






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()
UpperCamelCase												=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase												=      []
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     (						snake_case__			,snake_case__			,snake_case__						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    state_dict.pop(_UpperCamelCase						)
						_SCREAMING_SNAKE_CASE       =    val

def 			__lowerCamelCase     (						snake_case__						)				->    Union[str, Any]:






						"""simple docstring"""





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

						return new_state_dict

def 			__lowerCamelCase     (						snake_case__			,snake_case__=False						)				->    Dict:






						"""simple docstring"""





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

def 			__lowerCamelCase     (						)				->    List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_SCREAMING_SNAKE_CASE       =    Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase						).raw						)

						return im

@torch.no_grad()
def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												_SCREAMING_SNAKE_CASE       =    """resnet101"""
						if "dc5" in model_name:
												_SCREAMING_SNAKE_CASE       =    True
						_SCREAMING_SNAKE_CASE       =    """panoptic""" in model_name
						if is_panoptic:
												_SCREAMING_SNAKE_CASE       =    2_50
						else:
												_SCREAMING_SNAKE_CASE       =    91
												_SCREAMING_SNAKE_CASE       =    """huggingface/label-files"""
												_SCREAMING_SNAKE_CASE       =    """coco-detection-id2label.json"""
												_SCREAMING_SNAKE_CASE       =    json.load(open(hf_hub_download(_UpperCamelCase			,_UpperCamelCase			,repo_type="""dataset"""						)			,"""r"""						)						)
												_SCREAMING_SNAKE_CASE       =    {int(_UpperCamelCase						): v for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE       =    idalabel
												_SCREAMING_SNAKE_CASE       =    {v: k for k, v in idalabel.items()}

						# load image processor
						_SCREAMING_SNAKE_CASE       =    """coco_panoptic""" if is_panoptic else """coco_detection"""
						_SCREAMING_SNAKE_CASE       =    DetrImageProcessor(format=_UpperCamelCase						)

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

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

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


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





"""simple docstring"""







import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__a      			= [nn.LayerNorm]

__a      			= logging.get_logger(__name__)

__a      			= version.parse(version.parse(torch.__version__).base_version)

__a      			= parsed_torch_version_base >= version.parse("2.0")
__a      			= parsed_torch_version_base >= version.parse("1.12")
__a      			= parsed_torch_version_base >= version.parse("1.11")
__a      			= parsed_torch_version_base < version.parse("1.11")






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      self			):





						"""simple docstring"""

						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(_UpperCamelCase						,      _UpperCamelCase						,      parent.dim						,      self			)
						else:
												return _softmax_backward_data(_UpperCamelCase						,      _UpperCamelCase						,      parent.dim						,      self.dtype			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 0			):





						"""simple docstring"""

						UpperCAmelCase__												=     index.to(layer.weight.device			)
						UpperCAmelCase__												=     layer.weight.index_select(_UpperCamelCase						,      _UpperCamelCase			).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		UpperCAmelCase__												=     layer.bias.clone().detach()
												else:
																		UpperCAmelCase__												=     layer.bias[index].clone().detach()
						UpperCAmelCase__												=     list(layer.weight.size()			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     nn.Linear(new_size[1]						,      new_size[0]						,      bias=layer.bias is not None			).to(layer.weight.device			)
						UpperCAmelCase__												=     False
						new_layer.weight.copy_(W.contiguous()			)
						UpperCAmelCase__												=     True
						if layer.bias is not None:
												UpperCAmelCase__												=     False
												new_layer.bias.copy_(b.contiguous()			)
												UpperCAmelCase__												=     True
						return new_layer
class 				lowerCamelCase							(      nn.Module			):


							"""simple docstring"""






							def __init__(    self			:  str		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Dict					):
													super().__init__()
													UpperCAmelCase__												=     nf
													UpperCAmelCase__												=     nn.Parameter(torch.empty(lowerCamelCase_		,			lowerCamelCase_					)					)
													UpperCAmelCase__												=     nn.Parameter(torch.zeros(lowerCamelCase_					)					)
													nn.init.normal_(self.weight		,			std=0.02					)






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Tuple					):
													UpperCAmelCase__												=     x.size()[:-1] + (self.nf,)
													UpperCAmelCase__												=     torch.addmm(self.bias		,			x.view(-1		,			x.size(-1					)					)		,			self.weight					)
													UpperCAmelCase__												=     x.view(lowerCamelCase_					)
													return x






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 1			):





						"""simple docstring"""

						UpperCAmelCase__												=     index.to(layer.weight.device			)
						UpperCAmelCase__												=     layer.weight.index_select(_UpperCamelCase						,      _UpperCamelCase			).clone().detach()
						if dim == 0:
												UpperCAmelCase__												=     layer.bias.clone().detach()
						else:
												UpperCAmelCase__												=     layer.bias[index].clone().detach()
						UpperCAmelCase__												=     list(layer.weight.size()			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     ConvaD(new_size[1]						,      new_size[0]			).to(layer.weight.device			)
						UpperCAmelCase__												=     False
						new_layer.weight.copy_(W.contiguous()			)
						UpperCAmelCase__												=     True
						UpperCAmelCase__												=     False
						new_layer.bias.copy_(b.contiguous()			)
						UpperCAmelCase__												=     True
						return new_layer






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = None			):





						"""simple docstring"""

						if isinstance(_UpperCamelCase						,      nn.Linear			):
												return prune_linear_layer(_UpperCamelCase						,      _UpperCamelCase						,      dim=0 if dim is None else dim			)
						elif isinstance(_UpperCamelCase						,      _UpperCamelCase			):
												return prune_convad_layer(_UpperCamelCase						,      _UpperCamelCase						,      dim=1 if dim is None else dim			)
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}"""			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      *_UpperCamelCase			):





						"""simple docstring"""

						assert len(_UpperCamelCase			) > 0, F"""{input_tensors} has to be a tuple/list of tensors"""

						# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
						UpperCAmelCase__												=     len(inspect.signature(_UpperCamelCase			).parameters			)
						if num_args_in_forward_chunk_fn != len(_UpperCamelCase			):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_UpperCamelCase			)} input """
												    '''tensors are given'''			)

						if chunk_size > 0:
												UpperCAmelCase__												=     input_tensors[0].shape[chunk_dim]
												for input_tensor in input_tensors:
																		if input_tensor.shape[chunk_dim] != tensor_shape:
																								raise ValueError(
																								    F"""All input tenors have to be of the same shape: {tensor_shape}, """
																								    F"""found shape {input_tensor.shape[chunk_dim]}"""			)

												if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																		raise ValueError(
																		    F"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
																		    F"""size {chunk_size}"""			)

												UpperCAmelCase__												=     input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												UpperCAmelCase__												=     tuple(input_tensor.chunk(_UpperCamelCase						,      dim=_UpperCamelCase			) for input_tensor in input_tensors			)
												# apply forward fn to every tuple
												UpperCAmelCase__												=     tuple(forward_fn(*_UpperCamelCase			) for input_tensors_chunk in zip(*_UpperCamelCase			)			)
												# concatenate output at same dimension
												return torch.cat(_UpperCamelCase						,      dim=_UpperCamelCase			)

						return forward_fn(*_UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     torch.ones(_UpperCamelCase						,      _UpperCamelCase			)
						UpperCAmelCase__												=     set(_UpperCamelCase			) - already_pruned_heads  # Convert to set and remove already pruned heads
						for head in heads:
												# Compute how many pruned heads are before the head and move the index accordingly
												UpperCAmelCase__												=     head - sum(1 if h < head else 0 for h in already_pruned_heads			)
												UpperCAmelCase__												=     0
						UpperCAmelCase__												=     mask.view(-1			).contiguous().eq(1			)
						UpperCAmelCase__												=     torch.arange(len(_UpperCamelCase			)			)[mask].long()
						return heads, index






def        a__			(							*_UpperCamelCase						,      _UpperCamelCase = None			):





						"""simple docstring"""

						return torch.meshgrid(*_UpperCamelCase						,      indexing=_UpperCamelCase			)






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						return tensor.device, storage_ptr(_UpperCamelCase			), storage_size(_UpperCamelCase			)