

'''simple docstring'''

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_     =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_     =      []
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      __magic_name__							(			A       , A       , A				)      ->		str:
	snake_case							=					state_dict.pop(A				)
	snake_case							=					val

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			snake_case							=					key.replace('backbone.0.body'       , 'backbone.conv_encoder.model'				)
			snake_case							=					value
		else:
			snake_case							=					value

	return new_state_dict

def      __magic_name__							(			A       , A=False				)      ->		Dict:
	snake_case							=					''
	if is_panoptic:
		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)
		snake_case							=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
		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
		snake_case							=					in_proj_weight[:2_5_6, :]
		snake_case							=					in_proj_bias[:2_5_6]
		snake_case							=					in_proj_weight[2_5_6:5_1_2, :]
		snake_case							=					in_proj_bias[2_5_6:5_1_2]
		snake_case							=					in_proj_weight[-2_5_6:, :]
		snake_case							=					in_proj_bias[-2_5_6:]
	# 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							=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
		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
		snake_case							=					in_proj_weight[:2_5_6, :]
		snake_case							=					in_proj_bias[:2_5_6]
		snake_case							=					in_proj_weight[2_5_6:5_1_2, :]
		snake_case							=					in_proj_bias[2_5_6:5_1_2]
		snake_case							=					in_proj_weight[-2_5_6:, :]
		snake_case							=					in_proj_bias[-2_5_6:]
		# read in weights + bias of input projection layer of cross-attention
		snake_case							=					state_dict.pop(
		    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
		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
		snake_case							=					in_proj_weight_cross_attn[:2_5_6, :]
		snake_case							=					in_proj_bias_cross_attn[:2_5_6]
		snake_case							=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
		snake_case							=					in_proj_bias_cross_attn[2_5_6:5_1_2]
		snake_case							=					in_proj_weight_cross_attn[-2_5_6:, :]
		snake_case							=					in_proj_bias_cross_attn[-2_5_6:]

def      __magic_name__							(			)      ->		int:
	snake_case							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case							=					Image.open(requests.get(A       , stream=A				).raw				)

	return im

@torch.no_grad()
def      __magic_name__							(			A       , A				)      ->		int:
	snake_case							=					DetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		snake_case							=					'resnet101'
	if "dc5" in model_name:
		snake_case							=					True
	snake_case							=					'panoptic' in model_name
	if is_panoptic:
		snake_case							=					2_5_0
	else:
		snake_case							=					9_1
		snake_case							=					'huggingface/label-files'
		snake_case							=					'coco-detection-id2label.json'
		snake_case							=					json.load(open(hf_hub_download(A       , A       , repo_type='dataset'				)       , 'r'				)				)
		snake_case							=					{int(A				): v for k, v in idalabel.items()}
		snake_case							=					idalabel
		snake_case							=					{v: k for k, v in idalabel.items()}

	# load image processor
	snake_case							=					'coco_panoptic' if is_panoptic else 'coco_detection'
	snake_case							=					DetrImageProcessor(format=A				)

	# prepare image
	snake_case							=					prepare_img()
	snake_case							=					image_processor(images=A       , return_tensors='pt'				)
	snake_case							=					encoding['pixel_values']

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

	# load original model from torch hub
	snake_case							=					torch.hub.load('facebookresearch/detr'       , A       , pretrained=A				).eval()
	snake_case							=					detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			snake_case							=					'detr.' + src
		rename_key(A       , A       , A				)
	snake_case							=					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
	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'				)
			):
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
			elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
				continue
			else:
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
		else:
			if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
    # finally, create HuggingFace model and load state dict
	snake_case							=					DetrForSegmentation(A				) if is_panoptic else DetrForObjectDetection(A				)
	model.load_state_dict(A				)
	model.eval()
	# verify our conversion
	snake_case							=					detr(A				)
	snake_case							=					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__":
						lowerCAmelCase_     =      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_     =      parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)











import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case=None					)	->			str:

			'''simple docstring'''


			UpperCamelCase_     				= None
			# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
			if str(weight.dtype					) == "torch.bfloat16":
						# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
						UpperCamelCase_     				= weight.view(torch.intaa					)
						UpperCamelCase_     				= 'bfloat16'
			UpperCamelCase_     				= weight.cpu().numpy()
			UpperCamelCase_     				= os.path.join(__snake_case     ,		F'''{weight_name}.dat'''					)
			if index is not None:
						if dtype is None:
									UpperCamelCase_     				= str(array.dtype					)
						UpperCamelCase_     				= {'dtype': dtype, 'shape': list(array.shape					)}
			if array.ndim == 0:
						UpperCamelCase_     				= array[None]
			UpperCamelCase_     				= np.memmap(__snake_case     ,		dtype=array.dtype     ,		mode='w+'     ,		shape=array.shape					)
			UpperCamelCase_     				= array[:]
			file_array.flush()
			return index




def  a_		(							__snake_case     ,		__snake_case					)	->			Dict:

			'''simple docstring'''


			UpperCamelCase_     				= tuple(weight_info['shape']					)
			if shape == ():
						# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
						UpperCamelCase_     				= (1,)

			UpperCamelCase_     				= weight_info['dtype']
			if dtype == "bfloat16":
						# NumPy does not support bfloat16 so this was saved as a int16
						UpperCamelCase_     				= 'int16'

			UpperCamelCase_     				= np.memmap(__snake_case     ,		dtype=__snake_case     ,		shape=__snake_case     ,		mode='r'					)

			if len(weight_info['shape']					) == 0:
						UpperCamelCase_     				= weight[0]
			UpperCamelCase_     				= torch.tensor(__snake_case					)
			if weight_info["dtype"] == "bfloat16":
						UpperCamelCase_     				= weight.view(torch.bfloataa					)

			return weight




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			if index is None or len(__snake_case					) == 0:
						# Nothing to save
						return

			UpperCamelCase_     				= os.path.join(__snake_case     ,		'index.json'					)
			if os.path.isfile(__snake_case					):
						with open(__snake_case     ,		'r'     ,		encoding='utf-8'					) as f:
									UpperCamelCase_     				= json.load(__snake_case					)
			else:
						UpperCamelCase_     				= {}
			current_index.update(__snake_case					)

			with open(__snake_case     ,		'w'     ,		encoding='utf-8'					) as f:
						json.dump(__snake_case     ,		__snake_case     ,		indent=2					)




def  a_		(							__snake_case     ,		__snake_case					)	->			Tuple:

			'''simple docstring'''


			os.makedirs(__snake_case     ,		exist_ok=__snake_case					)
			UpperCamelCase_     				= {}
			for name, parameter in state_dict.items():
						UpperCamelCase_     				= offload_weight(__snake_case     ,		__snake_case     ,		__snake_case     ,		index=__snake_case					)

			# Update index
			save_offload_index(__snake_case     ,		__snake_case					)




class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Mapping   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= dataset
										UpperCamelCase_     				= prefix







							def __getitem__(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict				)		->					Tuple:

										"""simple docstring"""



										return self.dataset[f'''{self.prefix}{key}''']







							def __iter__(    self   :	int				)		->					List[str]:

										"""simple docstring"""



										return iter([key for key in self.dataset if key.startswith(self.prefix				)]				)







							def __len__(    self   :	Any				)		->					Optional[Any]:

										"""simple docstring"""



										return len(self.dataset				)







class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Dict[str, torch.Tensor] = None   ,     __UpperCAmelCase   :	Optional[Union[str, os.PathLike]] = None   ,     __UpperCAmelCase   :	Mapping = None   ,     __UpperCAmelCase   :	Optional[int]=None   ,     )		->					List[str]:

										"""simple docstring"""



										if state_dict is None and save_folder is None:
													raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'				)

										UpperCamelCase_     				= {} if state_dict is None else state_dict
										UpperCamelCase_     				= save_folder
										if index is None and save_folder is not None:
													with open(os.path.join(__UpperCAmelCase   ,     'index.json'				)				) as f:
																UpperCamelCase_     				= json.load(__UpperCAmelCase				)
										UpperCamelCase_     				= {} if index is None else index
										UpperCamelCase_     				= list(self.state_dict.keys()				)
										self.all_keys.extend([key for key in self.index if key not in self.all_keys]				)
										UpperCamelCase_     				= device







							def __getitem__(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										if key in self.state_dict:
													return self.state_dict[key]
										UpperCamelCase_     				= self.index[key]
										if weight_info.get('safetensors_file'				) is not None:
													if not is_safetensors_available():
																raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'				)

													from safetensors import safe_open

													UpperCamelCase_     				= 'cpu' if self.device is None else self.device
													with safe_open(weight_info['safetensors_file']   ,     framework='pt'   ,     device=__UpperCAmelCase				) as f:
																UpperCamelCase_     				= f.get_tensor(weight_info.get('weight_name'   ,     __UpperCAmelCase				)				)

													if "dtype" in weight_info:
																return tensor.to(getattr(__UpperCAmelCase   ,     weight_info['dtype']				)				)
													else:
																return tensor

										UpperCamelCase_     				= os.path.join(self.save_folder   ,     f'''{key}.dat'''				)
										return load_offloaded_weight(__UpperCAmelCase   ,     __UpperCAmelCase				)







							def __iter__(    self   :	Optional[int]				)		->					Union[str, Any]:

										"""simple docstring"""



										return iter(self.all_keys				)







							def __len__(    self   :	Any				)		->					Any:

										"""simple docstring"""



										return len(self.all_keys				)







def  a_		(							__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			for module_name in submodule_names:
						# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
						# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
						result.update(
						    {
						        key: param
						        for key, param in state_dict.items()
						        if key == module_name or key.startswith(module_name + '.'					)
						    }					)
			return result
