], image_depth

													if output_type == "pil":
																				return self.numpy_to_pil(_a      ), self.numpy_to_depth(_a      )
													else:
																				raise Exception(F"This type {output_type} is not supported"      )










import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__UpperCAmelCase    	=  False
__UpperCAmelCase    	=  Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
__UpperCAmelCase    	=  datasets.Features(
    OrderedDict(
        {
            '''attr_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''attr_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''boxes''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='''float32'''),
            '''img_id''': datasets.Value('''int32'''),
            '''obj_ids''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''obj_probs''': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('''float32''')),
            '''roi_features''': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)


class 						lowerCamelCase__			:


						def __init__(			self    :	int   ,							_a    :	List[str]=sys.argv[1:]      ):
													a__:							List[Any]									=None
													a__:							int									=None
													a__:							int									=None
													a__:							List[str]									=1
													a__				,      a__:							List[Any]									=getopt.getopt(_a   ,							"i:o:b:s"   ,							["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
													for opt, arg in opts:
																				if opt in ("-i", "--inputdir"):
																											a__:							Union[str, Any]									=arg
																				elif opt in ("-o", "--outfile"):
																											a__:							List[str]									=arg
																				elif opt in ("-b", "--batch_size"):
																											a__:							List[str]									=int(_a      )
																				elif opt in ("-s", "--subset_list"):
																											a__:							str									=arg

													assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
													assert outputfile is not None and not os.path.isfile(_a      ), F"{outputfile}"
													if subset_list is not None:
																				with open(os.path.realpath(_a      )      ) as f:
																											a__:							Any									={self._vqa_file_split()[0] for x in tryload(_a      )}
													else:
																				a__:							Optional[Any]									=None

													a__:							List[Any]									=CONFIG
													if torch.cuda.is_available():
																				a__:							Dict									="cuda"
													a__:							Any									=os.path.realpath(_a      )
													a__:							Optional[int]									=os.path.realpath(_a      )
													a__:							Union[str, Any]									=Preprocess(self.config      )
													a__:							str									=GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"   ,							config=self.config      )
													a__:							List[Any]									=batch_size if batch_size != 0 else 1
													a__:							Union[str, Any]									=DEFAULT_SCHEMA


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Dict      ):
													a__:							Tuple									=int(file.split("."      )[0].split("_"      )[-1]      )
													a__:							int									=os.path.join(self.inputdir   ,							_a      )
													return (img_id, filepath)


						@property
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							List[Any]									=[]
													for i, file in enumerate(os.listdir(self.inputdir      )      ):
																				if self.subset_list is not None and i not in self.subset_list:
																											continue
																				batch.append(self._vqa_file_split(_a      )      )
																				if len(_a      ) == self.batch:
																											a__:							List[Any]									=batch
																											a__:							Union[str, Any]									=[]
																											yield list(map(_a   ,							zip(*_a      )      )      )

													for i in range(1      ):
																				yield list(map(_a   ,							zip(*_a      )      )      )







						def __call__(			self    :	List[str]      ):
													# make writer
													if not TEST:
																				a__:							int									=datasets.ArrowWriter(features=self.schema   ,							path=self.outputfile      )
													# do file generator
													for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
																				a__				,      a__				,      a__:							Dict									=self.preprocess(_a      )
																				a__:							Optional[Any]									=self.model(
																				    _a   ,							_a   ,							scales_yx=_a   ,							padding="max_detections"   ,							max_detections=self.config.MAX_DETECTIONS   ,							pad_value=0   ,							return_tensors="np"   ,							location="cpu"   ,							)
																				a__:							Dict									=output_dict.pop("normalized_boxes"      )
																				if not TEST:
																											a__:							Tuple									=np.array(_a      )
																											a__:							Union[str, Any]									=self.schema.encode_batch(_a      )
																											writer.write_batch(_a      )
																				if TEST:
																											break
																				# finalizer the writer
													if not TEST:
																				a__				,      a__:							Any									=writer.finalize()
																				print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"      )





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]    ):
							try:
														a__:							Any									=json.load(__magic_name__    )
														try:
																					a__:							List[str]									=list(data.keys()    )
														except Exception:
																					a__:							Union[str, Any]									=[d["img_id"] for d in data]
							except Exception:
														try:
																					a__:							Dict									=eval(stream.read()    )
														except Exception:
																					a__:							int									=stream.read().split("\n"    )
							return data


if __name__ == "__main__":
		__UpperCAmelCase    	=  Extract(sys.argv[1:])
		extract()
		if not TEST:
				__UpperCAmelCase    	=  datasets.Dataset.from_file(extract.outputfile)
				# wala!
				# print(np.array(dataset[0:2]["roi_features"]).shape)





