




'''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()
A					       =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A					       =[]
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 		snake_case_				(_a      :				int       ,  _a      :				int       ,  _a      :				Optional[Any]	):
    UpperCAmelCase     			=						state_dict.pop(_a	)
    UpperCAmelCase     			=						val





def 		snake_case_				(_a      :				Tuple	):
    UpperCAmelCase     			=						OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCAmelCase     			=						key.replace('''backbone.0.body'''       ,  '''backbone.conv_encoder.model'''	)
            UpperCAmelCase     			=						value
        else:
            UpperCAmelCase     			=						value

    return new_state_dict





def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				Optional[int]=False	):
    UpperCAmelCase     			=						''''''
    if is_panoptic:
        UpperCAmelCase     			=						'''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)
        UpperCAmelCase     			=						state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
        UpperCAmelCase     			=						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
        UpperCAmelCase     			=						in_proj_weight[:2_5_6, :]
        UpperCAmelCase     			=						in_proj_bias[:2_5_6]
        UpperCAmelCase     			=						in_proj_weight[2_5_6:5_1_2, :]
        UpperCAmelCase     			=						in_proj_bias[2_5_6:5_1_2]
        UpperCAmelCase     			=						in_proj_weight[-2_5_6:, :]
        UpperCAmelCase     			=						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
        UpperCAmelCase     			=						state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
        UpperCAmelCase     			=						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
        UpperCAmelCase     			=						in_proj_weight[:2_5_6, :]
        UpperCAmelCase     			=						in_proj_bias[:2_5_6]
        UpperCAmelCase     			=						in_proj_weight[2_5_6:5_1_2, :]
        UpperCAmelCase     			=						in_proj_bias[2_5_6:5_1_2]
        UpperCAmelCase     			=						in_proj_weight[-2_5_6:, :]
        UpperCAmelCase     			=						in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        UpperCAmelCase     			=						state_dict.pop(
            F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
        UpperCAmelCase     			=						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
        UpperCAmelCase     			=						in_proj_weight_cross_attn[:2_5_6, :]
        UpperCAmelCase     			=						in_proj_bias_cross_attn[:2_5_6]
        UpperCAmelCase     			=						in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        UpperCAmelCase     			=						in_proj_bias_cross_attn[2_5_6:5_1_2]
        UpperCAmelCase     			=						in_proj_weight_cross_attn[-2_5_6:, :]
        UpperCAmelCase     			=						in_proj_bias_cross_attn[-2_5_6:]





def 		snake_case_				():
    UpperCAmelCase     			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase     			=						Image.open(requests.get(_a       ,  stream=_a	).raw	)

    return im





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

    # load image processor
    UpperCAmelCase     			=						'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCAmelCase     			=						DetrImageProcessor(format=_a	)

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

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

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










'''simple docstring'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




