


import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
		from .testing_utils import (
		    floats_tensor,
		    load_hf_numpy,
		    load_image,
		    load_numpy,
		    load_pt,
		    nightly,
		    parse_flag_from_env,
		    print_tensor_test,
		    require_torch_a,
		    require_torch_gpu,
		    skip_mps,
		    slow,
		    torch_all_close,
		    torch_device,
		)
		from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowerCamelCase		=	get_logger(__name__)






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		if version.parse(_a ) < version.parse(_a ):
				if "dev" in min_version:
						lowerCAmelCase__     :    int					  =      (
						    '''This example requires a source install from HuggingFace diffusers (see '''
						    '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
						)
				else:
						lowerCAmelCase__     :    Tuple					  =      f'This example requires a minimum version of {min_version},'
				error_message += f' but the version found is {__version__}.\n'
				raise ImportError(_a )








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
lowerCamelCase		=	False
lowerCamelCase		=	Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowerCamelCase		=	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, 2048), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)





class       _a							:




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

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

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




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	)-> Optional[Any]:
			lowerCAmelCase__     :    Optional[Any]					  =      int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
			lowerCAmelCase__     :    List[Any]					  =      os.path.join(self.inputdir	,      _SCREAMING_SNAKE_CASE	)
			return (img_id, filepath)




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> Tuple:
			lowerCAmelCase__     :    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(_SCREAMING_SNAKE_CASE	)	)
					if len(_SCREAMING_SNAKE_CASE	) == self.batch:
							lowerCAmelCase__     :    Optional[int]					  =      batch
							lowerCAmelCase__     :    Union[str, Any]					  =      []
							yield list(map(_SCREAMING_SNAKE_CASE	,      zip(*_SCREAMING_SNAKE_CASE	)	)	)

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




	def __call__(      self		:	int	)-> Dict:
			# make writer
			if not TEST:
					lowerCAmelCase__     :    Union[str, Any]					  =      datasets.ArrowWriter(features=self.schema	,      path=self.outputfile	)
			# do file generator
			for i, (img_ids, filepaths) in enumerate(self.file_generator	):
					lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.preprocess(_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    List[Any]					  =      self.model(
					    _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      scales_yx=_SCREAMING_SNAKE_CASE	,      padding='''max_detections'''	,      max_detections=self.config.MAX_DETECTIONS	,      pad_value=0	,      return_tensors='''np'''	,      location='''cpu'''	,      )
					lowerCAmelCase__     :    Any					  =      output_dict.pop('''normalized_boxes'''	)
					if not TEST:
							lowerCAmelCase__     :    Optional[int]					  =      np.array(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    str					  =      self.schema.encode_batch(_SCREAMING_SNAKE_CASE	)
							writer.write_batch(_SCREAMING_SNAKE_CASE	)
					if TEST:
							break
					# finalizer the writer
			if not TEST:
					lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[int]					  =      writer.finalize()
					print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		try:
				lowerCAmelCase__     :    Any					  =      json.load(_a )
				try:
						lowerCAmelCase__     :    Tuple					  =      list(data.keys() )
				except Exception:
						lowerCAmelCase__     :    List[str]					  =      [d['''img_id'''] for d in data]
		except Exception:
				try:
						lowerCAmelCase__     :    int					  =      eval(stream.read() )
				except Exception:
						lowerCAmelCase__     :    Tuple					  =      stream.read().split('''\n''' )
		return data


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




