



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 DeformableDetrImageProcessor







class 							_a							(					unittest.TestCase ):

			def __init__(self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=7,					SCREAMING_SNAKE_CASE_=3,					SCREAMING_SNAKE_CASE_=30,					SCREAMING_SNAKE_CASE_=400,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],					SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=1 / 255,					SCREAMING_SNAKE_CASE_=True,					)    ->					Optional[int]:
						# 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""": 18, """longest_edge""": 1333}
						UpperCAmelCase_:						List[Any]					=			parent
						UpperCAmelCase_:						Dict					=			batch_size
						UpperCAmelCase_:						List[Any]					=			num_channels
						UpperCAmelCase_:						Optional[int]					=			min_resolution
						UpperCAmelCase_:						int					=			max_resolution
						UpperCAmelCase_:						str					=			do_resize
						UpperCAmelCase_:						Optional[int]					=			size
						UpperCAmelCase_:						Dict					=			do_normalize
						UpperCAmelCase_:						Tuple					=			image_mean
						UpperCAmelCase_:						Any					=			image_std
						UpperCAmelCase_:						Optional[int]					=			do_rescale
						UpperCAmelCase_:						List[Any]					=			rescale_factor
						UpperCAmelCase_:						Optional[int]					=			do_pad

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

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

						return expected_height, expected_width







@require_torch
@require_vision
class 							_a							(					__a      ,						unittest.TestCase ):
			A							=     DeformableDetrImageProcessor if is_vision_available() else None

			def 			__snake_case    (self )    ->					Dict:
						UpperCAmelCase_:						int					=			DeformableDetrImageProcessingTester(self )

			@property
			def 			__snake_case    (self )    ->					List[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def 			__snake_case    (self )    ->					List[Any]:
						UpperCAmelCase_:						Union[str, 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_resize""" ) )
						self.assertTrue(hasattr(_UpperCamelCase,					"""do_rescale""" ) )
						self.assertTrue(hasattr(_UpperCamelCase,					"""do_pad""" ) )
						self.assertTrue(hasattr(_UpperCamelCase,					"""size""" ) )

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						List[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 )

						UpperCAmelCase_:						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 			__snake_case    (self )    ->					Union[str, Any]:
						pass

			def 			__snake_case    (self )    ->					Optional[Any]:
						# Initialize image_processing
						UpperCAmelCase_:						str					=			self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						UpperCAmelCase_:						int					=			prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCamelCase )
						for image in image_inputs:
									self.assertIsInstance(_UpperCamelCase,					Image.Image )

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

						UpperCAmelCase_	,				UpperCAmelCase_:						Tuple					=			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
						UpperCAmelCase_	,				UpperCAmelCase_:						Any					=			self.image_processor_tester.get_expected_values(_UpperCamelCase,					batched=_UpperCamelCase )

						UpperCAmelCase_:						int					=			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 			__snake_case    (self )    ->					List[Any]:
						# Initialize image_processing
						UpperCAmelCase_:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						UpperCAmelCase_:						int					=			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
						UpperCAmelCase_:						Tuple					=			image_processing(image_inputs[0],					return_tensors="""pt""" ).pixel_values

						UpperCAmelCase_	,				UpperCAmelCase_:						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
						UpperCAmelCase_:						Union[str, Any]					=			image_processing(_UpperCamelCase,					return_tensors="""pt""" ).pixel_values

						UpperCAmelCase_	,				UpperCAmelCase_:						Dict					=			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 			__snake_case    (self )    ->					Tuple:
						# Initialize image_processing
						UpperCAmelCase_:						Union[str, Any]					=			self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						UpperCAmelCase_:						str					=			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
						UpperCAmelCase_:						Tuple					=			image_processing(image_inputs[0],					return_tensors="""pt""" ).pixel_values

						UpperCAmelCase_	,				UpperCAmelCase_:						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
						UpperCAmelCase_:						List[Any]					=			image_processing(_UpperCamelCase,					return_tensors="""pt""" ).pixel_values

						UpperCAmelCase_	,				UpperCAmelCase_:						Optional[int]					=			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 			__snake_case    (self )    ->					Dict:
						# prepare image and target
						UpperCAmelCase_:						int					=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
						with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""",					"""r""" ) as f:
									UpperCAmelCase_:						int					=			json.loads(f.read() )

						UpperCAmelCase_:						int					=			{"""image_id""": 39769, """annotations""": target}

						# encode them
						UpperCAmelCase_:						str					=			DeformableDetrImageProcessor()
						UpperCAmelCase_:						int					=			image_processing(images=_UpperCamelCase,					annotations=_UpperCamelCase,					return_tensors="""pt""" )

						# verify pixel values
						UpperCAmelCase_:						List[Any]					=			torch.Size([1, 3, 800, 1066] )
						self.assertEqual(encoding["""pixel_values"""].shape,					_UpperCamelCase )

						UpperCAmelCase_:						List[str]					=			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
						UpperCAmelCase_:						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"""],					_UpperCamelCase ) )
						# verify boxes
						UpperCAmelCase_:						int					=			torch.Size([6, 4] )
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,					_UpperCamelCase )
						UpperCAmelCase_:						Union[str, 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
						UpperCAmelCase_:						Union[str, Any]					=			torch.tensor([39769] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],					_UpperCamelCase ) )
						# verify is_crowd
						UpperCAmelCase_:						Optional[int]					=			torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],					_UpperCamelCase ) )
						# verify class_labels
						UpperCAmelCase_:						Any					=			torch.tensor([75, 75, 63, 65, 17, 17] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],					_UpperCamelCase ) )
						# verify orig_size
						UpperCAmelCase_:						Tuple					=			torch.tensor([480, 640] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],					_UpperCamelCase ) )
						# verify size
						UpperCAmelCase_:						List[Any]					=			torch.tensor([800, 1066] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],					_UpperCamelCase ) )

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

						UpperCAmelCase_:						int					=			{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

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

						# encode them
						UpperCAmelCase_:						str					=			DeformableDetrImageProcessor(format="""coco_panoptic""" )
						UpperCAmelCase_:						Optional[Any]					=			image_processing(images=_UpperCamelCase,					annotations=_UpperCamelCase,					masks_path=_UpperCamelCase,					return_tensors="""pt""" )

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

						UpperCAmelCase_:						Any					=			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
						UpperCAmelCase_:						List[Any]					=			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
						UpperCAmelCase_:						Optional[int]					=			torch.Size([6, 4] )
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,					_UpperCamelCase )
						UpperCAmelCase_:						Tuple					=			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
						UpperCAmelCase_:						List[Any]					=			torch.tensor([39769] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],					_UpperCamelCase ) )
						# verify is_crowd
						UpperCAmelCase_:						Union[str, Any]					=			torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],					_UpperCamelCase ) )
						# verify class_labels
						UpperCAmelCase_:						Optional[Any]					=			torch.tensor([17, 17, 63, 75, 75, 93] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],					_UpperCamelCase ) )
						# verify masks
						UpperCAmelCase_:						Dict					=			822873
						self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item(),					_UpperCamelCase )
						# verify orig_size
						UpperCAmelCase_:						Union[str, Any]					=			torch.tensor([480, 640] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],					_UpperCamelCase ) )
						# verify size
						UpperCAmelCase_:						Any					=			torch.tensor([800, 1066] )
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],					_UpperCamelCase ) )








from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	ArgumentParser("Transformers CLI tool"    ,							usage="transformers-cli <command> [<args>]"				)
			lowerCAmelCase_						=	parser.add_subparsers(help="transformers-cli command helpers"				)

			# Register commands
			ConvertCommand.register_subcommand(__lowerCAmelCase				)
			DownloadCommand.register_subcommand(__lowerCAmelCase				)
			EnvironmentCommand.register_subcommand(__lowerCAmelCase				)
			RunCommand.register_subcommand(__lowerCAmelCase				)
			ServeCommand.register_subcommand(__lowerCAmelCase				)
			UserCommands.register_subcommand(__lowerCAmelCase				)
			AddNewModelCommand.register_subcommand(__lowerCAmelCase				)
			AddNewModelLikeCommand.register_subcommand(__lowerCAmelCase				)
			LfsCommands.register_subcommand(__lowerCAmelCase				)
			PTtoTFCommand.register_subcommand(__lowerCAmelCase				)

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

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

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


if __name__ == "__main__":
			main()



