
'''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__		: Optional[Any]  							=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a__		: int  							=							[]
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				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      __A													=      state_dict.pop(UpperCAmelCase						)
      __A													=      val





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







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

      return new_state_dict





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=False						)->      Dict:







      """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_5_6, :]
            __A													=      in_proj_bias[:2_5_6]
            __A													=      in_proj_weight[2_5_6:5_1_2, :]
            __A													=      in_proj_bias[2_5_6:5_1_2]
            __A													=      in_proj_weight[-2_5_6:, :]
            __A													=      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
            __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_5_6, :]
            __A													=      in_proj_bias[:2_5_6]
            __A													=      in_proj_weight[2_5_6:5_1_2, :]
            __A													=      in_proj_bias[2_5_6:5_1_2]
            __A													=      in_proj_weight[-2_5_6:, :]
            __A													=      in_proj_bias[-2_5_6:]
            # 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_5_6, :]
            __A													=      in_proj_bias_cross_attn[:2_5_6]
            __A													=      in_proj_weight_cross_attn[2_5_6:5_1_2, :]
            __A													=      in_proj_bias_cross_attn[2_5_6:5_1_2]
            __A													=      in_proj_weight_cross_attn[-2_5_6:, :]
            __A													=      in_proj_bias_cross_attn[-2_5_6:]





def 							snake_case				(		)->      int:







      """simple docstring"""
      __A													=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      __A													=      Image.open(requests.get(UpperCAmelCase   ,	stream=UpperCAmelCase						).raw						)

      return im





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      int:







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

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

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

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

      # load original model from torch hub
      __A													=      torch.hub.load('facebookresearch/detr'   ,	UpperCAmelCase   ,	pretrained=UpperCAmelCase						).eval()
      __A													=      detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  __A													=      'detr.' + src
            rename_key(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)
      __A													=      rename_backbone_keys(UpperCAmelCase						)
      # query, key and value matrices need special treatment
      read_in_q_k_v(UpperCAmelCase   ,	is_panoptic=UpperCAmelCase						)
      # 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(UpperCAmelCase						)
                        __A													=      val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        __A													=      state_dict.pop(UpperCAmelCase						)
                        __A													=      val
                  elif key.startswith('bbox_attention'						) or key.startswith('mask_head'						):
                        continue
                  else:
                        __A													=      state_dict.pop(UpperCAmelCase						)
                        __A													=      val
            else:
                  if not key.startswith('class_labels_classifier'						) and not key.startswith('bbox_predictor'						):
                        __A													=      state_dict.pop(UpperCAmelCase						)
                        __A													=      val
    # finally, create HuggingFace model and load state dict
      __A													=      DetrForSegmentation(UpperCAmelCase						) if is_panoptic else DetrForObjectDetection(UpperCAmelCase						)
      model.load_state_dict(UpperCAmelCase						)
      model.eval()
      # verify our conversion
      __A													=      detr(UpperCAmelCase						)
      __A													=      model(UpperCAmelCase						)
      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(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
      model.save_pretrained(UpperCAmelCase						)
      image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							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__		: Any  							=							parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)














'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
