

'''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()
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 			__UpperCamelCase							(		lowercase__  :					Tuple,   lowercase__  :					Dict,   lowercase__  :					List[Any]	):






     '''simple docstring'''





     __lowercase     						=state_dict.pop(lowercase__	)
     __lowercase     						=val

def 			__UpperCamelCase							(		lowercase__  :					List[str]	):






     '''simple docstring'''





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

     return new_state_dict

def 			__UpperCamelCase							(		lowercase__  :					int,   lowercase__  :					List[Any]=False	):






     '''simple docstring'''





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

def 			__UpperCamelCase							(		):






     '''simple docstring'''





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

     return im

@torch.no_grad()
def 			__UpperCamelCase							(		lowercase__  :					str,   lowercase__  :					Any	):






     '''simple docstring'''





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

     # load image processor
     __lowercase     						='coco_panoptic' if is_panoptic else 'coco_detection'
     __lowercase     						=DetrImageProcessor(format=lowercase__	)

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

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

     # load original model from torch hub
     __lowercase     						=torch.hub.load('facebookresearch/detr',   lowercase__,   pretrained=lowercase__	).eval()
     __lowercase     						=detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               __lowercase     						='detr.' + src
          rename_key(lowercase__,   lowercase__,   lowercase__	)
     __lowercase     						=rename_backbone_keys(lowercase__	)
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase__,   is_panoptic=lowercase__	)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     __lowercase     						='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'	)
               ):
                    __lowercase     						=state_dict.pop(lowercase__	)
                    __lowercase     						=val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __lowercase     						=state_dict.pop(lowercase__	)
                    __lowercase     						=val
               elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
                    continue
               else:
                    __lowercase     						=state_dict.pop(lowercase__	)
                    __lowercase     						=val
          else:
               if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
                    __lowercase     						=state_dict.pop(lowercase__	)
                    __lowercase     						=val
    # finally, create HuggingFace model and load state dict
     __lowercase     						=DetrForSegmentation(lowercase__	) if is_panoptic else DetrForObjectDetection(lowercase__	)
     model.load_state_dict(lowercase__	)
     model.eval()
     # verify our conversion
     __lowercase     						=detr(lowercase__	)
     __lowercase     						=model(lowercase__	)
     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(lowercase__	).mkdir(exist_ok=lowercase__	)
     model.save_pretrained(lowercase__	)
     image_processor.save_pretrained(lowercase__	)


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'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







       def __init__(							self     :	List[Any]  , __lowercase     :	bool = True  , __lowercase     :	Optional[Dict[str, int]] = None  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	bool = True  , __lowercase     :	Union[int, float] = 1 / 255  , __lowercase     :	bool = True  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , **__lowercase     :	List[Any]  , ):







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            return rescale(__lowercase  , scale=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







            return normalize(__lowercase  , mean=__lowercase  , std=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	ImageInput  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Dict[str, int] = None  , __lowercase     :	PILImageResampling = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[float] = None  , __lowercase     :	Optional[bool] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[float, List[float]]] = None  , __lowercase     :	Optional[Union[str, TensorType]] = None  , __lowercase     :	Union[str, ChannelDimension] = ChannelDimension.FIRST  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase     						=image_mean if image_mean is not None else self.image_mean
            __lowercase     						=image_std if image_std is not None else self.image_std

            __lowercase     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.' )

            if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.' )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.' )

            # All transformations expect numpy arrays.
            __lowercase     						=[to_numpy_array(__lowercase ) for image in images]

            if do_resize:
                 __lowercase     						=[self.resize(image=__lowercase  , size=__lowercase  , resample=__lowercase ) for image in images]

            if do_rescale:
                 __lowercase     						=[self.rescale(image=__lowercase  , scale=__lowercase ) for image in images]

            if do_normalize:
                 __lowercase     						=[self.normalize(image=__lowercase  , mean=__lowercase  , std=__lowercase ) for image in images]

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

            __lowercase     						={'pixel_values': images}
            return BatchFeature(data=__lowercase  , tensor_type=__lowercase )


