



"""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()
_lowerCAmelCase					:Any  =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase					:Union[str, 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 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	int  , UpperCamelCase__	:	List[Any]							):
    _UpperCAmelCase							:						str       							=					state_dict.pop(UpperCamelCase__							)
    _UpperCAmelCase							:						List[Any]       							=					val





def 				lowerCamelCase_		(UpperCamelCase__	:	Dict							):
    _UpperCAmelCase							:						Dict       							=					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							:						List[Any]       							=					value
        else:
            _UpperCAmelCase							:						Any       							=					value

    return new_state_dict





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





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

    return im





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

    # load image processor
    _UpperCAmelCase							:						str       							=					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    _UpperCAmelCase							:						Any       							=					DetrImageProcessor(format=UpperCamelCase__							)

    # prepare image
    _UpperCAmelCase							:						List[Any]       							=					prepare_img()
    _UpperCAmelCase							:						Any       							=					image_processor(images=UpperCamelCase__  , return_tensors='''pt'''							)
    _UpperCAmelCase							:						List[Any]       							=					encoding['''pixel_values''']

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

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







"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import tensorflow as tf
class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):





     '''simple docstring'''



     def __init__(      self ,			A=None ,			**A	)							->      List[str]:
         super().__init__(features=A	)
         _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
         import tensorflow as tf

         if isinstance(A ,			A	) and column:
             if all(
                 isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                 return tf.stack(A	)
             elif all(
                 isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                 for x in column	):
                 # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                 return tf.ragged.stack(A	)

         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      int:
         import tensorflow as tf

         if value is None:
             return value

         _UpperCAmelCase							:						Any       							=					{}

         if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
             _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
         elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
             _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A ,			PIL.Image.Image	):
                 _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)

         return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
         import tensorflow as tf

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(A ,			torch.Tensor	):
                 return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
         if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
             _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(A ,			np.ndarray	):
             if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                 return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
         elif isinstance(A ,			(list, tuple)	):
             return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
         return self._tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
         return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
         _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
         return self.recursive_tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
         _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
         _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
         for column_name in batch:
             _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
         return batch


