





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   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				)	->		Any:






				"""simple docstring"""


				snake_case_		:					Optional[Any]			    =						state_dict.pop(_SCREAMING_SNAKE_CASE				)
				snake_case_		:					Any			    =						val






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Union[str, Any]:






				"""simple docstring"""


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

				return new_state_dict






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase=False				)	->		Tuple:






				"""simple docstring"""


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






def   lowerCamelCase_		(       )	->		Optional[Any]:






				"""simple docstring"""


				snake_case_		:					str			    =						'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case_		:					Optional[Any]			    =						Image.open(requests.get(_SCREAMING_SNAKE_CASE				,       stream=_SCREAMING_SNAKE_CASE				).raw				)

				return im






@torch.no_grad()
def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		Optional[Any]:






				"""simple docstring"""


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

				# load image processor
				snake_case_		:					Any			    =						'coco_panoptic' if is_panoptic else 'coco_detection'
				snake_case_		:					Union[str, Any]			    =						DetrImageProcessor(format=_SCREAMING_SNAKE_CASE				)

				# prepare image
				snake_case_		:					Union[str, Any]			    =						prepare_img()
				snake_case_		:					List[Any]			    =						image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''				)
				snake_case_		:					Dict			    =						encoding['pixel_values']

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

				# load original model from torch hub
				snake_case_		:					List[Any]			    =						torch.hub.load('''facebookresearch/detr'''				,       _SCREAMING_SNAKE_CASE				,       pretrained=_SCREAMING_SNAKE_CASE				).eval()
				snake_case_		:					Tuple			    =						detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												snake_case_		:					Dict			    =						'detr.' + src
								rename_key(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				)
				snake_case_		:					int			    =						rename_backbone_keys(_SCREAMING_SNAKE_CASE				)
				# query, key and value matrices need special treatment
				read_in_q_k_v(_SCREAMING_SNAKE_CASE				,       is_panoptic=_SCREAMING_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
				snake_case_		:					Optional[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'''				)
												):
																snake_case_		:					int			    =						state_dict.pop(_SCREAMING_SNAKE_CASE				)
																snake_case_		:					Tuple			    =						val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case_		:					Any			    =						state_dict.pop(_SCREAMING_SNAKE_CASE				)
																snake_case_		:					Union[str, Any]			    =						val
												elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
																continue
												else:
																snake_case_		:					Dict			    =						state_dict.pop(_SCREAMING_SNAKE_CASE				)
																snake_case_		:					int			    =						val
								else:
												if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
																snake_case_		:					int			    =						state_dict.pop(_SCREAMING_SNAKE_CASE				)
																snake_case_		:					Optional[Any]			    =						val
    # finally, create HuggingFace model and load state dict
				snake_case_		:					Union[str, Any]			    =						DetrForSegmentation(_SCREAMING_SNAKE_CASE				) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE				)
				model.load_state_dict(_SCREAMING_SNAKE_CASE				)
				model.eval()
				# verify our conversion
				snake_case_		:					Union[str, Any]			    =						detr(_SCREAMING_SNAKE_CASE				)
				snake_case_		:					int			    =						model(_SCREAMING_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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				image_processor.save_pretrained(_SCREAMING_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)







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
							    ''' use LevitImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
