



"""simple docstring"""






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def 	snake_case_  (				*A_ :		Dict						):


       '''simple docstring'''





       if not isinstance(lowerCamelCase_,    lowerCamelCase_						):
              _lowerCamelCase						:    Union[str, Any]         =			list(lowerCamelCase_						)
       for i in range(len(lowerCamelCase_						)						):
              _lowerCamelCase						:    int         =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects



def 	snake_case_  (				A_ :		Exception						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			[
           """CUDA out of memory.""",  # CUDA OOM
           """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
           """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
       ]
       if isinstance(lowerCamelCase_,    lowerCamelCase_						) and len(exception.args						) == 1:
              return any(err in exception.args[0] for err in _statements						)
       return False







def 	snake_case_  (				A_ :		callable = None,    A_ :		int = 1_28						):


       '''simple docstring'''





       if function is None:
              return functools.partial(lowerCamelCase_,    starting_batch_size=lowerCamelCase_						)

       _lowerCamelCase						:    Union[str, Any]         =			starting_batch_size

       def decorator(*A_ :		List[Any],    **A_ :		List[str]						):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              _lowerCamelCase						:    Dict         =			list(inspect.signature(lowerCamelCase_						).parameters.keys()						)
              # Guard against user error
              if len(lowerCamelCase_						) < (len(lowerCamelCase_						) + 1):
                     _lowerCamelCase						:    Any         =			""", """.join([F'''{arg}={value}''' for arg, value in zip(params[1:],    args[1:]						)]						)
                     raise TypeError(
                         F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                         F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''						)
              while True:
                     if batch_size == 0:
                            raise RuntimeError('''No executable batch size found, reached zero.'''						)
                     try:
                            return function(lowerCamelCase_,    *lowerCamelCase_,    **lowerCamelCase_						)
                     except Exception as e:
                            if should_reduce_batch_size(lowerCamelCase_						):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator







"""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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__      =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__      =    []
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}.cross_attn.out_proj.weight""",
           F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
       )
   )
   rename_keys.append(
       (
           F"""transformer.decoder.layers.{i}.cross_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"""))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
   )

   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)






def 	snake_case_  (				A_ :		str,    A_ :		Tuple,    A_ :		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			state_dict.pop(A_						)
       _lowerCamelCase						:    Union[str, Any]         =			val






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





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

       return new_state_dict






def 	snake_case_  (				A_ :		Optional[int],    A_ :		List[str]=False						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			''''''
       if is_panoptic:
              _lowerCamelCase						:    Optional[Any]         =			'''conditional_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)
              _lowerCamelCase						:    Optional[int]         =			state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
              _lowerCamelCase						:    Dict         =			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
              _lowerCamelCase						:    str         =			in_proj_weight[:2_56, :]
              _lowerCamelCase						:    int         =			in_proj_bias[:2_56]
              _lowerCamelCase						:    str         =			in_proj_weight[2_56:5_12, :]
              _lowerCamelCase						:    Optional[Any]         =			in_proj_bias[2_56:5_12]
              _lowerCamelCase						:    List[Any]         =			in_proj_weight[-2_56:, :]
              _lowerCamelCase						:    List[str]         =			in_proj_bias[-2_56:]






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    List[str]         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase						:    Any         =			Image.open(requests.get(A_,    stream=A_						).raw						)

       return im






@torch.no_grad()
def 	snake_case_  (				A_ :		Optional[Any],    A_ :		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              _lowerCamelCase						:    Union[str, Any]         =			'''resnet101'''
       if "dc5" in model_name:
              _lowerCamelCase						:    Optional[int]         =			True
       _lowerCamelCase						:    Tuple         =			'''panoptic''' in model_name
       if is_panoptic:
              _lowerCamelCase						:    Optional[int]         =			2_50
       else:
              _lowerCamelCase						:    int         =			91
              _lowerCamelCase						:    List[str]         =			'''huggingface/label-files'''
              _lowerCamelCase						:    Any         =			'''coco-detection-id2label.json'''
              _lowerCamelCase						:    Optional[int]         =			json.load(open(hf_hub_download(A_,    A_,    repo_type='''dataset'''						),    '''r'''						)						)
              _lowerCamelCase						:    List[str]         =			{int(A_						): v for k, v in idalabel.items()}
              _lowerCamelCase						:    List[str]         =			idalabel
              _lowerCamelCase						:    str         =			{v: k for k, v in idalabel.items()}

       # load image processor
       _lowerCamelCase						:    int         =			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
       _lowerCamelCase						:    Any         =			ConditionalDetrImageProcessor(format=A_						)

       # prepare image
       _lowerCamelCase						:    Optional[int]         =			prepare_img()
       _lowerCamelCase						:    str         =			image_processor(images=A_,    return_tensors='''pt'''						)
       _lowerCamelCase						:    Union[str, Any]         =			encoding['''pixel_values''']

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

       # load original model from torch hub
       _lowerCamelCase						:    int         =			torch.hub.load('''DeppMeng/ConditionalDETR''',    A_,    pretrained=A_						).eval()
       _lowerCamelCase						:    Tuple         =			conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     _lowerCamelCase						:    Optional[Any]         =			'''conditional_detr.''' + src
              rename_key(A_,    A_,    A_						)
       _lowerCamelCase						:    Dict         =			rename_backbone_keys(A_						)
       # query, key and value matrices need special treatment
       read_in_q_k_v(A_,    is_panoptic=A_						)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       _lowerCamelCase						:    Optional[int]         =			'''conditional_detr.model.''' if is_panoptic else '''model.'''
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith('''conditional_detr'''						)
                         and not key.startswith('''class_labels_classifier'''						)
                         and not key.startswith('''bbox_predictor'''						)
                     ):
                            _lowerCamelCase						:    List[Any]         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    int         =			val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            _lowerCamelCase						:    List[str]         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    Optional[Any]         =			val
                     elif key.startswith('''bbox_attention'''						) or key.startswith('''mask_head'''						):
                            continue
                     else:
                            _lowerCamelCase						:    Optional[Any]         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    Any         =			val
              else:
                     if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
                            _lowerCamelCase						:    int         =			state_dict.pop(A_						)
                            _lowerCamelCase						:    str         =			val
    # finally, create HuggingFace model and load state dict
       _lowerCamelCase						:    Dict         =			ConditionalDetrForSegmentation(A_						) if is_panoptic else ConditionalDetrForObjectDetection(A_						)
       model.load_state_dict(A_						)
       model.eval()
       model.push_to_hub(repo_id=A_,    organization='''DepuMeng''',    commit_message='''Add model'''						)
       # verify our conversion
       _lowerCamelCase						:    Dict         =			conditional_detr(A_						)
       _lowerCamelCase						:    Optional[int]         =			model(A_						)
       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(A_						).mkdir(exist_ok=A_						)
       model.save_pretrained(A_						)
       image_processor.save_pretrained(A_						)


if __name__ == "__main__":
   lowerCAmelCase__      =    argparse.ArgumentParser()

   parser.add_argument(
       '''--model_name''',
       default='''conditional_detr_resnet50''',
       type=str,
       help='''Name of the CONDITIONAL_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__      =    parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


