



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 DetrImageProcessor


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=1 / 255 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=[0.5, 0.5, 0.5] ,					__UpperCAmelCase=[0.5, 0.5, 0.5] ,					__UpperCAmelCase=True ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
											a__	:							Optional[int]      						= parent
											a__	:							Dict      						= batch_size
											a__	:							int      						= num_channels
											a__	:							Union[str, Any]      						= min_resolution
											a__	:							Optional[Any]      						= max_resolution
											a__	:							List[str]      						= do_resize
											a__	:							Any      						= size
											a__	:							Tuple      						= do_rescale
											a__	:							str      						= rescale_factor
											a__	:							str      						= do_normalize
											a__	:							Dict      						= image_mean
											a__	:							List[Any]      						= image_std
											a__	:							List[str]      						= do_pad



							def 		_A	(							self				):







											"""simple docstring"""



											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=False				):







											"""simple docstring"""



											if not batched:
															a__	:							List[str]      						= image_inputs[0]
															if isinstance(__UpperCAmelCase ,					Image.Image				):
																			a__						,   a__	:							Dict      						= image.size
															else:
																			a__						,   a__	:							int      						= image.shape[1], image.shape[2]
															if w < h:
																			a__	:							List[str]      						= int(self.size["shortest_edge"] * h / w				)
																			a__	:							Optional[Any]      						= self.size["shortest_edge"]
															elif w > h:
																			a__	:							Optional[int]      						= self.size["shortest_edge"]
																			a__	:							Optional[int]      						= int(self.size["shortest_edge"] * w / h				)
															else:
																			a__	:							Any      						= self.size["shortest_edge"]
																			a__	:							Optional[int]      						= self.size["shortest_edge"]

											else:
															a__	:							Dict      						= []
															for image in image_inputs:
																			a__						,   a__	:							Optional[Any]      						= self.get_expected_values([image]				)
																			expected_values.append((expected_height, expected_width)				)
															a__	:							Any      						= max(__UpperCAmelCase ,					key=lambda __UpperCAmelCase				: item[0]				)[0]
															a__	:							Optional[int]      						= max(__UpperCAmelCase ,					key=lambda __UpperCAmelCase				: item[1]				)[1]

											return expected_height, expected_width





@require_torch
@require_vision
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       DetrImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= DetrImageProcessingTester(self				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_mean"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_std"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_rescale"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"rescale_factor"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_resize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"size"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_pad"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class.from_dict(self.image_processor_dict				)
											self.assertEqual(image_processor.size ,					{"shortest_edge": 18, "longest_edge": 1333}				)
											self.assertEqual(image_processor.do_pad ,					__UpperCAmelCase				)

											a__	:							Tuple      						= self.image_processing_class.from_dict(
											    self.image_processor_dict ,					size=42 ,					max_size=84 ,					pad_and_return_pixel_mask=__UpperCAmelCase				)
											self.assertEqual(image_processor.size ,					{"shortest_edge": 42, "longest_edge": 84}				)
											self.assertEqual(image_processor.do_pad ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											pass



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Tuple      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

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

											a__						,   a__	:							Union[str, Any]      						= self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

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

											# Test batched
											a__						,   a__	:							int      						= self.image_processor_tester.get_expected_values(__UpperCAmelCase ,					batched=__UpperCAmelCase				)

											a__	:							Dict      						= image_processing(__UpperCAmelCase ,					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				):







											"""simple docstring"""



											a__	:							Dict      						= self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											a__	:							Tuple      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase ,					numpify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					np.ndarray				)

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

											a__						,   a__	:							Dict      						= self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

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

											# Test batched
											a__	:							Optional[int]      						= image_processing(__UpperCAmelCase ,					return_tensors="pt"				).pixel_values

											a__						,   a__	:							Any      						= self.image_processor_tester.get_expected_values(__UpperCAmelCase ,					batched=__UpperCAmelCase				)

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



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											a__	:							List[Any]      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase ,					torchify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					torch.Tensor				)

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

											a__						,   a__	:							Optional[Any]      						= self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

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

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

											a__						,   a__	:							Union[str, Any]      						= self.image_processor_tester.get_expected_values(__UpperCAmelCase ,					batched=__UpperCAmelCase				)

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



							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
											with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,					"r"				) as f:
															a__	:							Any      						= json.loads(f.read()				)

											a__	:							Dict      						= {"image_id": 3_9769, "annotations": target}

											# encode them
											a__	:							str      						= DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"				)
											a__	:							List[Any]      						= image_processing(images=__UpperCAmelCase ,					annotations=__UpperCAmelCase ,					return_tensors="pt"				)

											# verify pixel values
											a__	:							str      						= torch.Size([1, 3, 800, 1066]				)
											self.assertEqual(encoding["pixel_values"].shape ,					__UpperCAmelCase				)

											a__	:							Dict      						= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,					__UpperCAmelCase ,					atol=1E-4				)				)

											# verify area
											a__	:							Union[str, Any]      						= torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,					__UpperCAmelCase				)				)
											# verify boxes
											a__	:							Any      						= torch.Size([6, 4]				)
											self.assertEqual(encoding["labels"][0]["boxes"].shape ,					__UpperCAmelCase				)
											a__	:							Optional[Any]      						= torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,					__UpperCAmelCase ,					atol=1E-3				)				)
											# verify image_id
											a__	:							Optional[int]      						= torch.tensor([3_9769]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,					__UpperCAmelCase				)				)
											# verify is_crowd
											a__	:							int      						= torch.tensor([0, 0, 0, 0, 0, 0]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,					__UpperCAmelCase				)				)
											# verify class_labels
											a__	:							Tuple      						= torch.tensor([75, 75, 63, 65, 17, 17]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,					__UpperCAmelCase				)				)
											# verify orig_size
											a__	:							Optional[Any]      						= torch.tensor([480, 640]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,					__UpperCAmelCase				)				)
											# verify size
											a__	:							Dict      						= torch.tensor([800, 1066]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,					__UpperCAmelCase				)				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
											with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,					"r"				) as f:
															a__	:							int      						= json.loads(f.read()				)

											a__	:							List[str]      						= {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

											# encode them
											a__	:							str      						= DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"				)
											a__	:							Any      						= image_processing(images=__UpperCAmelCase ,					annotations=__UpperCAmelCase ,					masks_path=__UpperCAmelCase ,					return_tensors="pt"				)

											# verify pixel values
											a__	:							Optional[int]      						= torch.Size([1, 3, 800, 1066]				)
											self.assertEqual(encoding["pixel_values"].shape ,					__UpperCAmelCase				)

											a__	:							Dict      						= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,					__UpperCAmelCase ,					atol=1E-4				)				)

											# verify area
											a__	:							Tuple      						= torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,					__UpperCAmelCase				)				)
											# verify boxes
											a__	:							Dict      						= torch.Size([6, 4]				)
											self.assertEqual(encoding["labels"][0]["boxes"].shape ,					__UpperCAmelCase				)
											a__	:							int      						= torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,					__UpperCAmelCase ,					atol=1E-3				)				)
											# verify image_id
											a__	:							Optional[int]      						= torch.tensor([3_9769]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,					__UpperCAmelCase				)				)
											# verify is_crowd
											a__	:							Any      						= torch.tensor([0, 0, 0, 0, 0, 0]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,					__UpperCAmelCase				)				)
											# verify class_labels
											a__	:							Optional[Any]      						= torch.tensor([17, 17, 63, 75, 75, 93]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,					__UpperCAmelCase				)				)
											# verify masks
											a__	:							int      						= 82_2873
											self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,					__UpperCAmelCase				)
											# verify orig_size
											a__	:							Union[str, Any]      						= torch.tensor([480, 640]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,					__UpperCAmelCase				)				)
											# verify size
											a__	:							Any      						= torch.tensor([800, 1066]				)
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,					__UpperCAmelCase				)				)







import operator as op


lowerCamelCase		       =      """scaler.pt"""
lowerCamelCase		       =      """pytorch_model"""
lowerCamelCase		       =      """random_states"""
lowerCamelCase		       =      """optimizer"""
lowerCamelCase		       =      """scheduler"""
lowerCamelCase		       =      """pytorch_model.bin"""
lowerCamelCase		       =      """pytorch_model.bin.index.json"""
lowerCamelCase		       =      """model.safetensors"""
lowerCamelCase		       =      """model.safetensors.index.json"""
lowerCamelCase		       =      """1.10.2"""
lowerCamelCase		       =      """py38"""
lowerCamelCase		       =      """4.17.0"""
lowerCamelCase		       =      ["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
lowerCamelCase		       =      ["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
lowerCamelCase		       =      ["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
lowerCamelCase		       =      ["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
lowerCamelCase		       =      ["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
lowerCamelCase		       =      """2.0.1"""
lowerCamelCase		       =      ["""pdsh""", """standard""", """openmpi""", """mvapich"""]
lowerCamelCase		       =      ["""default""", """reduce-overhead""", """max-autotune"""]

lowerCamelCase		       =      {""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowerCamelCase		       =      [
    """nnodes""",
    """nproc_per_node""",
    """rdzv_backend""",
    """rdzv_endpoint""",
    """rdzv_id""",
    """rdzv_conf""",
    """standalone""",
    """max_restarts""",
    """monitor_interval""",
    """start_method""",
    """role""",
    """module""",
    """m""",
    """no_python""",
    """run_path""",
    """log_dir""",
    """r""",
    """redirects""",
    """t""",
    """tee""",
    """node_rank""",
    """master_addr""",
    """master_port""",
]

lowerCamelCase		       =      ["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
lowerCamelCase		       =      ["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]


