





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 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=7,	__magic_name__=3,	__magic_name__=30,	__magic_name__=400,	__magic_name__=True,	__magic_name__=None,	__magic_name__=True,	__magic_name__=1 / 255,	__magic_name__=True,	__magic_name__=[0.5, 0.5, 0.5],	__magic_name__=[0.5, 0.5, 0.5],	__magic_name__=True,	)				->   List[str]:






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




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """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    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=False							)				->   Optional[Any]:






       """simple docstring"""





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

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

       return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Tuple     	=	DetrImageProcessor if is_vision_available() else None




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       int      =   DetrImageProcessingTester(self							)




   @property
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   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    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       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__							:       Optional[Any]      =   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    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCamelCase__							:       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
       UpperCamelCase__							:       Dict      =   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__     ,UpperCamelCase__							:       Union[str, Any]      =   self.image_processor_tester.get_expected_values(UpperCamelCase__,	batched=UpperCamelCase__							)

       UpperCamelCase__							:       Optional[Any]      =   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    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       UpperCamelCase__							:       Optional[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__							:       Dict      =   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__							:       Any      =   image_processing(UpperCamelCase__,	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       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,
           ),	)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       UpperCamelCase__							:       Tuple      =   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__							:       Optional[int]      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   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__							:       Any      =   image_processing(UpperCamelCase__,	return_tensors='''pt'''							).pixel_values

       UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   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    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''',	'''r'''							) as f:
           UpperCamelCase__							:       Any      =   json.loads(f.read()							)

       UpperCamelCase__							:       Optional[int]      =   {'''image_id''': 39769, '''annotations''': target}

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

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

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

       # verify area
       UpperCamelCase__							:       Any      =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
       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__							:       Any      =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
       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__							:       Union[str, Any]      =   torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],	UpperCamelCase__							)							)
       # verify class_labels
       UpperCamelCase__							:       List[str]      =   torch.tensor([75, 75, 63, 65, 17, 17]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],	UpperCamelCase__							)							)
       # verify orig_size
       UpperCamelCase__							:       str      =   torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],	UpperCamelCase__							)							)
       # verify size
       UpperCamelCase__							:       str      =   torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],	UpperCamelCase__							)							)




   @slow
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''',	'''r'''							) as f:
           UpperCamelCase__							:       str      =   json.loads(f.read()							)

       UpperCamelCase__							:       Optional[Any]      =   {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

       UpperCamelCase__							:       Tuple      =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

       # encode them
       UpperCamelCase__							:       Tuple      =   DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''							)
       UpperCamelCase__							:       Any      =   image_processing(images=UpperCamelCase__,	annotations=UpperCamelCase__,	masks_path=UpperCamelCase__,	return_tensors='''pt'''							)

       # verify pixel values
       UpperCamelCase__							:       Dict      =   torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape,	UpperCamelCase__							)

       UpperCamelCase__							:       Optional[int]      =   torch.tensor([0.2796, 0.3138, 0.3481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3],	UpperCamelCase__,	atol=1E-4							)							)

       # verify area
       UpperCamelCase__							:       str      =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''],	UpperCamelCase__							)							)
       # verify boxes
       UpperCamelCase__							:       Dict      =   torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,	UpperCamelCase__							)
       UpperCamelCase__							:       Any      =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],	UpperCamelCase__,	atol=1E-3							)							)
       # verify image_id
       UpperCamelCase__							:       Optional[int]      =   torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],	UpperCamelCase__							)							)
       # verify is_crowd
       UpperCamelCase__							:       List[Any]      =   torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],	UpperCamelCase__							)							)
       # verify class_labels
       UpperCamelCase__							:       Any      =   torch.tensor([17, 17, 63, 75, 75, 93]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],	UpperCamelCase__							)							)
       # verify masks
       UpperCamelCase__							:       Optional[int]      =   822873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),	UpperCamelCase__							)
       # verify orig_size
       UpperCamelCase__							:       List[str]      =   torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],	UpperCamelCase__							)							)
       # verify size
       UpperCamelCase__							:       Optional[int]      =   torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],	UpperCamelCase__							)							)








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=				logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			Optional[Any]:
    UpperCamelCase__							:       Optional[Any]      =   SwinConfig(
        embed_dim=192	,     depths=(2, 2, 18, 2)	,     num_heads=(6, 12, 24, 48)	,     window_size=12	,     out_features=['''stage2''', '''stage3''', '''stage4''']	,     )

    UpperCamelCase__							:       List[Any]      =   DetaConfig(
        backbone_config=__UpperCAmelCase	,     num_queries=900	,     encoder_ffn_dim=2048	,     decoder_ffn_dim=2048	,     num_feature_levels=5	,     assign_first_stage=__UpperCAmelCase	,     with_box_refine=__UpperCAmelCase	,     two_stage=__UpperCAmelCase	,     )

    # set labels
    UpperCamelCase__							:       str      =   '''huggingface/label-files'''
    if "o365" in model_name:
        UpperCamelCase__							:       str      =   366
        UpperCamelCase__							:       List[Any]      =   '''object365-id2label.json'''
    else:
        UpperCamelCase__							:       Dict      =   91
        UpperCamelCase__							:       Optional[int]      =   '''coco-detection-id2label.json'''

    UpperCamelCase__							:       Dict      =   num_labels
    UpperCamelCase__							:       List[str]      =   json.load(open(cached_download(hf_hub_url(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )   )	,     '''r'''   )   )
    UpperCamelCase__							:       Optional[Any]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
    UpperCamelCase__							:       Optional[Any]      =   idalabel
    UpperCamelCase__							:       Optional[int]      =   {v: k for k, v in idalabel.items()}

    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   []

    # stem
    # fmt: off
    rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')   )
    rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')   )
    rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')   )
    rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')   )
    # stages
    for i in range(len(config.backbone_config.depths   )   ):
        for j in range(config.backbone_config.depths[i]   ):
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")   )

        if i < 3:
            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")   )
            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")   )

    rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')   )
    rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')   )
    rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')   )
    rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')   )
    rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')   )
    rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')   )

    # transformer encoder
    for i in range(config.encoder_layers   ):
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")   )
        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")   )

    # transformer decoder
    for i in range(config.decoder_layers   ):
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")   )
        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")   )

    # fmt: on

    return rename_keys




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    List[Any]   )   ->			str:
    UpperCamelCase__							:       Optional[Any]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[int]   )   ->			int:
    UpperCamelCase__							:       Any      =   [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
    for i in range(len(backbone_config.depths   )   ):
        UpperCamelCase__							:       int      =   num_features[i]
        for j in range(backbone_config.depths[i]   ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            UpperCamelCase__							:       Optional[Any]      =   state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"   )
            UpperCamelCase__							:       Dict      =   state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase__							:       List[str]      =   in_proj_weight[:dim, :]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_bias[: dim]
            UpperCamelCase__							:       str      =   in_proj_weight[
                dim : dim * 2, :
            ]
            UpperCamelCase__							:       Dict      =   in_proj_bias[
                dim : dim * 2
            ]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_weight[
                -dim :, :
            ]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_bias[-dim :]
            # fmt: on




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    Optional[int]   )   ->			Optional[Any]:
    # transformer decoder self-attention layers
    UpperCamelCase__							:       List[str]      =   config.d_model
    for i in range(config.decoder_layers   ):
        # read in weights + bias of input projection layer of self-attention
        UpperCamelCase__							:       Tuple      =   state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       Any      =   state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias"   )
        # next, add query, keys and values (in that order) to the state dict
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[:hidden_size, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:hidden_size]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        UpperCamelCase__							:       Any      =   in_proj_bias[hidden_size : hidden_size * 2]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[-hidden_size:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-hidden_size:]




def 	lowerCAmelCase_					(						)   ->			Union[str, Any]:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       int      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Any   )   ->			Union[str, Any]:
    UpperCamelCase__							:       str      =   get_deta_config(__UpperCAmelCase   )

    # load original state dict
    if model_name == "deta-swin-large":
        UpperCamelCase__							:       Optional[int]      =   hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,     filename='''adet_swin_ft.pth'''   )
    elif model_name == "deta-swin-large-o365":
        UpperCamelCase__							:       Optional[Any]      =   hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''	,     filename='''deta_swin_pt_o365.pth'''   )
    else:
        raise ValueError(f"Model name {model_name} not supported"   )

    UpperCamelCase__							:       Union[str, Any]      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']

    # original state dict
    for name, param in state_dict.items():
        print(__UpperCAmelCase	,     param.shape   )

    # rename keys
    UpperCamelCase__							:       List[str]      =   create_rename_keys(__UpperCAmelCase   )
    for src, dest in rename_keys:
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    read_in_swin_q_k_v(__UpperCAmelCase	,     config.backbone_config   )
    read_in_decoder_q_k_v(__UpperCAmelCase	,     __UpperCAmelCase   )

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            UpperCamelCase__							:       Tuple      =   state_dict.pop(__UpperCAmelCase   )
            UpperCamelCase__							:       Dict      =   val
        if "input_proj" in key:
            UpperCamelCase__							:       Optional[int]      =   state_dict.pop(__UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            UpperCamelCase__							:       Optional[int]      =   state_dict.pop(__UpperCAmelCase   )
            UpperCamelCase__							:       Union[str, Any]      =   val

    # finally, create HuggingFace model and load state dict
    UpperCamelCase__							:       Optional[int]      =   DetaForObjectDetection(__UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )
    model.eval()

    UpperCamelCase__							:       List[str]      =   '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(__UpperCAmelCase   )

    # load image processor
    UpperCamelCase__							:       Any      =   DetaImageProcessor(format='''coco_detection'''   )

    # verify our conversion on image
    UpperCamelCase__							:       Optional[Any]      =   prepare_img()
    UpperCamelCase__							:       str      =   processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']
    UpperCamelCase__							:       str      =   model(pixel_values.to(__UpperCAmelCase   )   )

    # verify logits
    print('''Logits:'''	,     outputs.logits[0, :3, :3]   )
    print('''Boxes:'''	,     outputs.pred_boxes[0, :3, :3]   )
    if model_name == "deta-swin-large":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]   )
        UpperCamelCase__							:       List[str]      =   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]   )
    elif model_name == "deta-swin-large-o365":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]   )
        UpperCamelCase__							:       Tuple      =   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]   )
    assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(__UpperCAmelCase   )	,     atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(__UpperCAmelCase   )	,     atol=1e-4   )
    print('''Everything ok!'''   )

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."   )
        Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
        model.save_pretrained(__UpperCAmelCase   )
        processor.save_pretrained(__UpperCAmelCase   )

    # Push to hub
    if push_to_hub:
        print('''Pushing model and processor to hub...'''   )
        model.push_to_hub(f"jozhang97/{model_name}"   )
        processor.push_to_hub(f"jozhang97/{model_name}"   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()

  parser.add_argument(
      '--model_name',
      type=str,
      default='deta-swin-large',
      choices=['deta-swin-large', 'deta-swin-large-o365'],
      help='Name of the 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 or not to push the converted model to the 🤗 hub.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

