






'''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_				      =							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_				      =							[]
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__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	Any									=						state_dict.pop(SCREAMING_SNAKE_CASE__		)
 __a    :	List[Any]									=						val






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 __a    :	Dict									=						OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __a    :	Any									=						key.replace('backbone.0.body'       ,		'backbone.conv_encoder.model'		)
   __a    :	Tuple									=						value
  else:
   __a    :	str									=						value

 return new_state_dict






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






def   lowerCAmelCase__			(					):
 __a    :	Union[str, Any]									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __a    :	Any									=						Image.open(requests.get(SCREAMING_SNAKE_CASE__       ,		stream=SCREAMING_SNAKE_CASE__		).raw		)

 return im






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

 # load image processor
 __a    :	Optional[Any]									=						'coco_panoptic' if is_panoptic else 'coco_detection'
 __a    :	Any									=						DetrImageProcessor(format=SCREAMING_SNAKE_CASE__		)

 # prepare image
 __a    :	List[str]									=						prepare_img()
 __a    :	Optional[int]									=						image_processor(images=SCREAMING_SNAKE_CASE__       ,		return_tensors='pt'		)
 __a    :	Optional[Any]									=						encoding['pixel_values']

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

 # load original model from torch hub
 __a    :	Tuple									=						torch.hub.load('facebookresearch/detr'       ,		SCREAMING_SNAKE_CASE__       ,		pretrained=SCREAMING_SNAKE_CASE__		).eval()
 __a    :	Optional[Any]									=						detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __a    :	Dict									=						'detr.' + src
  rename_key(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
 __a    :	Any									=						rename_backbone_keys(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    :	str									=						'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    :	Optional[Any]									=						state_dict.pop(SCREAMING_SNAKE_CASE__		)
    __a    :	Dict									=						val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __a    :	List[str]									=						state_dict.pop(SCREAMING_SNAKE_CASE__		)
    __a    :	List[str]									=						val
   elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
    continue
   else:
    __a    :	int									=						state_dict.pop(SCREAMING_SNAKE_CASE__		)
    __a    :	Optional[Any]									=						val
  else:
   if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
    __a    :	List[Any]									=						state_dict.pop(SCREAMING_SNAKE_CASE__		)
    __a    :	Any									=						val
    # finally, create HuggingFace model and load state dict
 __a    :	List[Any]									=						DetrForSegmentation(SCREAMING_SNAKE_CASE__		) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__		)
 model.load_state_dict(SCREAMING_SNAKE_CASE__		)
 model.eval()
 # verify our conversion
 __a    :	Optional[int]									=						detr(SCREAMING_SNAKE_CASE__		)
 __a    :	List[str]									=						model(SCREAMING_SNAKE_CASE__		)
 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(SCREAMING_SNAKE_CASE__		).mkdir(exist_ok=SCREAMING_SNAKE_CASE__		)
 model.save_pretrained(SCREAMING_SNAKE_CASE__		)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_				      =							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_				      =							parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)













'''simple docstring'''


from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def   lowerCAmelCase__			(					cls		):
   return cls






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		):
 __a    :	int									=						device
 __a    :	Union[str, Any]									=						shape[0]

 __a    :	Union[str, Any]									=						layout or torch.strided
 __a    :	Any									=						device or torch.device('cpu'		)

 if generator is not None:
  __a    :	Optional[int]									=						generator.device.type if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   __a    :	int									=						'cpu'
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''		)
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''		)

 if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  __a    :	Optional[int]									=						(1,) + shape[1:]
  __a    :	Dict									=						[
      torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=generator[i]       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		)
      for i in range(SCREAMING_SNAKE_CASE__		)
  ]
  __a    :	List[str]									=						torch.cat(SCREAMING_SNAKE_CASE__       ,		dim=0		).to(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	Tuple									=						torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

 return latents






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if is_torch_version('<'       ,		'2.0.0'		) or not hasattr(SCREAMING_SNAKE_CASE__       ,		'_dynamo'		):
  return False
 return isinstance(SCREAMING_SNAKE_CASE__       ,		torch._dynamo.eval_frame.OptimizedModule		)




