

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__							=				logging.get_logger(__name__)




def        _lowercase  (					a_					:       Optional[int]					)					->						Any:




					'''simple docstring'''







					if "backbone.0.body" in orig_key:
										__magic_name__				=     orig_key.replace('backbone.0.body'       ,'backbone.conv_encoder.model'					)
					if "transformer" in orig_key:
										__magic_name__				=     orig_key.replace('transformer.'       ,''					)
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															__magic_name__				=     orig_key.replace('norm1'       ,'self_attn_layer_norm'					)
										else:
															__magic_name__				=     orig_key.replace('norm1'       ,'encoder_attn_layer_norm'					)
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															__magic_name__				=     orig_key.replace('norm2'       ,'final_layer_norm'					)
										else:
															__magic_name__				=     orig_key.replace('norm2'       ,'self_attn_layer_norm'					)
					if "norm3" in orig_key:
										__magic_name__				=     orig_key.replace('norm3'       ,'final_layer_norm'					)
					if "linear1" in orig_key:
										__magic_name__				=     orig_key.replace('linear1'       ,'fc1'					)
					if "linear2" in orig_key:
										__magic_name__				=     orig_key.replace('linear2'       ,'fc2'					)
					if "query_embed" in orig_key:
										__magic_name__				=     orig_key.replace('query_embed'       ,'query_position_embeddings'					)
					if "cross_attn" in orig_key:
										__magic_name__				=     orig_key.replace('cross_attn'       ,'encoder_attn'					)

					return orig_key




def        _lowercase  (					a_					:       Dict					)					->						Dict:




					'''simple docstring'''







					for i in range(6					):
										# read in weights + bias of input projection layer of self-attention
										__magic_name__				=     state_dict.pop(F'decoder.layers.{i}.self_attn.in_proj_weight'					)
										__magic_name__				=     state_dict.pop(F'decoder.layers.{i}.self_attn.in_proj_bias'					)
										# next, add query, keys and values (in that order) to the state dict
										__magic_name__				=     in_proj_weight[:2_5_6, :]
										__magic_name__				=     in_proj_bias[:2_5_6]
										__magic_name__				=     in_proj_weight[2_5_6:5_1_2, :]
										__magic_name__				=     in_proj_bias[2_5_6:5_1_2]
										__magic_name__				=     in_proj_weight[-2_5_6:, :]
										__magic_name__				=     in_proj_bias[-2_5_6:]




def        _lowercase  (					)					->						str:




					'''simple docstring'''







					__magic_name__				=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__				=     Image.open(requests.get(a__       ,stream=a__					).raw					)

					return im




@torch.no_grad()
def        _lowercase  (					a_					:       Any       ,a_					:       Dict       ,a_					:       Optional[int]       ,a_					:       List[Any]       ,a_					:       Union[str, Any]       ,a_					:       int       ,a_					:       Optional[int]       ,)					->						Any:




					'''simple docstring'''







					__magic_name__				=     DeformableDetrConfig()
					# set config attributes
					if single_scale:
										__magic_name__				=     1
					__magic_name__				=     dilation
					__magic_name__				=     with_box_refine
					__magic_name__				=     two_stage
					# set labels
					__magic_name__				=     9_1
					__magic_name__				=     '''huggingface/label-files'''
					__magic_name__				=     '''coco-detection-id2label.json'''
					__magic_name__				=     json.load(open(cached_download(hf_hub_url(a__       ,a__       ,repo_type='dataset'					)					)       ,'r'					)					)
					__magic_name__				=     {int(a__					): v for k, v in idalabel.items()}
					__magic_name__				=     idalabel
					__magic_name__				=     {v: k for k, v in idalabel.items()}

					# load image processor
					__magic_name__				=     DeformableDetrImageProcessor(format='coco_detection'					)

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

					logger.info('Converting model...'					)

					# load original state dict
					__magic_name__				=     torch.load(a__       ,map_location='cpu'					)['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__magic_name__				=     state_dict.pop(a__					)
										__magic_name__				=     val
					# query, key and value matrices need special treatment
					read_in_q_k_v(a__					)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__magic_name__				=     '''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('class_embed'					) and not key.startswith('bbox_embed'					):
															__magic_name__				=     state_dict.pop(a__					)
															__magic_name__				=     val
    # finally, create HuggingFace model and load state dict
					__magic_name__				=     DeformableDetrForObjectDetection(a__					)
					model.load_state_dict(a__					)
					model.eval()

					__magic_name__				=     '''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(a__					)
					# verify our conversion
					__magic_name__				=     model(pixel_values.to(a__					)					)

					__magic_name__				=     torch.tensor(
					    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]					)
					__magic_name__				=     torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]					)

					if single_scale:
										__magic_name__				=     torch.tensor(
										    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]					)
										__magic_name__				=     torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]					)

					if single_scale and dilation:
										__magic_name__				=     torch.tensor(
										    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]					)
										__magic_name__				=     torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]					)

					if with_box_refine:
										__magic_name__				=     torch.tensor(
										    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]					)
										__magic_name__				=     torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]					)

					if with_box_refine and two_stage:
										__magic_name__				=     torch.tensor(
										    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]					)
										__magic_name__				=     torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]					)

					print('Logits:'       ,outputs.logits[0, :3, :3]					)

					assert torch.allclose(outputs.logits[0, :3, :3]       ,expected_logits.to(a__					)       ,atol=1e-4					)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,expected_boxes.to(a__					)       ,atol=1e-4					)

					print('Everything ok!'					)

					# 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__					)

					# Push to hub
					if push_to_hub:
										__magic_name__				=     '''deformable-detr'''
										model_name += "-single-scale" if single_scale else ""
										model_name += "-dc5" if dilation else ""
										model_name += "-with-box-refine" if with_box_refine else ""
										model_name += "-two-stage" if two_stage else ""
										print('Pushing model to hub...'					)
										model.push_to_hub(repo_path_or_name=a__       ,organization='nielsr'       ,commit_message='Add model'					)


if __name__ == "__main__":
						A__							=				argparse.ArgumentParser()

						parser.add_argument(
						    "--checkpoint_path",
						    type=str,
						    default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
						    help="Path to Pytorch checkpoint (.pth file) you\'d like to convert.",
						)
						parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
						parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
						parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
						parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the folder to output PyTorch model.",
						)
						parser.add_argument(
						    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
						)
						A__							=				parser.parse_args()
						convert_deformable_detr_checkpoint(
						    args.checkpoint_path,
						    args.single_scale,
						    args.dilation,
						    args.with_box_refine,
						    args.two_stage,
						    args.pytorch_dump_folder_path,
						    args.push_to_hub,
						)






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__				:      Tuple    		=     [nn.LayerNorm]

a__				:      Tuple    		=     logging.get_logger(__name__)

a__				:      int    		=     version.parse(version.parse(torch.__version__).base_version)

a__				:      Dict    		=     parsed_torch_version_base >= version.parse('''2.0''')
a__				:      Any    		=     parsed_torch_version_base >= version.parse('''1.12''')
a__				:      Union[str, Any]    		=     parsed_torch_version_base >= version.parse('''1.11''')
a__				:      Union[str, Any]    		=     parsed_torch_version_base < version.parse('''1.11''')







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ ,						self   ):



    """simple docstring"""



    from torch import _softmax_backward_data

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







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



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = index.to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = layer.weight.index_select(a__ ,						a__   ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            SCREAMING_SNAKE_CASE				:				int				      = layer.bias.clone().detach()
        else:
            SCREAMING_SNAKE_CASE				:				List[Any]				      = layer.bias[index].clone().detach()
    SCREAMING_SNAKE_CASE				:				List[Any]				      = list(layer.weight.size()   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = len(a__   )
    SCREAMING_SNAKE_CASE				:				str				      = nn.Linear(new_size[1] ,						new_size[0] ,						bias=layer.bias is not None   ).to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = False
    new_layer.weight.copy_(W.contiguous()   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = True
    if layer.bias is not None:
        SCREAMING_SNAKE_CASE				:				Dict				      = False
        new_layer.bias.copy_(b.contiguous()   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = True
    return new_layer






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->int:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				List[str]				      = nf
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Parameter(torch.empty(_lowerCamelCase				,       _lowerCamelCase   )   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = nn.Parameter(torch.zeros(_lowerCamelCase   )   )
           nn.init.normal_(self.weight				,       std=0.0_2   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = x.size()[:-1] + (self.nf,)
           SCREAMING_SNAKE_CASE				:				int				      = torch.addmm(self.bias				,       x.view(-1				,       x.size(-1   )   )				,       self.weight   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = x.view(_lowerCamelCase   )
           return x







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



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = index.to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Any				      = layer.weight.index_select(a__ ,						a__   ).clone().detach()
    if dim == 0:
        SCREAMING_SNAKE_CASE				:				Tuple				      = layer.bias.clone().detach()
    else:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = layer.bias[index].clone().detach()
    SCREAMING_SNAKE_CASE				:				int				      = list(layer.weight.size()   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = len(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = ConvaD(new_size[1] ,						new_size[0]   ).to(layer.weight.device   )
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = False
    new_layer.weight.copy_(W.contiguous()   )
    SCREAMING_SNAKE_CASE				:				int				      = True
    SCREAMING_SNAKE_CASE				:				Any				      = False
    new_layer.bias.copy_(b.contiguous()   )
    SCREAMING_SNAKE_CASE				:				int				      = True
    return new_layer







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



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						*a__   ):



    """simple docstring"""



    assert len(a__   ) > 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
    SCREAMING_SNAKE_CASE				:				str				      = len(inspect.signature(a__   ).parameters   )
    if num_args_in_forward_chunk_fn != len(a__   ):
        raise ValueError(
            F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a__   )} input """
            '''tensors are given'''   )

    if chunk_size > 0:
        SCREAMING_SNAKE_CASE				:				Dict				      = 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}"""   )

        SCREAMING_SNAKE_CASE				:				Any				      = input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = tuple(input_tensor.chunk(a__ ,						dim=a__   ) for input_tensor in input_tensors   )
        # apply forward fn to every tuple
        SCREAMING_SNAKE_CASE				:				Tuple				      = tuple(forward_fn(*a__   ) for input_tensors_chunk in zip(*a__   )   )
        # concatenate output at same dimension
        return torch.cat(a__ ,						dim=a__   )

    return forward_fn(*a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.ones(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = set(a__   ) - 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
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = head - sum(1 if h < head else 0 for h in already_pruned_heads   )
        SCREAMING_SNAKE_CASE				:				str				      = 0
    SCREAMING_SNAKE_CASE				:				List[str]				      = mask.view(-1   ).contiguous().eq(1   )
    SCREAMING_SNAKE_CASE				:				torch.LongTensor				      = torch.arange(len(a__   )   )[mask].long()
    return heads, index







def 				UpperCAmelCase_(    *a__ ,						a__ = None   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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