

"""simple docstring"""





import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


_a				   =							namedtuple('ExampleNamedTuple', 'a b c')




class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		torch.randn(5,      2			)
    __lowercase			=		torch.device("cuda"			) if torch.cuda.is_available() else torch.device("cpu"			)

    __lowercase			=		send_to_device(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta.cpu(),      UpperCAmelCase__			)			)

    __lowercase			=		send_to_device((tensor, [tensor, tensor], 1),      UpperCAmelCase__			)
    self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta[0].cpu(),      UpperCAmelCase__			)			)
    self.assertIsInstance(resulta[1],      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta[1][0].cpu(),      UpperCAmelCase__			)			)
    self.assertTrue(torch.equal(resulta[1][1].cpu(),      UpperCAmelCase__			)			)
    self.assertEqual(resulta[2],      1			)

    __lowercase			=		send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1},      UpperCAmelCase__			)
    self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta["a"].cpu(),      UpperCAmelCase__			)			)
    self.assertIsInstance(resulta["b"],      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta["b"][0].cpu(),      UpperCAmelCase__			)			)
    self.assertTrue(torch.equal(resulta["b"][1].cpu(),      UpperCAmelCase__			)			)
    self.assertEqual(resulta["c"],      1			)

    __lowercase			=		send_to_device(ExampleNamedTuple(a=UpperCAmelCase__,      b=[tensor, tensor],      c=1			),      UpperCAmelCase__			)
    self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta.a.cpu(),      UpperCAmelCase__			)			)
    self.assertIsInstance(resulta.b,      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta.b[0].cpu(),      UpperCAmelCase__			)			)
    self.assertTrue(torch.equal(resulta.b[1].cpu(),      UpperCAmelCase__			)			)
    self.assertEqual(resulta.c,      1			)

    __lowercase			=		send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}			),      UpperCAmelCase__			)
    self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta["a"].cpu(),      UpperCAmelCase__			)			)
    self.assertIsInstance(resulta["b"],      UpperCAmelCase__			)
    self.assertTrue(torch.equal(resulta["b"][0].cpu(),      UpperCAmelCase__			)			)
    self.assertTrue(torch.equal(resulta["b"][1].cpu(),      UpperCAmelCase__			)			)
    self.assertEqual(resulta["c"],      1			)


 def       _lowercase				(    self							:    Tuple			):
    with self.assertRaises(UpperCAmelCase__			) as cm:
       __lowercase			=		recursively_apply(torch.tensor,      (torch.tensor(1			), 1),      error_on_other_type=UpperCAmelCase__			)
    self.assertEqual(
        str(cm.exception			),      "Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.",      )


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		torch.tensor([1, 2, 3, 4, 5]			)
    self.assertEqual(listify(UpperCAmelCase__			),      [1, 2, 3, 4, 5]			)

    __lowercase			=		torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]			)
    self.assertEqual(listify(UpperCAmelCase__			),      [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]			)

    __lowercase			=		torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]			)
    self.assertEqual(
        listify(UpperCAmelCase__			),      [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]			)


 def       _lowercase				(    self							:    Dict			):
    with patch_environment(aa=1,      BB=2			):
       self.assertEqual(os.environ.get("AA"			),      "1"			)
       self.assertEqual(os.environ.get("BB"			),      "2"			)

    self.assertNotIn("AA",      os.environ			)
    self.assertNotIn("BB",      os.environ			)


 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		RegressionModel()
    __lowercase			=		model.forward
    __lowercase			=		convert_outputs_to_fpaa(model.forward			)
    __lowercase			=		extract_model_from_parallel(UpperCAmelCase__,      keep_fpaa_wrapper=UpperCAmelCase__			)
    __lowercase			=		pickle.dumps(UpperCAmelCase__			)


 @require_cuda
 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		RegressionModel()
    __lowercase			=		model.forward
    __lowercase			=		torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
    __lowercase			=		convert_outputs_to_fpaa(model.forward			)
    __lowercase			=		extract_model_from_parallel(UpperCAmelCase__,      keep_fpaa_wrapper=UpperCAmelCase__			)
    __lowercase			=		pickle.dumps(UpperCAmelCase__			)


 @require_cuda
 @require_torch_min_version(version="2.0"			)
 def       _lowercase				(    self							:    Any			):
    __lowercase			=		RegressionModel()
    __lowercase			=		model.forward
    __lowercase			=		torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
    __lowercase			=		convert_outputs_to_fpaa(model.forward			)
    __lowercase			=		torch.compile(model.forward,      backend="inductor"			)
    __lowercase			=		torch.randn(4,      1_0			).cuda()
    __lowercase			=		model(UpperCAmelCase__			)


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		RegressionModel()
    # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
    __lowercase			=		torch.nn.parallel.DataParallel(UpperCAmelCase__			)
    __lowercase			=		extract_model_from_parallel(UpperCAmelCase__			)

    self.assertEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 @require_torch_min_version(version="2.0"			)
 def       _lowercase				(    self							:    int			):
    __lowercase			=		RegressionModel()
    __lowercase			=		torch.compile(UpperCAmelCase__			)

    # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
    __lowercase			=		torch.nn.parallel.DataParallel(UpperCAmelCase__			)
    __lowercase			=		torch.compile(UpperCAmelCase__			)
    __lowercase			=		extract_model_from_parallel(UpperCAmelCase__			)

    self.assertEqual(compiled_model._orig_mod,      compiled_model_unwrapped._orig_mod			)



 def       _lowercase				(    self							:    str			):
    self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3]			)]			),      torch.device("cpu"			)			)
    self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3]			)}			),      torch.device("cpu"			)			)
    self.assertIsNone(find_device([1, "a"]			)			)







"""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)




