






import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
UpperCAmelCase__						:			str									=logging.get_logger(__name__)





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

				lowerCamelCase	 =DetrConfig(use_timm_backbone=_lowerCAmelCase			,      backbone_config=_lowerCAmelCase				)

				# set label attributes
				lowerCamelCase	 ="""panoptic""" in model_name
				if is_panoptic:
								lowerCamelCase	 =2_50
				else:
								lowerCamelCase	 =91
								lowerCamelCase	 ="""huggingface/label-files"""
								lowerCamelCase	 ="""coco-detection-id2label.json"""
								lowerCamelCase	 =json.load(open(hf_hub_download(_lowerCAmelCase			,      _lowerCAmelCase			,      repo_type="""dataset"""				)			,      """r"""				)				)
								lowerCamelCase	 ={int(_lowerCAmelCase				): v for k, v in idalabel.items()}
								lowerCamelCase	 =idalabel
								lowerCamelCase	 ={v: k for k, v in idalabel.items()}

				return config, is_panoptic





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

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

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

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

				return rename_keys





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, Any]:
				lowerCamelCase	 =state_dict.pop(_lowerCAmelCase				)
				lowerCamelCase	 =val





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=False				)						->  List[str]:
				lowerCamelCase	 =""""""
				if is_panoptic:
								lowerCamelCase	 ="""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)
								lowerCamelCase	 =state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
								lowerCamelCase	 =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
								lowerCamelCase	 =in_proj_weight[:2_56, :]
								lowerCamelCase	 =in_proj_bias[:2_56]
								lowerCamelCase	 =in_proj_weight[2_56:5_12, :]
								lowerCamelCase	 =in_proj_bias[2_56:5_12]
								lowerCamelCase	 =in_proj_weight[-2_56:, :]
								lowerCamelCase	 =in_proj_bias[-2_56:]
				# 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
								lowerCamelCase	 =state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
								lowerCamelCase	 =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
								lowerCamelCase	 =in_proj_weight[:2_56, :]
								lowerCamelCase	 =in_proj_bias[:2_56]
								lowerCamelCase	 =in_proj_weight[2_56:5_12, :]
								lowerCamelCase	 =in_proj_bias[2_56:5_12]
								lowerCamelCase	 =in_proj_weight[-2_56:, :]
								lowerCamelCase	 =in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								lowerCamelCase	 =state_dict.pop(
								    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
								lowerCamelCase	 =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
								lowerCamelCase	 =in_proj_weight_cross_attn[:2_56, :]
								lowerCamelCase	 =in_proj_bias_cross_attn[:2_56]
								lowerCamelCase	 =in_proj_weight_cross_attn[2_56:5_12, :]
								lowerCamelCase	 =in_proj_bias_cross_attn[2_56:5_12]
								lowerCamelCase	 =in_proj_weight_cross_attn[-2_56:, :]
								lowerCamelCase	 =in_proj_bias_cross_attn[-2_56:]





def      _lowercase					(						)						->  Dict:
				lowerCamelCase	 ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase	 =Image.open(requests.get(_lowerCAmelCase			,      stream=_lowerCAmelCase				).raw				)

				return im





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=False				)						->  Dict:
				lowerCamelCase ,  lowerCamelCase	 =get_detr_config(_lowerCAmelCase				)

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

    # finally, create HuggingFace model and load state dict
				lowerCamelCase	 =DetrForSegmentation(_lowerCAmelCase				) if is_panoptic else DetrForObjectDetection(_lowerCAmelCase				)
				model.load_state_dict(_lowerCAmelCase				)
				model.eval()

				# verify our conversion on an image
				lowerCamelCase	 ="""coco_panoptic""" if is_panoptic else """coco_detection"""
				lowerCamelCase	 =DetrImageProcessor(format=_lowerCAmelCase				)

				lowerCamelCase	 =processor(images=prepare_img()			,      return_tensors="""pt"""				)
				lowerCamelCase	 =encoding["""pixel_values"""]

				lowerCamelCase	 =detr(_lowerCAmelCase				)
				lowerCamelCase	 =model(_lowerCAmelCase				)

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

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

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


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

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












from typing import List

from .keymap import KEYMAP, get_character





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += [key]
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator





def      _lowercase					(						*_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += keys
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator

class       __A     (						a    ):


				def __new__(		cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =super().__new__(cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)
								if not hasattr(UpperCAmelCase_   , """key_handler"""							):
												setattr(UpperCAmelCase_   , """key_handler"""   , {}							)
								setattr(UpperCAmelCase_   , """handle_input"""   , KeyHandler.handle_input							)

								for value in attrs.values():
												lowerCamelCase	 =getattr(UpperCAmelCase_   , """handle_key"""   , []							)
												for key in handled_keys:
																lowerCamelCase	 =value
								return new_cls


				@staticmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =get_character()
								if char != KEYMAP["undefined"]:
												lowerCamelCase	 =ord(UpperCAmelCase_							)
								lowerCamelCase	 =cls.key_handler.get(UpperCAmelCase_							)
								if handler:
												lowerCamelCase	 =char
												return handler(cls							)
								else:
												return None





def      _lowercase					(						cls				)						->  List[str]:
				return KeyHandler(cls.__name__			,      cls.__bases__			,      cls.__dict__.copy()				)





