
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     ,   a		:Optional[Any]     ,   a		:List[Any]=7     ,   a		:int=3     ,   a		:Union[str, Any]=3_0     ,   a		:Tuple=4_0_0     ,   a		:int=True     ,   a		:Union[str, Any]=None     ,   a		:Dict=True     ,   a		:Optional[int]=[0.5, 0.5, 0.5]     ,   a		:int=[0.5, 0.5, 0.5]     ,   a		:List[str]=True     ,   a		:Optional[int]=1 / 2_5_5     ,   a		:Dict=True     ,   )		-> Any:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								__UpperCamelCase	:   str			   =					size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
								__UpperCamelCase	:   List[str]			   =					parent
								__UpperCamelCase	:   Optional[Any]			   =					batch_size
								__UpperCamelCase	:   Any			   =					num_channels
								__UpperCamelCase	:   int			   =					min_resolution
								__UpperCamelCase	:   List[Any]			   =					max_resolution
								__UpperCamelCase	:   Optional[int]			   =					do_resize
								__UpperCamelCase	:   Tuple			   =					size
								__UpperCamelCase	:   int			   =					do_normalize
								__UpperCamelCase	:   Tuple			   =					image_mean
								__UpperCamelCase	:   List[str]			   =					image_std
								__UpperCamelCase	:   Dict			   =					do_rescale
								__UpperCamelCase	:   Optional[int]			   =					rescale_factor
								__UpperCamelCase	:   Any			   =					do_pad







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

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

								return expected_height, expected_width




@require_torch
@require_vision
class  lowerCamelCase__							(				lowerCamelCase_     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     YolosImageProcessor if is_vision_available() else None







	def    _lowerCamelCase				(					self		:Dict						)		-> Any:
								__UpperCamelCase	:   Tuple			   =					YolosImageProcessingTester(self						)







	@property
	def    _lowerCamelCase				(					self		:int						)		-> Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								__UpperCamelCase	:   Optional[Any]			   =					self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(__snake_case     ,   "image_mean"						)						)
								self.assertTrue(hasattr(__snake_case     ,   "image_std"						)						)
								self.assertTrue(hasattr(__snake_case     ,   "do_normalize"						)						)
								self.assertTrue(hasattr(__snake_case     ,   "do_resize"						)						)
								self.assertTrue(hasattr(__snake_case     ,   "size"						)						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> str:
								__UpperCamelCase	:   Union[str, Any]			   =					self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size     ,   {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}						)
								self.assertEqual(image_processor.do_pad     ,   __snake_case						)

								__UpperCamelCase	:   Union[str, Any]			   =					self.image_processing_class.from_dict(
								    self.image_processor_dict     ,   size=4_2     ,   max_size=8_4     ,   pad_and_return_pixel_mask=__snake_case						)
								self.assertEqual(image_processor.size     ,   {"shortest_edge": 4_2, "longest_edge": 8_4}						)
								self.assertEqual(image_processor.do_pad     ,   __snake_case						)







	def    _lowerCamelCase				(					self		:str						)		-> List[str]:
								pass







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Any:
								# Initialize image_processing
								__UpperCamelCase	:   Tuple			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								__UpperCamelCase	:   List[Any]			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case						)
								for image in image_inputs:
															self.assertIsInstance(__snake_case     ,   Image.Image						)

								# 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(__snake_case						)

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

								# Test batched
								__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case						)

								__UpperCamelCase	:   str			   =					image_processing(__snake_case     ,   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    _lowerCamelCase				(					self		:Optional[int]						)		-> Dict:
								# Initialize image_processing
								__UpperCamelCase	:   Any			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								__UpperCamelCase	:   Optional[int]			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   numpify=__snake_case						)
								for image in image_inputs:
															self.assertIsInstance(__snake_case     ,   np.ndarray						)

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

								__UpperCamelCase   ,						__UpperCamelCase	:   Dict			   =					self.image_processor_tester.get_expected_values(__snake_case						)

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

								# Test batched
								__UpperCamelCase	:   List[Any]			   =					image_processing(__snake_case     ,   return_tensors="pt"						).pixel_values

								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case						)

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







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> int:
								# Initialize image_processing
								__UpperCamelCase	:   int			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								__UpperCamelCase	:   Any			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case						)
								for image in image_inputs:
															self.assertIsInstance(__snake_case     ,   torch.Tensor						)

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

								__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					self.image_processor_tester.get_expected_values(__snake_case						)

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

								# Test batched
								__UpperCamelCase	:   List[Any]			   =					image_processing(__snake_case     ,   return_tensors="pt"						).pixel_values

								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case						)

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







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Dict:
								# Initialize image_processings
								__UpperCamelCase	:   Optional[Any]			   =					self.image_processing_class(**self.image_processor_dict						)
								__UpperCamelCase	:   str			   =					self.image_processing_class(do_resize=__snake_case     ,   do_normalize=__snake_case     ,   do_rescale=__snake_case						)
								# create random PyTorch tensors
								__UpperCamelCase	:   Union[str, Any]			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case						)
								for image in image_inputs:
															self.assertIsInstance(__snake_case     ,   torch.Tensor						)

								# Test whether the method "pad" and calling the image processor return the same tensors
								__UpperCamelCase	:   List[str]			   =					image_processing_a.pad(__snake_case     ,   return_tensors="pt"						)
								__UpperCamelCase	:   Tuple			   =					image_processing_a(__snake_case     ,   return_tensors="pt"						)

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







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

								__UpperCamelCase	:   Optional[Any]			   =					{"image_id": 3_9_7_6_9, "annotations": target}

								# encode them
								__UpperCamelCase	:   Optional[Any]			   =					YolosImageProcessor.from_pretrained("hustvl/yolos-small"						)
								__UpperCamelCase	:   Dict			   =					image_processing(images=__snake_case     ,   annotations=__snake_case     ,   return_tensors="pt"						)

								# verify pixel values
								__UpperCamelCase	:   List[str]			   =					torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
								self.assertEqual(encoding["pixel_values"].shape     ,   __snake_case						)

								__UpperCamelCase	:   Union[str, Any]			   =					torch.tensor([0.2796, 0.3138, 0.3481]						)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4						)						)

								# verify area
								__UpperCamelCase	:   List[str]			   =					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"]     ,   __snake_case						)						)
								# verify boxes
								__UpperCamelCase	:   Optional[Any]			   =					torch.Size([6, 4]						)
								self.assertEqual(encoding["labels"][0]["boxes"].shape     ,   __snake_case						)
								__UpperCamelCase	:   Union[str, Any]			   =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,   __snake_case     ,   atol=1E-3						)						)
								# verify image_id
								__UpperCamelCase	:   int			   =					torch.tensor([3_9_7_6_9]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,   __snake_case						)						)
								# verify is_crowd
								__UpperCamelCase	:   List[Any]			   =					torch.tensor([0, 0, 0, 0, 0, 0]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,   __snake_case						)						)
								# verify class_labels
								__UpperCamelCase	:   Optional[Any]			   =					torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,   __snake_case						)						)
								# verify orig_size
								__UpperCamelCase	:   Optional[Any]			   =					torch.tensor([4_8_0, 6_4_0]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,   __snake_case						)						)
								# verify size
								__UpperCamelCase	:   Any			   =					torch.tensor([8_0_0, 1_0_6_6]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,   __snake_case						)						)







	@slow
	def    _lowerCamelCase				(					self		:str						)		-> Tuple:
								# prepare image, target and masks_path
								__UpperCamelCase	:   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:
															__UpperCamelCase	:   Optional[Any]			   =					json.loads(f.read()						)

								__UpperCamelCase	:   List[str]			   =					{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

								__UpperCamelCase	:   str			   =					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

								# encode them
								__UpperCamelCase	:   Tuple			   =					YolosImageProcessor(format="coco_panoptic"						)
								__UpperCamelCase	:   List[Any]			   =					image_processing(images=__snake_case     ,   annotations=__snake_case     ,   masks_path=__snake_case     ,   return_tensors="pt"						)

								# verify pixel values
								__UpperCamelCase	:   Dict			   =					torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
								self.assertEqual(encoding["pixel_values"].shape     ,   __snake_case						)

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

								# verify area
								__UpperCamelCase	:   List[str]			   =					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"]     ,   __snake_case						)						)
								# verify boxes
								__UpperCamelCase	:   Union[str, Any]			   =					torch.Size([6, 4]						)
								self.assertEqual(encoding["labels"][0]["boxes"].shape     ,   __snake_case						)
								__UpperCamelCase	:   List[Any]			   =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,   __snake_case     ,   atol=1E-3						)						)
								# verify image_id
								__UpperCamelCase	:   Tuple			   =					torch.tensor([3_9_7_6_9]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,   __snake_case						)						)
								# verify is_crowd
								__UpperCamelCase	:   Any			   =					torch.tensor([0, 0, 0, 0, 0, 0]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,   __snake_case						)						)
								# verify class_labels
								__UpperCamelCase	:   Optional[Any]			   =					torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,   __snake_case						)						)
								# verify masks
								__UpperCamelCase	:   Optional[int]			   =					8_2_2_8_7_3
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,   __snake_case						)
								# verify orig_size
								__UpperCamelCase	:   List[Any]			   =					torch.tensor([4_8_0, 6_4_0]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,   __snake_case						)						)
								# verify size
								__UpperCamelCase	:   str			   =					torch.tensor([8_0_0, 1_0_6_6]						)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,   __snake_case						)						)



from argparse import ArgumentParser

from .env import EnvironmentCommand






def 							__lowercase   ( ):
							a__             =     ArgumentParser('Diffusers CLI tool'					,    usage='diffusers-cli <command> [<args>]'   )
							a__             =     parser.add_subparsers(help='diffusers-cli command helpers'   )

							# Register commands
							EnvironmentCommand.register_subcommand(__lowerCAmelCase   )

							# Let's go
							a__             =     parser.parse_args()

							if not hasattr(__lowerCAmelCase					,    'func'   ):
														parser.print_help()
														exit(1   )

							# Run
							a__             =     args.func(__lowerCAmelCase   )
							service.run()


if __name__ == "__main__":
						main()




