

"""simple docstring"""





import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_a     :   Optional[int]				   =							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_a     :   Any				   =							direct_transformers_import(Path(__file__).parent)


_a     :   List[Any]				   =							{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}




class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Optional[int]     	=			["feature_extractor", "tokenizer"]
 # Names need to be attr_class for attr in attributes
 __UpperCAmelCase			:      Union[str, Any]     	=			None
 __UpperCAmelCase			:      Optional[int]     	=			None
 __UpperCAmelCase			:      Any     	=			None


 def __init__(    self							:    Union[str, Any],      *UpperCAmelCase__							:    Any,      **UpperCAmelCase__							:    str			):
    # Sanitize args and kwargs
    for key in kwargs:
       if key not in self.attributes:
          raise TypeError(F"""Unexpected keyword argument {key}."""			)
    for arg, attribute_name in zip(UpperCAmelCase__,      self.attributes			):
       if attribute_name in kwargs:
          raise TypeError(F"""Got multiple values for argument {attribute_name}."""			)
       else:
          __lowercase			=		arg

    if len(UpperCAmelCase__			) != len(self.attributes			):
       raise ValueError(
           F"""This processor requires {len(self.attributes			)} arguments: {", ".join(self.attributes			)}. Got """
           F"""{len(UpperCAmelCase__			)} arguments instead."""			)

    # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
    for attribute_name, arg in kwargs.items():
       __lowercase			=		getattr(self,      F"""{attribute_name}_class"""			)
       # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
       __lowercase			=		AUTO_TO_BASE_CLASS_MAPPING.get(UpperCAmelCase__,      UpperCAmelCase__			)
       if isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
          __lowercase			=		tuple(getattr(UpperCAmelCase__,      UpperCAmelCase__			) for n in class_name if n is not None			)
       else:
          __lowercase			=		getattr(UpperCAmelCase__,      UpperCAmelCase__			)

       if not isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
          raise ValueError(
              F"""Received a {type(UpperCAmelCase__			).__name__} for argument {attribute_name}, but a {class_name} was expected."""			)

       setattr(self,      UpperCAmelCase__,      UpperCAmelCase__			)


 def __repr__(    self							:    Optional[int]			):
    __lowercase			=		[F"""- {name}: {repr(getattr(self,      UpperCAmelCase__			)			)}""" for name in self.attributes]
    __lowercase			=		"\n".join(UpperCAmelCase__			)
    return F"""{self.__class__.__name__}:\n{attributes_repr}"""


 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    bool = False,      **UpperCAmelCase__							:    Optional[Any]			):
    os.makedirs(UpperCAmelCase__,      exist_ok=UpperCAmelCase__			)

    if push_to_hub:
       __lowercase			=		kwargs.pop("commit_message",      UpperCAmelCase__			)
       __lowercase			=		kwargs.pop("repo_id",      save_directory.split(os.path.sep			)[-1]			)
       __lowercase			=		self._create_repo(UpperCAmelCase__,      **UpperCAmelCase__			)
       __lowercase			=		self._get_files_timestamps(UpperCAmelCase__			)
    # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
    # loaded from the Hub.
    if self._auto_class is not None:
       __lowercase			=		[getattr(self,      UpperCAmelCase__			) for attribute_name in self.attributes]
       __lowercase			=		[(a.init_kwargs if isinstance(UpperCAmelCase__,      UpperCAmelCase__			) else a) for a in attrs]
       custom_object_save(self,      UpperCAmelCase__,      config=UpperCAmelCase__			)

    for attribute_name in self.attributes:
       __lowercase			=		getattr(self,      UpperCAmelCase__			)
       # Include the processor class in the attribute config so this processor can then be reloaded with the
       # `AutoProcessor` API.
       if hasattr(UpperCAmelCase__,      "_set_processor_class"			):
          attribute._set_processor_class(self.__class__.__name__			)
       attribute.save_pretrained(UpperCAmelCase__			)

    if self._auto_class is not None:
       # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
       for attribute_name in self.attributes:
          __lowercase			=		getattr(self,      UpperCAmelCase__			)
          if isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
             del attribute.init_kwargs["auto_map"]

    if push_to_hub:
       self._upload_modified_files(
           UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      commit_message=UpperCAmelCase__,      token=kwargs.get("use_auth_token"			),      )


 @classmethod
 def       _lowercase				(    cls							:    Any,      UpperCAmelCase__							:    Union[str, os.PathLike],      UpperCAmelCase__							:    Optional[Union[str, os.PathLike]] = None,      UpperCAmelCase__							:    bool = False,      UpperCAmelCase__							:    bool = False,      UpperCAmelCase__							:    Optional[Union[str, bool]] = None,      UpperCAmelCase__							:    str = "main",      **UpperCAmelCase__							:    Dict,      ):
    __lowercase			=		cache_dir
    __lowercase			=		force_download
    __lowercase			=		local_files_only
    __lowercase			=		revision

    __lowercase			=		kwargs.pop("use_auth_token",      UpperCAmelCase__			)
    if use_auth_token is not None:
       warnings.warn(
           "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.",      UpperCAmelCase__			)
       if token is not None:
          raise ValueError(
              "`token` and `use_auth_token` are both specified. Please set only the argument `token`."			)
       __lowercase			=		use_auth_token

    if token is not None:
       # change to `token` in a follow-up PR
       __lowercase			=		token

    __lowercase			=		cls._get_arguments_from_pretrained(UpperCAmelCase__,      **UpperCAmelCase__			)
    return cls(*UpperCAmelCase__			)


 @classmethod
 def       _lowercase				(    cls							:    int,      UpperCAmelCase__							:    Any="AutoProcessor"			):
    if not isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
       __lowercase			=		auto_class.__name__

    import transformers.models.auto as auto_module

    if not hasattr(UpperCAmelCase__,      UpperCAmelCase__			):
       raise ValueError(F"""{auto_class} is not a valid auto class."""			)

    __lowercase			=		auto_class


 @classmethod
 def       _lowercase				(    cls							:    Dict,      UpperCAmelCase__							:    Dict,      **UpperCAmelCase__							:    Any			):
    __lowercase			=		[]
    for attribute_name in cls.attributes:
       __lowercase			=		getattr(cls,      F"""{attribute_name}_class"""			)
       if isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
          __lowercase			=		tuple(getattr(UpperCAmelCase__,      UpperCAmelCase__			) if n is not None else None for n in class_name			)
          __lowercase			=		kwargs.get("use_fast",      UpperCAmelCase__			)
          if use_fast and classes[1] is not None:
             __lowercase			=		classes[1]
          else:
             __lowercase			=		classes[0]
       else:
          __lowercase			=		getattr(UpperCAmelCase__,      UpperCAmelCase__			)

       args.append(attribute_class.from_pretrained(UpperCAmelCase__,      **UpperCAmelCase__			)			)
    return args



 @property
 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		getattr(self,      self.attributes[0]			)
    return getattr(UpperCAmelCase__,      "model_input_names",      UpperCAmelCase__			)







_a     :   Dict				   =							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
   _a     :   List[str]				   =							ProcessorMixin.push_to_hub.__doc__.format(
       object='processor', object_class='AutoProcessor', object_files='processor files'
   )







"""simple docstring"""





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
_a				   =							False
_a				   =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a				   =							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						:







 """simple docstring"""







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

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

    __lowercase			=		CONFIG
    if torch.cuda.is_available():
       __lowercase			=		"cuda"
    __lowercase			=		os.path.realpath(UpperCAmelCase__			)
    __lowercase			=		os.path.realpath(UpperCAmelCase__			)
    __lowercase			=		Preprocess(self.config			)
    __lowercase			=		GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned",      config=self.config			)
    __lowercase			=		batch_size if batch_size != 0 else 1
    __lowercase			=		DEFAULT_SCHEMA


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[Any]			):
    __lowercase			=		int(file.split("."			)[0].split("_"			)[-1]			)
    __lowercase			=		os.path.join(self.inputdir,      UpperCAmelCase__			)
    return (img_id, filepath)


 @property
 def       _lowercase				(    self							:    int			):
    __lowercase			=		[]
    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(UpperCAmelCase__			)			)
       if len(UpperCAmelCase__			) == self.batch:
          __lowercase			=		batch
          __lowercase			=		[]
          yield list(map(UpperCAmelCase__,      zip(*UpperCAmelCase__			)			)			)

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



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







def 						_A						(						UpperCamelCase_  : int)			->		Optional[Any]:
   '''simple docstring'''


   try:
      __lowercase			=		json.load(UpperCamelCase_)
      try:
         __lowercase			=		list(data.keys())
      except Exception:
         __lowercase			=		[d["img_id"] for d in data]
   except Exception:
      try:
         __lowercase			=		eval(stream.read())
      except Exception:
         __lowercase			=		stream.read().split("\n")
   return data


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




