


'''simple docstring'''






import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
a_					:    str          =    logging.get_logger(__name__)






def 	_A				(lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     if "resnet-50" in model_name:
          _a			      =							ResNetConfig.from_pretrained('microsoft/resnet-50'		)
     elif "resnet-101" in model_name:
          _a			      =							ResNetConfig.from_pretrained('microsoft/resnet-101'		)
     else:
          raise ValueError('Model name should include either resnet50 or resnet101'		)

     _a			      =							DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,						backbone_config=SCREAMING_SNAKE_CASE__		)

     # set label attributes
     _a			      =							"""panoptic""" in model_name
     if is_panoptic:
          _a			      =							2_50
     else:
          _a			      =							91
          _a			      =							"""huggingface/label-files"""
          _a			      =							"""coco-detection-id2label.json"""
          _a			      =							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						repo_type='dataset'		)				,						'r'		)		)
          _a			      =							{int(SCREAMING_SNAKE_CASE__		): v for k, v in idalabel.items()}
          _a			      =							idalabel
          _a			      =							{v: k for k, v in idalabel.items()}

     return config, is_panoptic






def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  int:




     '''simple docstring'''



     _a			      =							[]

     # stem
     # fmt: off
     rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')		)
     rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')		)
     rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')		)
     rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')		)
     rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')		)
     # stages
     for stage_idx in range(len(config.backbone_config.depths		)		):
          for layer_idx in range(config.backbone_config.depths[stage_idx]		):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                        )		)
               # 3 convs
               for i in range(3		):
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                        )		)
                    rename_keys.append(
                        (
                            f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                            f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                        )		)
    # fmt: on

     for i in range(config.encoder_layers		):
          # 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'),
         ]		)

     return rename_keys






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Dict		)   ->  int:




     '''simple docstring'''



     _a			      =							state_dict.pop(SCREAMING_SNAKE_CASE__		)
     _a			      =							val






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :str=False		)   ->  Optional[Any]:




     '''simple docstring'''



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






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




     '''simple docstring'''



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

     return im






@torch.no_grad()
def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :Optional[Any]=None				,						lowerCAmelCase__  :List[Any]=False		)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							get_detr_config(SCREAMING_SNAKE_CASE__		)

     # load original model from torch hub
     _a			      =							{
         """detr-resnet-50""": """detr_resnet50""",
         """detr-resnet-101""": """detr_resnet101""",
     }
     logger.info(f'Converting model {model_name}...'		)
     _a			      =							torch.hub.load('facebookresearch/detr'				,						model_name_to_original_name[model_name]				,						pretrained=SCREAMING_SNAKE_CASE__		).eval()
     _a			      =							detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__		):
          if is_panoptic:
               _a			      =							"""detr.""" + src
          rename_key(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__		)
     # query, key and value matrices need special treatment
     read_in_q_k_v(SCREAMING_SNAKE_CASE__				,						is_panoptic=SCREAMING_SNAKE_CASE__		)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _a			      =							"""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'		)
               ):
                    _a			      =							state_dict.pop(SCREAMING_SNAKE_CASE__		)
                    _a			      =							val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    _a			      =							state_dict.pop(SCREAMING_SNAKE_CASE__		)
                    _a			      =							val
               elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
                    continue
               else:
                    _a			      =							state_dict.pop(SCREAMING_SNAKE_CASE__		)
                    _a			      =							val
          else:
               if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
                    _a			      =							state_dict.pop(SCREAMING_SNAKE_CASE__		)
                    _a			      =							val

    # finally, create HuggingFace model and load state dict
     _a			      =							DetrForSegmentation(SCREAMING_SNAKE_CASE__		) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__		)
     model.load_state_dict(SCREAMING_SNAKE_CASE__		)
     model.eval()

     # verify our conversion on an image
     _a			      =							"""coco_panoptic""" if is_panoptic else """coco_detection"""
     _a			      =							DetrImageProcessor(format=SCREAMING_SNAKE_CASE__		)

     _a			      =							processor(images=prepare_img()				,						return_tensors='pt'		)
     _a			      =							encoding["""pixel_values"""]

     _a			      =							detr(SCREAMING_SNAKE_CASE__		)
     _a			      =							model(SCREAMING_SNAKE_CASE__		)

     assert torch.allclose(outputs.logits				,						original_outputs['pred_logits']				,						atol=1E-3		)
     assert torch.allclose(outputs.pred_boxes				,						original_outputs['pred_boxes']				,						atol=1E-3		)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks				,						original_outputs['pred_masks']				,						atol=1E-4		)
     print('Looks ok!'		)

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
          Path(SCREAMING_SNAKE_CASE__		).mkdir(exist_ok=SCREAMING_SNAKE_CASE__		)
          model.save_pretrained(SCREAMING_SNAKE_CASE__		)
          processor.save_pretrained(SCREAMING_SNAKE_CASE__		)

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info('Uploading PyTorch model and image processor to the hub...'		)
          model.push_to_hub(f'nielsr/{model_name}'		)
          processor.push_to_hub(f'nielsr/{model_name}'		)


if __name__ == "__main__":
       a_					:    Any          =    argparse.ArgumentParser()

       parser.add_argument(
           "--model_name",
           default="detr-resnet-50",
           type=str,
           choices=["detr-resnet-50", "detr-resnet-101"],
           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."
       )
       parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
       a_					:    Optional[int]          =    parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

