

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_pytesseract_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor






class 			A_						(			unittest.TestCase							):
							def __init__(						self						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     str=7  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=3  ,SCREAMING_SNAKE_CASE__						:     Optional[int]=1_8  ,SCREAMING_SNAKE_CASE__						:     Tuple=3_0  ,SCREAMING_SNAKE_CASE__						:     str=4_0_0  ,SCREAMING_SNAKE_CASE__						:     int=True  ,SCREAMING_SNAKE_CASE__						:     Tuple=None  ,SCREAMING_SNAKE_CASE__						:     Any=True  ,):
								__lowerCamelCase							:							List[Any]   			=					size if size is not None else {'height': 1_8, 'width': 1_8}
								__lowerCamelCase							:							Tuple   			=					parent
								__lowerCamelCase							:							List[str]   			=					batch_size
								__lowerCamelCase							:							Union[str, Any]   			=					num_channels
								__lowerCamelCase							:							List[Any]   			=					image_size
								__lowerCamelCase							:							List[Any]   			=					min_resolution
								__lowerCamelCase							:							Optional[Any]   			=					max_resolution
								__lowerCamelCase							:							Any   			=					do_resize
								__lowerCamelCase							:							int   			=					size
								__lowerCamelCase							:							Union[str, Any]   			=					apply_ocr


							def        lowerCAmelCase	(						self						:     Optional[int]):
								return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 			A_						(			_a     ,				unittest.TestCase							):
							_UpperCAmelCase       :    str		 = LayoutLMvaImageProcessor if is_pytesseract_available() else None
							def        lowerCAmelCase	(						self						:     List[Any]):
								__lowerCamelCase							:							List[str]   			=					LayoutLMvaImageProcessingTester(self)
							@property
							def        lowerCAmelCase	(						self						:     Dict):
								return self.image_processor_tester.prepare_image_processor_dict()
							def        lowerCAmelCase	(						self						:     List[Any]):
								__lowerCamelCase							:							Tuple   			=					self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,'do_resize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,'size'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,'apply_ocr'))
							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								__lowerCamelCase							:							str   			=					self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size  ,{'height': 1_8, 'width': 1_8})

								__lowerCamelCase							:							Union[str, Any]   			=					self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2)
								self.assertEqual(image_processor.size  ,{'height': 4_2, 'width': 4_2})
							def        lowerCAmelCase	(						self						:     Tuple):
								pass
							def        lowerCAmelCase	(						self						:     Optional[Any]):
								__lowerCamelCase							:							str   			=					self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase							:							List[Any]   			=					prepare_image_inputs(self.image_processor_tester  ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,Image.Image)

								# Test not batched input
								__lowerCamelCase							:							str   			=					image_processing(image_inputs[0]  ,return_tensors='pt')
								self.assertEqual(
								    encoding.pixel_values.shape  ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )  ,)

								self.assertIsInstance(encoding.words  ,SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(encoding.boxes  ,SCREAMING_SNAKE_CASE__)

								# Test batched
								__lowerCamelCase							:							List[Any]   			=					image_processing(SCREAMING_SNAKE_CASE__  ,return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape  ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )  ,)
							def        lowerCAmelCase	(						self						:     Dict):
								__lowerCamelCase							:							Tuple   			=					self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								__lowerCamelCase							:							Optional[Any]   			=					prepare_image_inputs(self.image_processor_tester  ,equal_resolution=SCREAMING_SNAKE_CASE__  ,numpify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,np.ndarray)

								# Test not batched input
								__lowerCamelCase							:							int   			=					image_processing(image_inputs[0]  ,return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape  ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )  ,)

								# Test batched
								__lowerCamelCase							:							Optional[int]   			=					image_processing(SCREAMING_SNAKE_CASE__  ,return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape  ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )  ,)
							def        lowerCAmelCase	(						self						:     int):
								__lowerCamelCase							:							Dict   			=					self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								__lowerCamelCase							:							Optional[int]   			=					prepare_image_inputs(self.image_processor_tester  ,equal_resolution=SCREAMING_SNAKE_CASE__  ,torchify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,torch.Tensor)

								# Test not batched input
								__lowerCamelCase							:							Optional[Any]   			=					image_processing(image_inputs[0]  ,return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape  ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )  ,)

								# Test batched
								__lowerCamelCase							:							Tuple   			=					image_processing(SCREAMING_SNAKE_CASE__  ,return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape  ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )  ,)


							def        lowerCAmelCase	(						self						:     str):
								__lowerCamelCase							:							int   			=					LayoutLMvaImageProcessor()

								from datasets import load_dataset

								__lowerCamelCase							:							Union[str, Any]   			=					load_dataset('hf-internal-testing/fixtures_docvqa'  ,split='test')

								__lowerCamelCase							:							Union[str, Any]   			=					Image.open(ds[0]['file']).convert('RGB')

								__lowerCamelCase							:							Optional[Any]   			=					image_processing(SCREAMING_SNAKE_CASE__  ,return_tensors='pt')

								self.assertEqual(encoding.pixel_values.shape  ,(1, 3, 2_2_4, 2_2_4))
								self.assertEqual(len(encoding.words)  ,len(encoding.boxes))

								# fmt: off
								# the words and boxes were obtained with Tesseract 4.1.1
								__lowerCamelCase							:							Union[str, Any]   			=					[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
								__lowerCamelCase							:							str   			=					[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
								# fmt: on

								self.assertListEqual(encoding.words  ,SCREAMING_SNAKE_CASE__)
								self.assertListEqual(encoding.boxes  ,SCREAMING_SNAKE_CASE__)

								# with apply_OCR = False
								__lowerCamelCase							:							Union[str, Any]   			=					LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase							:							Tuple   			=					image_processing(SCREAMING_SNAKE_CASE__  ,return_tensors='pt')

								self.assertEqual(encoding.pixel_values.shape  ,(1, 3, 2_2_4, 2_2_4))








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: Optional[Any]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    # initialize config
    if "resnet-50" in model_name:
        _lowercase			:				Union[str, Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-50'					)
    elif "resnet-101" in model_name:
        _lowercase			:				Optional[Any] 						=						ResNetConfig.from_pretrained('microsoft/resnet-101'					)
    else:
        raise ValueError('Model name should include either resnet50 or resnet101'					)

    _lowercase			:				Tuple 						=						DetrConfig(use_timm_backbone=lowerCamelCase_				,					backbone_config=lowerCamelCase_					)

    # set label attributes
    _lowercase			:				Any 						=						'panoptic' in model_name
    if is_panoptic:
        _lowercase			:				List[Any] 						=						250
    else:
        _lowercase			:				str 						=						91
        _lowercase			:				List[Any] 						=						'huggingface/label-files'
        _lowercase			:				Any 						=						'coco-detection-id2label.json'
        _lowercase			:				Tuple 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
        _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
        _lowercase			:				int 						=						idalabel
        _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    return config, is_panoptic



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    _lowercase			:				List[str] 						=						[]

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')					)
    rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')					)
    rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')					)
    rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')					)
    rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')					)
    # stages
    for stage_idx in range(len(config.backbone_config.depths					)					):
        for layer_idx in range(config.backbone_config.depths[stage_idx]					):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                    )					)
            # 3 convs
            for i in range(3					):
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                    )					)
                rename_keys.append(
                    (
                        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                    )					)
    # fmt: on

    for i in range(config.encoder_layers					):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
                F'''encoder.layers.{i}.self_attn.out_proj.weight''',
            )					)
        rename_keys.append(
            (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')					)
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')					)
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')					)
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')					)
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')					)
        rename_keys.append(
            (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')					)
        rename_keys.append(
            (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')					)
        rename_keys.append(
            (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')					)
        rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')					)
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
                F'''decoder.layers.{i}.self_attn.out_proj.weight''',
            )					)
        rename_keys.append(
            (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')					)
        rename_keys.append(
            (
                F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
                F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
            )					)
        rename_keys.append(
            (
                F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
                F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
            )					)
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')					)
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')					)
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')					)
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')					)
        rename_keys.append(
            (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')					)
        rename_keys.append(
            (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')					)
        rename_keys.append(
            (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')					)
        rename_keys.append(
            (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')					)
        rename_keys.append(
            (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')					)
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')					)

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ('input_proj.weight', 'input_projection.weight'),
            ('input_proj.bias', 'input_projection.bias'),
            ('query_embed.weight', 'query_position_embeddings.weight'),
            ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
            ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
            ('class_embed.weight', 'class_labels_classifier.weight'),
            ('class_embed.bias', 'class_labels_classifier.bias'),
            ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
            ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
            ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
            ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
            ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
            ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ]					)

    return rename_keys



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				str 						=						state_dict.pop(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						val



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    _lowercase			:				Any 						=						''
    if is_panoptic:
        _lowercase			:				Optional[Any] 						=						'detr.'

    # first: transformer encoder
    for i in range(6					):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _lowercase			:				int 						=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
        _lowercase			:				Tuple 						=						state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''					)
        # next, add query, keys and values (in that order) to the state dict
        _lowercase			:				List[str] 						=						in_proj_weight[:256, :]
        _lowercase			:				Tuple 						=						in_proj_bias[:256]
        _lowercase			:				List[Any] 						=						in_proj_weight[256:512, :]
        _lowercase			:				Any 						=						in_proj_bias[256:512]
        _lowercase			:				int 						=						in_proj_weight[-256:, :]
        _lowercase			:				Optional[int] 						=						in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6					):
        # read in weights + bias of input projection layer of self-attention
        _lowercase			:				str 						=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
        _lowercase			:				Optional[int] 						=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''					)
        # next, add query, keys and values (in that order) to the state dict
        _lowercase			:				Union[str, Any] 						=						in_proj_weight[:256, :]
        _lowercase			:				Dict 						=						in_proj_bias[:256]
        _lowercase			:				Tuple 						=						in_proj_weight[256:512, :]
        _lowercase			:				Dict 						=						in_proj_bias[256:512]
        _lowercase			:				str 						=						in_proj_weight[-256:, :]
        _lowercase			:				Optional[int] 						=						in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        _lowercase			:				Tuple 						=						state_dict.pop(
            F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
        _lowercase			:				Dict 						=						state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''					)
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _lowercase			:				List[str] 						=						in_proj_weight_cross_attn[:256, :]
        _lowercase			:				Tuple 						=						in_proj_bias_cross_attn[:256]
        _lowercase			:				str 						=						in_proj_weight_cross_attn[256:512, :]
        _lowercase			:				Union[str, Any] 						=						in_proj_bias_cross_attn[256:512]
        _lowercase			:				List[Any] 						=						in_proj_weight_cross_attn[-256:, :]
        _lowercase			:				Dict 						=						in_proj_bias_cross_attn[-256:]



def 							UpperCamelCase_( )       ->		List[Any]:
    _lowercase			:				Dict 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				str 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return im



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=False					)       ->		List[Any]:
    _lowercase						,							_lowercase			:				int 						=						get_detr_config(lowerCamelCase_					)

    # load original model from torch hub
    _lowercase			:				int 						=						{
        'detr-resnet-50': 'detr_resnet50',
        'detr-resnet-101': 'detr_resnet101',
    }
    logger.info(F'''Converting model {model_name}...'''					)
    _lowercase			:				Optional[Any] 						=						torch.hub.load('facebookresearch/detr'				,					model_name_to_original_name[model_name]				,					pretrained=lowerCamelCase_					).eval()
    _lowercase			:				str 						=						detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(lowerCamelCase_					):
        if is_panoptic:
            _lowercase			:				str 						=						'detr.' + src
        rename_key(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowerCamelCase_				,					is_panoptic=lowerCamelCase_					)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _lowercase			:				List[Any] 						=						'detr.model.' if is_panoptic else 'model.'
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('detr'					)
                and not key.startswith('class_labels_classifier'					)
                and not key.startswith('bbox_predictor'					)
            ):
                _lowercase			:				Tuple 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				int 						=						val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				Optional[Any] 						=						val
            elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
                continue
            else:
                _lowercase			:				Optional[Any] 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				Union[str, Any] 						=						val
        else:
            if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
                _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
                _lowercase			:				List[str] 						=						val

    # finally, create HuggingFace model and load state dict
    _lowercase			:				Optional[Any] 						=						DetrForSegmentation(lowerCamelCase_					) if is_panoptic else DetrForObjectDetection(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # verify our conversion on an image
    _lowercase			:				str 						=						'coco_panoptic' if is_panoptic else 'coco_detection'
    _lowercase			:				Optional[int] 						=						DetrImageProcessor(format=lowerCamelCase_					)

    _lowercase			:				str 						=						processor(images=prepare_img()				,					return_tensors='pt'					)
    _lowercase			:				Tuple 						=						encoding['pixel_values']

    _lowercase			:				int 						=						detr(lowerCamelCase_					)
    _lowercase			:				Tuple 						=						model(lowerCamelCase_					)

    assert torch.allclose(outputs.logits				,					original_outputs['pred_logits']				,					atol=1e-3					)
    assert torch.allclose(outputs.pred_boxes				,					original_outputs['pred_boxes']				,					atol=1e-3					)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks				,					original_outputs['pred_masks']				,					atol=1e-4					)
    print('Looks ok!'					)

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''					)
        Path(lowerCamelCase_					).mkdir(exist_ok=lowerCamelCase_					)
        model.save_pretrained(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('Uploading PyTorch model and image processor to the hub...'					)
        model.push_to_hub(F'''nielsr/{model_name}'''					)
        processor.push_to_hub(F'''nielsr/{model_name}'''					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: List[Any]				   =					argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="detr-resnet-50",
     type=str,
     choices=["detr-resnet-50", "detr-resnet-101"],
     help="Name of the DETR model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
 SCREAMING_SNAKE_CASE			: Tuple				   =					parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


