






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import YolosImageProcessor

class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				def __init__( self     :int  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :List[str]=7  ,     lowerCamelCase__     :str=3  ,     lowerCamelCase__     :int=30  ,     lowerCamelCase__     :Tuple=4_00  ,     lowerCamelCase__     :Dict=True  ,     lowerCamelCase__     :List[str]=None  ,     lowerCamelCase__     :List[str]=True  ,     lowerCamelCase__     :List[Any]=[0.5, 0.5, 0.5]  ,     lowerCamelCase__     :Tuple=[0.5, 0.5, 0.5]  ,     lowerCamelCase__     :int=True  ,     lowerCamelCase__     :Tuple=1 / 2_55  ,     lowerCamelCase__     :Optional[Any]=True  ,     ):
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					UpperCamelCase__ :Dict						=      size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
					UpperCamelCase__ :List[str]						=      parent
					UpperCamelCase__ :List[str]						=      batch_size
					UpperCamelCase__ :Dict						=      num_channels
					UpperCamelCase__ :Optional[int]						=      min_resolution
					UpperCamelCase__ :str						=      max_resolution
					UpperCamelCase__ :Optional[int]						=      do_resize
					UpperCamelCase__ :List[Any]						=      size
					UpperCamelCase__ :Union[str, Any]						=      do_normalize
					UpperCamelCase__ :str						=      image_mean
					UpperCamelCase__ :Any						=      image_std
					UpperCamelCase__ :int						=      do_rescale
					UpperCamelCase__ :Any						=      rescale_factor
					UpperCamelCase__ :List[str]						=      do_pad







				def 				__a   ( self     :Any     ):
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :List[Any]=False     ):
					if not batched:
						UpperCamelCase__ :Union[str, Any]						=      image_inputs[0]
						if isinstance(lowerCAmelCase__  ,     Image.Image     ):
							UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      image.size
						else:
							UpperCamelCase__				,		UpperCamelCase__ :Dict						=      image.shape[1], image.shape[2]
						if w < h:
							UpperCamelCase__ :Tuple						=      int(self.size["""shortest_edge"""] * h / w     )
							UpperCamelCase__ :int						=      self.size["""shortest_edge"""]
						elif w > h:
							UpperCamelCase__ :Optional[Any]						=      self.size["""shortest_edge"""]
							UpperCamelCase__ :Union[str, Any]						=      int(self.size["""shortest_edge"""] * w / h     )
						else:
							UpperCamelCase__ :Dict						=      self.size["""shortest_edge"""]
							UpperCamelCase__ :Dict						=      self.size["""shortest_edge"""]

					else:
						UpperCamelCase__ :Tuple						=      []
						for image in image_inputs:
							UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      self.get_expected_values([image]     )
							expected_values.append((expected_height, expected_width)     )
						UpperCamelCase__ :Optional[Any]						=      max(lowerCAmelCase__  ,     key=lambda lowerCamelCase__     : item[0]     )[0]
						UpperCamelCase__ :str						=      max(lowerCAmelCase__  ,     key=lambda lowerCamelCase__     : item[1]     )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class        lowerCAmelCase_				(      __a						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Tuple        =			YolosImageProcessor if is_vision_available() else None







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Any						=      YolosImageProcessingTester(self     )







				@property
				def 				__a   ( self     :List[Any]     ):
					return self.image_processor_tester.prepare_image_processor_dict()







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :int						=      self.image_processing_class(**self.image_processor_dict     )
					self.assertTrue(hasattr(lowerCAmelCase__  ,     """image_mean"""     )     )
					self.assertTrue(hasattr(lowerCAmelCase__  ,     """image_std"""     )     )
					self.assertTrue(hasattr(lowerCAmelCase__  ,     """do_normalize"""     )     )
					self.assertTrue(hasattr(lowerCAmelCase__  ,     """do_resize"""     )     )
					self.assertTrue(hasattr(lowerCAmelCase__  ,     """size"""     )     )







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :int						=      self.image_processing_class.from_dict(self.image_processor_dict     )
					self.assertEqual(image_processor.size  ,     {"""shortest_edge""": 18, """longest_edge""": 13_33}     )
					self.assertEqual(image_processor.do_pad  ,     lowerCAmelCase__     )

					UpperCamelCase__ :Optional[Any]						=      self.image_processing_class.from_dict(
					    self.image_processor_dict  ,     size=42  ,     max_size=84  ,     pad_and_return_pixel_mask=lowerCAmelCase__     )
					self.assertEqual(image_processor.size  ,     {"""shortest_edge""": 42, """longest_edge""": 84}     )
					self.assertEqual(image_processor.do_pad  ,     lowerCAmelCase__     )







				def 				__a   ( self     :str     ):
					pass







				def 				__a   ( self     :str     ):
					# Initialize image_processing
					UpperCamelCase__ :Dict						=      self.image_processing_class(**self.image_processor_dict     )
					# create random PIL images
					UpperCamelCase__ :List[Any]						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=lowerCAmelCase__     )
					for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__  ,     Image.Image     )

					# Test not batched input
					UpperCamelCase__ :Optional[int]						=      image_processing(image_inputs[0]  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :List[str]						=      self.image_processor_tester.get_expected_values(lowerCAmelCase__     )

					self.assertEqual(
					    encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

					# Test batched
					UpperCamelCase__				,		UpperCamelCase__ :Dict						=      self.image_processor_tester.get_expected_values(lowerCAmelCase__  ,     batched=lowerCAmelCase__     )

					UpperCamelCase__ :str						=      image_processing(lowerCAmelCase__  ,     return_tensors="""pt"""     ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,     )







				def 				__a   ( self     :List[Any]     ):
					# Initialize image_processing
					UpperCamelCase__ :Optional[Any]						=      self.image_processing_class(**self.image_processor_dict     )
					# create random numpy tensors
					UpperCamelCase__ :List[Any]						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=lowerCAmelCase__  ,     numpify=lowerCAmelCase__     )
					for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__  ,     np.ndarray     )

					# Test not batched input
					UpperCamelCase__ :Optional[int]						=      image_processing(image_inputs[0]  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      self.image_processor_tester.get_expected_values(lowerCAmelCase__     )

					self.assertEqual(
					    encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

					# Test batched
					UpperCamelCase__ :Optional[Any]						=      image_processing(lowerCAmelCase__  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      self.image_processor_tester.get_expected_values(lowerCAmelCase__  ,     batched=lowerCAmelCase__     )

					self.assertEqual(
					    encoded_images.shape  ,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,     )







				def 				__a   ( self     :Tuple     ):
					# Initialize image_processing
					UpperCamelCase__ :List[str]						=      self.image_processing_class(**self.image_processor_dict     )
					# create random PyTorch tensors
					UpperCamelCase__ :Tuple						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=lowerCAmelCase__  ,     torchify=lowerCAmelCase__     )
					for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__  ,     torch.Tensor     )

					# Test not batched input
					UpperCamelCase__ :Union[str, Any]						=      image_processing(image_inputs[0]  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :Any						=      self.image_processor_tester.get_expected_values(lowerCAmelCase__     )

					self.assertEqual(
					    encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

					# Test batched
					UpperCamelCase__ :Union[str, Any]						=      image_processing(lowerCAmelCase__  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :Any						=      self.image_processor_tester.get_expected_values(lowerCAmelCase__  ,     batched=lowerCAmelCase__     )

					self.assertEqual(
					    encoded_images.shape  ,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,     )







				def 				__a   ( self     :Tuple     ):
					# Initialize image_processings
					UpperCamelCase__ :int						=      self.image_processing_class(**self.image_processor_dict     )
					UpperCamelCase__ :Optional[int]						=      self.image_processing_class(do_resize=lowerCAmelCase__  ,     do_normalize=lowerCAmelCase__  ,     do_rescale=lowerCAmelCase__     )
					# create random PyTorch tensors
					UpperCamelCase__ :Any						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=lowerCAmelCase__  ,     torchify=lowerCAmelCase__     )
					for image in image_inputs:
						self.assertIsInstance(lowerCAmelCase__  ,     torch.Tensor     )

					# Test whether the method "pad" and calling the image processor return the same tensors
					UpperCamelCase__ :Optional[Any]						=      image_processing_a.pad(lowerCAmelCase__  ,     return_tensors="""pt"""     )
					UpperCamelCase__ :Any						=      image_processing_a(lowerCAmelCase__  ,     return_tensors="""pt"""     )

					self.assertTrue(
					    torch.allclose(encoded_images_with_method["""pixel_values"""]  ,     encoded_images["""pixel_values"""]  ,     atol=1e-4     )     )







				@slow
				def 				__a   ( self     :str     ):
					# prepare image and target
					UpperCamelCase__ :Union[str, Any]						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
					with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""  ,     """r"""     ) as f:
						UpperCamelCase__ :Any						=      json.loads(f.read()     )

					UpperCamelCase__ :Optional[Any]						=      {"""image_id""": 3_97_69, """annotations""": target}

					# encode them
					UpperCamelCase__ :Any						=      YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""     )
					UpperCamelCase__ :List[str]						=      image_processing(images=lowerCAmelCase__  ,     annotations=lowerCAmelCase__  ,     return_tensors="""pt"""     )

					# verify pixel values
					UpperCamelCase__ :Any						=      torch.Size([1, 3, 8_00, 10_66]     )
					self.assertEqual(encoding["""pixel_values"""].shape  ,     lowerCAmelCase__     )

					UpperCamelCase__ :Optional[Any]						=      torch.tensor([0.2796, 0.3138, 0.3481]     )
					self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,     lowerCAmelCase__  ,     atol=1e-4     )     )

					# verify area
					UpperCamelCase__ :Tuple						=      torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,     lowerCAmelCase__     )     )
					# verify boxes
					UpperCamelCase__ :Any						=      torch.Size([6, 4]     )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,     lowerCAmelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,     lowerCAmelCase__  ,     atol=1e-3     )     )
					# verify image_id
					UpperCamelCase__ :Union[str, Any]						=      torch.tensor([3_97_69]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,     lowerCAmelCase__     )     )
					# verify is_crowd
					UpperCamelCase__ :Optional[Any]						=      torch.tensor([0, 0, 0, 0, 0, 0]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,     lowerCAmelCase__     )     )
					# verify class_labels
					UpperCamelCase__ :Optional[int]						=      torch.tensor([75, 75, 63, 65, 17, 17]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,     lowerCAmelCase__     )     )
					# verify orig_size
					UpperCamelCase__ :Any						=      torch.tensor([4_80, 6_40]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,     lowerCAmelCase__     )     )
					# verify size
					UpperCamelCase__ :Tuple						=      torch.tensor([8_00, 10_66]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,     lowerCAmelCase__     )     )







				@slow
				def 				__a   ( self     :List[str]     ):
					# prepare image, target and masks_path
					UpperCamelCase__ :Dict						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
					with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""  ,     """r"""     ) as f:
						UpperCamelCase__ :List[Any]						=      json.loads(f.read()     )

					UpperCamelCase__ :Optional[int]						=      {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

					UpperCamelCase__ :List[Any]						=      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""     )

					# encode them
					UpperCamelCase__ :str						=      YolosImageProcessor(format="""coco_panoptic"""     )
					UpperCamelCase__ :Union[str, Any]						=      image_processing(images=lowerCAmelCase__  ,     annotations=lowerCAmelCase__  ,     masks_path=lowerCAmelCase__  ,     return_tensors="""pt"""     )

					# verify pixel values
					UpperCamelCase__ :Dict						=      torch.Size([1, 3, 8_00, 10_66]     )
					self.assertEqual(encoding["""pixel_values"""].shape  ,     lowerCAmelCase__     )

					UpperCamelCase__ :Dict						=      torch.tensor([0.2796, 0.3138, 0.3481]     )
					self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,     lowerCAmelCase__  ,     atol=1e-4     )     )

					# verify area
					UpperCamelCase__ :Union[str, Any]						=      torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,     lowerCAmelCase__     )     )
					# verify boxes
					UpperCamelCase__ :Optional[int]						=      torch.Size([6, 4]     )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,     lowerCAmelCase__     )
					UpperCamelCase__ :Dict						=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,     lowerCAmelCase__  ,     atol=1e-3     )     )
					# verify image_id
					UpperCamelCase__ :List[str]						=      torch.tensor([3_97_69]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,     lowerCAmelCase__     )     )
					# verify is_crowd
					UpperCamelCase__ :int						=      torch.tensor([0, 0, 0, 0, 0, 0]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,     lowerCAmelCase__     )     )
					# verify class_labels
					UpperCamelCase__ :List[str]						=      torch.tensor([17, 17, 63, 75, 75, 93]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,     lowerCAmelCase__     )     )
					# verify masks
					UpperCamelCase__ :Union[str, Any]						=      82_28_73
					self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,     lowerCAmelCase__     )
					# verify orig_size
					UpperCamelCase__ :str						=      torch.tensor([4_80, 6_40]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,     lowerCAmelCase__     )     )
					# verify size
					UpperCamelCase__ :Optional[int]						=      torch.tensor([8_00, 10_66]     )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,     lowerCAmelCase__     )     )

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      ArgumentParser(
				    description=(
				        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
				    )						)

				# Optional arguments for the launch helper
				parser.add_argument("--num_cores"		,    type=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    help=(
				        "The full path to the single TPU training "
				        "program/script to be launched in parallel, "
				        "followed by all the arguments for the "
				        "training script"
				    )		,    )

				# rest from the training program
				parser.add_argument("training_script_args"		,    nargs=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores						)]

				xmp.spawn(mod._mp_fn		,    args=()		,    nprocs=args.num_cores						)


if __name__ == "__main__":
						main()
