

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()
__A	:   List[Any]	=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A	:   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					:							Any      ,  _A					:							List[str]      ,  _A					:							List[Any]		)				->str:



							"""simple docstring"""
							lowerCamelCase_								=state_dict.pop(_A		)
							lowerCamelCase_								=val




def        __UpperCamelCase     (      _A					:							Optional[Any]		)				->str:



							"""simple docstring"""
							lowerCamelCase_								=OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					lowerCamelCase_								=key.replace("""backbone.0.body"""      ,  """backbone.conv_encoder.model"""		)
																					lowerCamelCase_								=value
														else:
																					lowerCamelCase_								=value

							return new_state_dict




def        __UpperCamelCase     (      _A					:							Any      ,  _A					:							Optional[Any]=False		)				->Dict:



							"""simple docstring"""
							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[:256, :]
														lowerCamelCase_								=in_proj_bias[:256]
														lowerCamelCase_								=in_proj_weight[256:512, :]
														lowerCamelCase_								=in_proj_bias[256:512]
														lowerCamelCase_								=in_proj_weight[-256:, :]
														lowerCamelCase_								=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
														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[:256, :]
														lowerCamelCase_								=in_proj_bias[:256]
														lowerCamelCase_								=in_proj_weight[256:512, :]
														lowerCamelCase_								=in_proj_bias[256:512]
														lowerCamelCase_								=in_proj_weight[-256:, :]
														lowerCamelCase_								=in_proj_bias[-256:]
														# 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[:256, :]
														lowerCamelCase_								=in_proj_bias_cross_attn[:256]
														lowerCamelCase_								=in_proj_weight_cross_attn[256:512, :]
														lowerCamelCase_								=in_proj_bias_cross_attn[256:512]
														lowerCamelCase_								=in_proj_weight_cross_attn[-256:, :]
														lowerCamelCase_								=in_proj_bias_cross_attn[-256:]




def        __UpperCamelCase     (      )				->int:



							"""simple docstring"""
							lowerCamelCase_								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_								=Image.open(requests.get(_A      ,  stream=_A		).raw		)

							return im




@torch.no_grad()
def        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							int		)				->int:



							"""simple docstring"""
							lowerCamelCase_								=DetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														lowerCamelCase_								="""resnet101"""
							if "dc5" in model_name:
														lowerCamelCase_								=True
							lowerCamelCase_								="""panoptic""" in model_name
							if is_panoptic:
														lowerCamelCase_								=250
							else:
														lowerCamelCase_								=91
														lowerCamelCase_								="""huggingface/label-files"""
														lowerCamelCase_								="""coco-detection-id2label.json"""
														lowerCamelCase_								=json.load(open(hf_hub_download(_A      ,  _A      ,  repo_type="""dataset"""		)      ,  """r"""		)		)
														lowerCamelCase_								={int(_A		): v for k, v in idalabel.items()}
														lowerCamelCase_								=idalabel
														lowerCamelCase_								={v: k for k, v in idalabel.items()}

							# load image processor
							lowerCamelCase_								="""coco_panoptic""" if is_panoptic else """coco_detection"""
							lowerCamelCase_								=DetrImageProcessor(format=_A		)

							# prepare image
							lowerCamelCase_								=prepare_img()
							lowerCamelCase_								=image_processor(images=_A      ,  return_tensors="""pt"""		)
							lowerCamelCase_								=encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
	__A	:   str	=					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.'
	)
	__A	:   Any	=					parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



'''simple docstring'''

lowerCAmelCase_     =      "\n# Installation de Transformers\n! pip install transformers datasets\n# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCAmelCase_     =      [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase_     =      {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





