




"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor



class  _snake_case				(							unittest.TestCase       ):

				'''simple docstring'''




				def __init__(							self      :  List[str]					,							snake_case      :  List[str]					,							snake_case      :  int=7					,							snake_case      :  List[str]=3					,							snake_case      :  Union[str, Any]=10					,							snake_case      :  Tuple=18					,							snake_case      :  Tuple=30					,							snake_case      :  Tuple=400					,							snake_case      :  Any=True					,							snake_case      :  int=None					,							snake_case      :  Optional[Any]=True					,							snake_case      :  Any=[0.5, 0.5, 0.5]					,							snake_case      :  Any=[0.5, 0.5, 0.5]					,							snake_case      :  Any=None					,							):
								UpperCAmelCase_					:List[str]      			=      size if size is not None else {"shortest_edge": 18}
								UpperCAmelCase_					:Tuple      			=      crop_size if crop_size is not None else {"height": 18, "width": 18}

								UpperCAmelCase_					:Any      			=      parent
								UpperCAmelCase_					:int      			=      batch_size
								UpperCAmelCase_					:str      			=      num_channels
								UpperCAmelCase_					:List[Any]      			=      num_frames
								UpperCAmelCase_					:List[Any]      			=      image_size
								UpperCAmelCase_					:List[Any]      			=      min_resolution
								UpperCAmelCase_					:str      			=      max_resolution
								UpperCAmelCase_					:Optional[int]      			=      do_resize
								UpperCAmelCase_					:Optional[int]      			=      size
								UpperCAmelCase_					:Optional[int]      			=      do_normalize
								UpperCAmelCase_					:Dict      			=      image_mean
								UpperCAmelCase_					:Optional[Any]      			=      image_std
								UpperCAmelCase_					:int      			=      crop_size





				def 				snake_case_ (							self      :  Dict     ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class  _snake_case				(							A__							,						unittest.TestCase       ):

				'''simple docstring'''




				UpperCamelCase__		     =VideoMAEImageProcessor if is_vision_available() else None





				def 				snake_case_ (							self      :  Any     ):
								UpperCAmelCase_					:Any      			=      VideoMAEImageProcessingTester(self     )





				@property
				def 				snake_case_ (							self      :  str     ):
								return self.image_processor_tester.prepare_image_processor_dict()





				def 				snake_case_ (							self      :  Dict     ):
								UpperCAmelCase_					:Optional[int]      			=      self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(snake_case					,							'''image_mean'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''image_std'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_normalize'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_resize'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_center_crop'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''size'''     )     )





				def 				snake_case_ (							self      :  str     ):
								UpperCAmelCase_					:List[Any]      			=      self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size					,							{'''shortest_edge''': 18}     )
								self.assertEqual(image_processor.crop_size					,							{'''height''': 18, '''width''': 18}     )

								UpperCAmelCase_					:Union[str, Any]      			=      self.image_processing_class.from_dict(self.image_processor_dict					,							size=42					,							crop_size=84     )
								self.assertEqual(image_processor.size					,							{'''shortest_edge''': 42}     )
								self.assertEqual(image_processor.crop_size					,							{'''height''': 84, '''width''': 84}     )





				def 				snake_case_ (							self      :  Any     ):
								pass





				def 				snake_case_ (							self      :  int     ):
								UpperCAmelCase_					:Optional[Any]      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random PIL videos
								UpperCAmelCase_					:Optional[Any]      			=      prepare_video_inputs(self.image_processor_tester					,							equal_resolution=snake_case     )
								for video in video_inputs:
												self.assertIsInstance(snake_case					,							snake_case     )
												self.assertIsInstance(video[0]					,							Image.Image     )

								# Test not batched input
								UpperCAmelCase_					:List[Any]      			=      image_processing(video_inputs[0]					,							return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_videos.shape					,							(
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )					,							)

								# Test batched
								UpperCAmelCase_					:Any      			=      image_processing(snake_case					,							return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_videos.shape					,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )					,							)





				def 				snake_case_ (							self      :  Dict     ):
								UpperCAmelCase_					:Tuple      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								UpperCAmelCase_					:Dict      			=      prepare_video_inputs(self.image_processor_tester					,							equal_resolution=snake_case					,							numpify=snake_case     )
								for video in video_inputs:
												self.assertIsInstance(snake_case					,							snake_case     )
												self.assertIsInstance(video[0]					,							np.ndarray     )

								# Test not batched input
								UpperCAmelCase_					:Optional[int]      			=      image_processing(video_inputs[0]					,							return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_videos.shape					,							(
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )					,							)

								# Test batched
								UpperCAmelCase_					:Any      			=      image_processing(snake_case					,							return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_videos.shape					,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )					,							)





				def 				snake_case_ (							self      :  str     ):
								UpperCAmelCase_					:Optional[Any]      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								UpperCAmelCase_					:List[str]      			=      prepare_video_inputs(self.image_processor_tester					,							equal_resolution=snake_case					,							torchify=snake_case     )
								for video in video_inputs:
												self.assertIsInstance(snake_case					,							snake_case     )
												self.assertIsInstance(video[0]					,							torch.Tensor     )

								# Test not batched input
								UpperCAmelCase_					:str      			=      image_processing(video_inputs[0]					,							return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_videos.shape					,							(
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )					,							)

								# Test batched
								UpperCAmelCase_					:Dict      			=      image_processing(snake_case					,							return_tensors='''pt'''     ).pixel_values
								self.assertEqual(
								    encoded_videos.shape					,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )					,							)








"""simple docstring"""

import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase								=						[]
for i in range(6):
						# 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"),
    ]
)



def 				a (						__snake_case     :				Optional[Any],    __snake_case     :				Any,    __snake_case     :				Tuple  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(__snake_case  )
				UpperCAmelCase_					:List[str]      			=      val



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												UpperCAmelCase_					:List[Any]      			=      key.replace('''backbone.0.body''',    '''backbone.conv_encoder.model'''  )
												UpperCAmelCase_					:Union[str, Any]      			=      value
								else:
												UpperCAmelCase_					:Any      			=      value

				return new_state_dict



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Optional[Any]=False  ):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      ''''''
				if is_panoptic:
								UpperCAmelCase_					:Tuple      			=      '''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)
								UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					: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
								UpperCAmelCase_					:List[str]      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Union[str, Any]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					: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
								UpperCAmelCase_					:List[str]      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					:str      			=      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
								UpperCAmelCase_					:Dict      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Optional[int]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:int      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								UpperCAmelCase_					:int      			=      state_dict.pop(
								    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'  )
								UpperCAmelCase_					:Optional[int]      			=      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
								UpperCAmelCase_					:int      			=      in_proj_weight_cross_attn[:256, :]
								UpperCAmelCase_					:str      			=      in_proj_bias_cross_attn[:256]
								UpperCAmelCase_					:Tuple      			=      in_proj_weight_cross_attn[256:512, :]
								UpperCAmelCase_					:Any      			=      in_proj_bias_cross_attn[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight_cross_attn[-256:, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias_cross_attn[-256:]



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:List[str]      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )

				return im



@torch.no_grad()
def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				List[Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      DetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								UpperCAmelCase_					:Optional[Any]      			=      '''resnet101'''
				if "dc5" in model_name:
								UpperCAmelCase_					:Optional[int]      			=      True
				UpperCAmelCase_					:Dict      			=      '''panoptic''' in model_name
				if is_panoptic:
								UpperCAmelCase_					:Optional[int]      			=      250
				else:
								UpperCAmelCase_					:Dict      			=      91
								UpperCAmelCase_					:Union[str, Any]      			=      '''huggingface/label-files'''
								UpperCAmelCase_					:str      			=      '''coco-detection-id2label.json'''
								UpperCAmelCase_					:Dict      			=      json.load(open(hf_hub_download(__snake_case,    __snake_case,    repo_type='''dataset'''  ),    '''r'''  )  )
								UpperCAmelCase_					:Optional[Any]      			=      {int(__snake_case  ): v for k, v in idalabel.items()}
								UpperCAmelCase_					:str      			=      idalabel
								UpperCAmelCase_					:Tuple      			=      {v: k for k, v in idalabel.items()}

				# load image processor
				UpperCAmelCase_					:Dict      			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				UpperCAmelCase_					:Optional[Any]      			=      DetrImageProcessor(format=__snake_case  )

				# prepare image
				UpperCAmelCase_					:Dict      			=      prepare_img()
				UpperCAmelCase_					:Union[str, Any]      			=      image_processor(images=__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:Optional[Any]      			=      encoding['''pixel_values''']

				logger.info(f'Converting model {model_name}...'  )

				# load original model from torch hub
				UpperCAmelCase_					:int      			=      torch.hub.load('''facebookresearch/detr''',    __snake_case,    pretrained=__snake_case  ).eval()
				UpperCAmelCase_					:List[Any]      			=      detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												UpperCAmelCase_					:Optional[int]      			=      '''detr.''' + src
								rename_key(__snake_case,    __snake_case,    __snake_case  )
				UpperCAmelCase_					:int      			=      rename_backbone_keys(__snake_case  )
				# query, key and value matrices need special treatment
				read_in_q_k_v(__snake_case,    is_panoptic=__snake_case  )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				UpperCAmelCase_					:Tuple      			=      '''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'''  )
												):
																UpperCAmelCase_					:List[str]      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:List[Any]      			=      val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																UpperCAmelCase_					:int      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Union[str, Any]      			=      val
												elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
																continue
												else:
																UpperCAmelCase_					:Tuple      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Dict      			=      val
								else:
												if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
																UpperCAmelCase_					:Any      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Any      			=      val
    # finally, create HuggingFace model and load state dict
				UpperCAmelCase_					:Optional[int]      			=      DetrForSegmentation(__snake_case  ) if is_panoptic else DetrForObjectDetection(__snake_case  )
				model.load_state_dict(__snake_case  )
				model.eval()
				# verify our conversion
				UpperCAmelCase_					:List[Any]      			=      detr(__snake_case  )
				UpperCAmelCase_					:Optional[int]      			=      model(__snake_case  )
				assert torch.allclose(outputs.logits,    original_outputs['''pred_logits'''],    atol=1E-4  )
				assert torch.allclose(outputs.pred_boxes,    original_outputs['''pred_boxes'''],    atol=1E-4  )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks,    original_outputs['''pred_masks'''],    atol=1E-4  )

				# Save model and image processor
				logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'  )
				Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
				model.save_pretrained(__snake_case  )
				image_processor.save_pretrained(__snake_case  )


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

						parser.add_argument(
						    "--model_name", default="detr_resnet50", type=str, 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."
						)
						__lowerCamelCase								=						parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


