



"""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,
)


UpperCAmelCase:   Union[str, Any]       			=							namedtuple("""ExampleNamedTuple""", """a b c""")





class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					torch.randn(5							,2					)
			_lowercase							:      List[str]			=					torch.device("""cuda"""					) if torch.cuda.is_available() else torch.device("""cpu"""					)

			_lowercase							:      int			=					send_to_device(UpperCAmelCase_							,UpperCAmelCase_					)
			self.assertTrue(torch.equal(resulta.cpu()							,UpperCAmelCase_					)					)

			_lowercase							:      str			=					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							:      Any			=					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							:      List[Any]			=					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							:      Union[str, Any]			=					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 					lowerCamelCase__ (			self					):
			with self.assertRaises(UpperCAmelCase_					) as cm:
					_lowercase							:      List[str]			=					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 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					torch.tensor([1, 2, 3, 4, 5]					)
			self.assertEqual(listify(UpperCAmelCase_					)							,[1, 2, 3, 4, 5]					)

			_lowercase							:      Any			=					torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)
			self.assertEqual(listify(UpperCAmelCase_					)							,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)

			_lowercase							:      Any			=					torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)
			self.assertEqual(
			    listify(UpperCAmelCase_					)							,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)






	def 					lowerCamelCase__ (			self					):
			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 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					RegressionModel()
			_lowercase							:      Optional[Any]			=					model.forward
			_lowercase							:      Tuple			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      str			=					extract_model_from_parallel(UpperCAmelCase_							,keep_fpaa_wrapper=UpperCAmelCase_					)
			_lowercase							:      List[str]			=					pickle.dumps(UpperCAmelCase_					)






	@require_cuda
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[Any]			=					RegressionModel()
			_lowercase							:      int			=					model.forward
			_lowercase							:      List[str]			=					torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
			_lowercase							:      Dict			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      str			=					extract_model_from_parallel(UpperCAmelCase_							,keep_fpaa_wrapper=UpperCAmelCase_					)
			_lowercase							:      Tuple			=					pickle.dumps(UpperCAmelCase_					)






	@require_cuda
	@require_torch_min_version(version="""2.0"""					)
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Dict			=					RegressionModel()
			_lowercase							:      List[str]			=					model.forward
			_lowercase							:      Any			=					torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
			_lowercase							:      Any			=					convert_outputs_to_fpaa(model.forward					)
			_lowercase							:      Union[str, Any]			=					torch.compile(model.forward							,backend="""inductor"""					)
			_lowercase							:      Dict			=					torch.randn(4							,10					).cuda()
			_lowercase							:      str			=					model(UpperCAmelCase_					)






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

			self.assertEqual(UpperCAmelCase_							,UpperCAmelCase_					)






	@require_torch_min_version(version="""2.0"""					)
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Any			=					RegressionModel()
			_lowercase							:      str			=					torch.compile(UpperCAmelCase_					)

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase							:      Optional[int]			=					torch.nn.parallel.DataParallel(UpperCAmelCase_					)
			_lowercase							:      Any			=					torch.compile(UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					extract_model_from_parallel(UpperCAmelCase_					)

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






	def 					lowerCamelCase__ (			self					):
			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
lowercase__		    =	False
lowercase__		    =	Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowercase__		    =	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, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       List[str]=sys.argv[1:]	)		->       str:






				"""simple docstring"""







				snake_case   :    List[str]			       =		None
				snake_case   :    Tuple			       =		None
				snake_case   :    List[Any]			       =		None
				snake_case   :    Tuple			       =		1
				snake_case       ,snake_case   :    List[Any]			       =		getopt.getopt(UpperCamelCase__   ,     '''i:o:b:s'''   ,     ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
				for opt, arg in opts:
						if opt in ("-i", "--inputdir"):
								snake_case   :    Any			       =		arg
						elif opt in ("-o", "--outfile"):
								snake_case   :    str			       =		arg
						elif opt in ("-b", "--batch_size"):
								snake_case   :    Optional[Any]			       =		int(UpperCamelCase__	)
						elif opt in ("-s", "--subset_list"):
								snake_case   :    List[str]			       =		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:
								snake_case   :    Optional[Any]			       =		{self._vqa_file_split()[0] for x in tryload(UpperCamelCase__	)}
				else:
						snake_case   :    Union[str, Any]			       =		None

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



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[str]	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    str			       =		int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
				snake_case   :    Optional[Any]			       =		os.path.join(self.inputdir   ,     UpperCamelCase__	)
				return (img_id, filepath)



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, 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(UpperCamelCase__	)	)
						if len(UpperCamelCase__	) == self.batch:
								snake_case   :    Optional[int]			       =		batch
								snake_case   :    int			       =		[]
								yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)

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



		def __call__(	self							:       Dict	)		->       str:






				"""simple docstring"""







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







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


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


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


