




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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__ :   Union[str, Any]						=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__ :   List[str]						=							[]
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}.cross_attn.out_proj.weight""",
						        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
						    )
						)
						rename_keys.append(
						    (
						        F"""transformer.decoder.layers.{i}.cross_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"""))

						# q, k, v projections in self/cross-attention in decoder for conditional DETR
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
						)

						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
						)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     List[str]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      state_dict.pop(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      val





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  )       ->  Optional[Any]:







			"""simple docstring"""





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

			return new_state_dict





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Any=False  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      ''
			if is_panoptic:
						SCREAMING_SNAKE_CASE_   : Any					  =      'conditional_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)
						SCREAMING_SNAKE_CASE_   : Dict					  =      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
						SCREAMING_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
						SCREAMING_SNAKE_CASE_   : Tuple					  =      in_proj_weight[:2_56, :]
						SCREAMING_SNAKE_CASE_   : Tuple					  =      in_proj_bias[:2_56]
						SCREAMING_SNAKE_CASE_   : Tuple					  =      in_proj_weight[2_56:5_12, :]
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      in_proj_bias[2_56:5_12]
						SCREAMING_SNAKE_CASE_   : str					  =      in_proj_weight[-2_56:, :]
						SCREAMING_SNAKE_CASE_   : Any					  =      in_proj_bias[-2_56:]





def        __UpperCAmelCase						(							)       ->  Optional[int]:







			"""simple docstring"""





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

			return im





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







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						SCREAMING_SNAKE_CASE_   : int					  =      'resnet101'
			if "dc5" in model_name:
						SCREAMING_SNAKE_CASE_   : Dict					  =      True
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'panoptic' in model_name
			if is_panoptic:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      2_50
			else:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      91
						SCREAMING_SNAKE_CASE_   : Dict					  =      'huggingface/label-files'
						SCREAMING_SNAKE_CASE_   : int					  =      'coco-detection-id2label.json'
						SCREAMING_SNAKE_CASE_   : int					  =      json.load(open(hf_hub_download(lowerCamelCase_  ,							lowerCamelCase_  ,							repo_type='dataset'  )  ,							'r'  )  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      {int(lowerCamelCase_  ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_   : str					  =      idalabel
						SCREAMING_SNAKE_CASE_   : Any					  =      {v: k for k, v in idalabel.items()}

			# load image processor
			SCREAMING_SNAKE_CASE_   : int					  =      'coco_panoptic' if is_panoptic else 'coco_detection'
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConditionalDetrImageProcessor(format=lowerCamelCase_  )

			# prepare image
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      prepare_img()
			SCREAMING_SNAKE_CASE_   : int					  =      image_processor(images=lowerCamelCase_  ,							return_tensors='pt'  )
			SCREAMING_SNAKE_CASE_   : str					  =      encoding['pixel_values']

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

			# load original model from torch hub
			SCREAMING_SNAKE_CASE_   : str					  =      torch.hub.load('DeppMeng/ConditionalDETR'  ,							lowerCamelCase_  ,							pretrained=lowerCamelCase_  ).eval()
			SCREAMING_SNAKE_CASE_   : Any					  =      conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									SCREAMING_SNAKE_CASE_   : int					  =      'conditional_detr.' + src
						rename_key(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Any					  =      rename_backbone_keys(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
			SCREAMING_SNAKE_CASE_   : List[str]					  =      'conditional_detr.model.' if is_panoptic else 'model.'
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith('conditional_detr'  )
									    and not key.startswith('class_labels_classifier'  )
									    and not key.startswith('bbox_predictor'  )
									):
												SCREAMING_SNAKE_CASE_   : Tuple					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : Tuple					  =      val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												SCREAMING_SNAKE_CASE_   : str					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : List[str]					  =      val
									elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
												continue
									else:
												SCREAMING_SNAKE_CASE_   : List[str]					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      val
						else:
									if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
												SCREAMING_SNAKE_CASE_   : Tuple					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : str					  =      val
    # finally, create HuggingFace model and load state dict
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConditionalDetrForSegmentation(lowerCamelCase_  ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase_  )
			model.load_state_dict(lowerCamelCase_  )
			model.eval()
			model.push_to_hub(repo_id=lowerCamelCase_  ,							organization='DepuMeng'  ,							commit_message='Add model'  )
			# verify our conversion
			SCREAMING_SNAKE_CASE_   : str					  =      conditional_detr(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Any					  =      model(lowerCamelCase_  )
			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(lowerCamelCase_  ).mkdir(exist_ok=lowerCamelCase_  )
			model.save_pretrained(lowerCamelCase_  )
			image_processor.save_pretrained(lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[int]						=							argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''',
						    default='''conditional_detr_resnet50''',
						    type=str,
						    help='''Name of the CONDITIONAL_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.'''
						)
						UpperCamelCase__ :   Optional[int]						=							parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




