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__	:  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)
lowerCAmelCase__	:  Optional[Any]						=  []
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 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   List[str]:
				snake_case__   :      List[Any]   				= state_dict.pop(_snake_case       )
				snake_case__   :      List[str]   				= val
def 					UpperCamelCase__		(							A__       )  ->   Optional[int]:
				snake_case__   :      str   				= OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												snake_case__   :      Any   				= key.replace('backbone.0.body'     ,					'backbone.conv_encoder.model'       )
												snake_case__   :      int   				= value
								else:
												snake_case__   :      str   				= value

				return new_state_dict
def 					UpperCamelCase__		(							A__     ,					A__=False       )  ->   Optional[Any]:
				snake_case__   :      Optional[int]   				= ''
				if is_panoptic:
								snake_case__   :      List[str]   				= '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__   :      str   				= state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
								snake_case__   :      Optional[int]   				= 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__   :      Optional[Any]   				= in_proj_weight[:256, :]
								snake_case__   :      List[str]   				= in_proj_bias[:256]
								snake_case__   :      Optional[int]   				= in_proj_weight[256:512, :]
								snake_case__   :      Optional[int]   				= in_proj_bias[256:512]
								snake_case__   :      List[Any]   				= in_proj_weight[-256:, :]
								snake_case__   :      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
								snake_case__   :      int   				= state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
								snake_case__   :      Optional[int]   				= 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__   :      List[str]   				= in_proj_weight[:256, :]
								snake_case__   :      Optional[Any]   				= in_proj_bias[:256]
								snake_case__   :      Tuple   				= in_proj_weight[256:512, :]
								snake_case__   :      Union[str, Any]   				= in_proj_bias[256:512]
								snake_case__   :      Union[str, Any]   				= in_proj_weight[-256:, :]
								snake_case__   :      Optional[int]   				= in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								snake_case__   :      Any   				= state_dict.pop(
								    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
								snake_case__   :      List[str]   				= 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__   :      List[Any]   				= in_proj_weight_cross_attn[:256, :]
								snake_case__   :      List[str]   				= in_proj_bias_cross_attn[:256]
								snake_case__   :      List[Any]   				= in_proj_weight_cross_attn[256:512, :]
								snake_case__   :      int   				= in_proj_bias_cross_attn[256:512]
								snake_case__   :      List[Any]   				= in_proj_weight_cross_attn[-256:, :]
								snake_case__   :      Any   				= in_proj_bias_cross_attn[-256:]
def 					UpperCamelCase__		(							)  ->   str:
				snake_case__   :      Dict   				= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__   :      List[str]   				= Image.open(requests.get(_snake_case     ,					stream=_snake_case       ).raw       )

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

				# load image processor
				snake_case__   :      int   				= 'coco_panoptic' if is_panoptic else 'coco_detection'
				snake_case__   :      Any   				= DetrImageProcessor(format=_snake_case       )

				# prepare image
				snake_case__   :      Union[str, Any]   				= prepare_img()
				snake_case__   :      Union[str, Any]   				= image_processor(images=_snake_case     ,					return_tensors='pt'       )
				snake_case__   :      str   				= encoding['pixel_values']

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

				# load original model from torch hub
				snake_case__   :      Any   				= torch.hub.load('facebookresearch/detr'     ,					_snake_case     ,					pretrained=_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(_snake_case     ,					_snake_case     ,					_snake_case       )
				snake_case__   :      Union[str, Any]   				= 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
				snake_case__   :      List[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__   :      Tuple   				= state_dict.pop(_snake_case       )
																snake_case__   :      Tuple   				= val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case__   :      Any   				= state_dict.pop(_snake_case       )
																snake_case__   :      int   				= val
												elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
																continue
												else:
																snake_case__   :      Optional[Any]   				= state_dict.pop(_snake_case       )
																snake_case__   :      str   				= val
								else:
												if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
																snake_case__   :      Optional[Any]   				= state_dict.pop(_snake_case       )
																snake_case__   :      Tuple   				= val
    # finally, create HuggingFace model and load state dict
				snake_case__   :      List[Any]   				= DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
				model.load_state_dict(_snake_case       )
				model.eval()
				# verify our conversion
				snake_case__   :      Optional[Any]   				= detr(_snake_case       )
				snake_case__   :      Tuple   				= 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__	:  int						=  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__	:  str						=  parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""simple docstring"""


__UpperCamelCase       :    List[Any]         =		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

__UpperCamelCase       :    Any         =		[{"type": "code", "content": INSTALL_CONTENT}]