'''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()
__SCREAMING_SNAKE_CASE :  List[Any]  =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__SCREAMING_SNAKE_CASE :  List[Any]  =						[]
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_							(      _UpperCAmelCase						:   Any    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   Union[str, Any]					)					->					List[str]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					state_dict.pop(_UpperCAmelCase					)
       _UpperCAmelCase  :						List[str]							   =					val

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Any					)					->					str:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     _UpperCAmelCase  :						List[str]							   =					key.replace("backbone.0.body"    ,			"backbone.conv_encoder.model"					)
                     _UpperCAmelCase  :						Union[str, Any]							   =					value
              else:
                     _UpperCAmelCase  :						Tuple							   =					value

       return new_state_dict

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Any=False					)					->					Optional[int]:


       """simple docstring"""







       _UpperCAmelCase  :						Tuple							   =					""
       if is_panoptic:
              _UpperCAmelCase  :						List[Any]							   =					"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  :						Dict							   =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
              _UpperCAmelCase  :						Union[str, Any]							   =					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  :						List[str]							   =					in_proj_weight[:256, :]
              _UpperCAmelCase  :						Optional[int]							   =					in_proj_bias[:256]
              _UpperCAmelCase  :						List[str]							   =					in_proj_weight[256:512, :]
              _UpperCAmelCase  :						Optional[Any]							   =					in_proj_bias[256:512]
              _UpperCAmelCase  :						Any							   =					in_proj_weight[-256:, :]
              _UpperCAmelCase  :						Optional[int]							   =					in_proj_bias[-256:]
       # 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  :						int							   =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
              _UpperCAmelCase  :						Tuple							   =					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  :						Optional[Any]							   =					in_proj_weight[:256, :]
              _UpperCAmelCase  :						Optional[Any]							   =					in_proj_bias[:256]
              _UpperCAmelCase  :						Any							   =					in_proj_weight[256:512, :]
              _UpperCAmelCase  :						List[str]							   =					in_proj_bias[256:512]
              _UpperCAmelCase  :						Optional[int]							   =					in_proj_weight[-256:, :]
              _UpperCAmelCase  :						int							   =					in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              _UpperCAmelCase  :						Any							   =					state_dict.pop(
                  F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
              _UpperCAmelCase  :						Any							   =					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  :						Any							   =					in_proj_weight_cross_attn[:256, :]
              _UpperCAmelCase  :						Union[str, Any]							   =					in_proj_bias_cross_attn[:256]
              _UpperCAmelCase  :						Dict							   =					in_proj_weight_cross_attn[256:512, :]
              _UpperCAmelCase  :						Dict							   =					in_proj_bias_cross_attn[256:512]
              _UpperCAmelCase  :						Tuple							   =					in_proj_weight_cross_attn[-256:, :]
              _UpperCAmelCase  :						Optional[int]							   =					in_proj_bias_cross_attn[-256:]

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


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       _UpperCAmelCase  :						Tuple							   =					Image.open(requests.get(_UpperCAmelCase    ,			stream=_UpperCAmelCase					).raw					)

       return im

@torch.no_grad()
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Optional[Any]					)					->					str:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              _UpperCAmelCase  :						Tuple							   =					"resnet101"
       if "dc5" in model_name:
              _UpperCAmelCase  :						int							   =					True
       _UpperCAmelCase  :						str							   =					"panoptic" in model_name
       if is_panoptic:
              _UpperCAmelCase  :						Optional[Any]							   =					250
       else:
              _UpperCAmelCase  :						Union[str, Any]							   =					91
              _UpperCAmelCase  :						Optional[int]							   =					"huggingface/label-files"
              _UpperCAmelCase  :						Optional[int]							   =					"coco-detection-id2label.json"
              _UpperCAmelCase  :						Optional[Any]							   =					json.load(open(hf_hub_download(_UpperCAmelCase    ,			_UpperCAmelCase    ,			repo_type="dataset"					)    ,			"r"					)					)
              _UpperCAmelCase  :						Tuple							   =					{int(_UpperCAmelCase					): v for k, v in idalabel.items()}
              _UpperCAmelCase  :						Union[str, Any]							   =					idalabel
              _UpperCAmelCase  :						Optional[int]							   =					{v: k for k, v in idalabel.items()}

       # load image processor
       _UpperCAmelCase  :						int							   =					"coco_panoptic" if is_panoptic else "coco_detection"
       _UpperCAmelCase  :						str							   =					DetrImageProcessor(format=_UpperCAmelCase					)

       # prepare image
       _UpperCAmelCase  :						Union[str, Any]							   =					prepare_img()
       _UpperCAmelCase  :						int							   =					image_processor(images=_UpperCAmelCase    ,			return_tensors="pt"					)
       _UpperCAmelCase  :						Union[str, Any]							   =					encoding["pixel_values"]

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

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


'''simple docstring'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

