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      :				Dict     			=logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase      :				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     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    state_dict.pop(__A						)
    _lowerCamelCase  :		Optional[Any]			    =    val







def     A__	(       __A						):







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

    return new_state_dict







def     A__	(       __A ,			__A=False						):







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







def     A__	(       ):







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

    return im







@torch.no_grad()
def     A__	(       __A ,			__A						):







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

    # load image processor
    _lowerCamelCase  :		Dict			    =    """coco_panoptic""" if is_panoptic else """coco_detection"""
    _lowerCamelCase  :		Dict			    =    DetrImageProcessor(format=__A						)

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





import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def     A__	(       __A ,			__A ,			__A ,			__A=None						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    None
    # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
    if str(weight.dtype						) == "torch.bfloat16":
        # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
        _lowerCamelCase  :		int			    =    weight.view(torch.intaa						)
        _lowerCamelCase  :		List[Any]			    =    """bfloat16"""
    _lowerCamelCase  :		Tuple			    =    weight.cpu().numpy()
    _lowerCamelCase  :		Tuple			    =    os.path.join(__A ,			F"""{weight_name}.dat"""						)
    if index is not None:
        if dtype is None:
            _lowerCamelCase  :		Union[str, Any]			    =    str(array.dtype						)
        _lowerCamelCase  :		Optional[Any]			    =    {"""dtype""": dtype, """shape""": list(array.shape						)}
    if array.ndim == 0:
        _lowerCamelCase  :		int			    =    array[None]
    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=array.dtype ,			mode="""w+""" ,			shape=array.shape						)
    _lowerCamelCase  :		Union[str, Any]			    =    array[:]
    file_array.flush()
    return index







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    tuple(weight_info["""shape"""]						)
    if shape == ():
        # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
        _lowerCamelCase  :		Union[str, Any]			    =    (1,)

    _lowerCamelCase  :		str			    =    weight_info["""dtype"""]
    if dtype == "bfloat16":
        # NumPy does not support bfloat16 so this was saved as a int16
        _lowerCamelCase  :		Optional[int]			    =    """int16"""

    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=__A ,			shape=__A ,			mode="""r"""						)

    if len(weight_info["""shape"""]						) == 0:
        _lowerCamelCase  :		List[str]			    =    weight[0]
    _lowerCamelCase  :		List[str]			    =    torch.tensor(__A						)
    if weight_info["dtype"] == "bfloat16":
        _lowerCamelCase  :		List[Any]			    =    weight.view(torch.bfloataa						)

    return weight







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    if index is None or len(__A						) == 0:
        # Nothing to save
        return

    _lowerCamelCase  :		List[Any]			    =    os.path.join(__A ,			"""index.json"""						)
    if os.path.isfile(__A						):
        with open(__A ,			"""r""" ,			encoding="""utf-8"""						) as f:
            _lowerCamelCase  :		List[str]			    =    json.load(__A						)
    else:
        _lowerCamelCase  :		List[str]			    =    {}
    current_index.update(__A						)

    with open(__A ,			"""w""" ,			encoding="""utf-8"""						) as f:
        json.dump(__A ,			__A ,			indent=2						)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    os.makedirs(__A ,			exist_ok=__A						)
    _lowerCamelCase  :		Dict			    =    {}
    for name, parameter in state_dict.items():
        _lowerCamelCase  :		Union[str, Any]			    =    offload_weight(__A ,			__A ,			__A ,			index=__A						)

    # Update index
    save_offload_index(__A ,			__A						)




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					str     ,	_UpperCamelCase  :					Mapping     ,	_UpperCamelCase  :					str)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    dataset
          _lowerCamelCase  :		Tuple			    =    prefix



      def __getitem__(       self  :					List[Any]     ,	_UpperCamelCase  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          return self.dataset[F"""{self.prefix}{key}"""]



      def __iter__(       self  :					List[str])					->Any:


          """simple docstring"""


          return iter([key for key in self.dataset if key.startswith(self.prefix)])




      def __len__(       self  :					Any)					->int:


          """simple docstring"""


          return len(self.dataset)





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Dict[str, torch.Tensor] = None     ,	_UpperCamelCase  :					Optional[Union[str, os.PathLike]] = None     ,	_UpperCamelCase  :					Mapping = None     ,	_UpperCamelCase  :					Union[str, Any]=None     ,	)					->Any:


          """simple docstring"""


          if state_dict is None and save_folder is None:
              raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights.""")

          _lowerCamelCase  :		int			    =    {} if state_dict is None else state_dict
          _lowerCamelCase  :		Optional[int]			    =    save_folder
          if index is None and save_folder is not None:
              with open(os.path.join(_UpperCamelCase     ,	"""index.json""")) as f:
                  _lowerCamelCase  :		Union[str, Any]			    =    json.load(_UpperCamelCase)
          _lowerCamelCase  :		str			    =    {} if index is None else index
          _lowerCamelCase  :		Optional[int]			    =    list(self.state_dict.keys())
          self.all_keys.extend([key for key in self.index if key not in self.all_keys])
          _lowerCamelCase  :		str			    =    device



      def __getitem__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str)					->Tuple:


          """simple docstring"""


          if key in self.state_dict:
              return self.state_dict[key]
          _lowerCamelCase  :		int			    =    self.index[key]
          if weight_info.get("""safetensors_file""") is not None:
              if not is_safetensors_available():
                  raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`.""")

              from safetensors import safe_open

              _lowerCamelCase  :		Any			    =    """cpu""" if self.device is None else self.device
              with safe_open(weight_info["""safetensors_file"""]     ,	framework="""pt"""     ,	device=_UpperCamelCase) as f:
                  _lowerCamelCase  :		Tuple			    =    f.get_tensor(weight_info.get("""weight_name"""     ,	_UpperCamelCase))

              if "dtype" in weight_info:
                  return tensor.to(getattr(_UpperCamelCase     ,	weight_info["""dtype"""]))
              else:
                  return tensor

          _lowerCamelCase  :		Optional[Any]			    =    os.path.join(self.save_folder     ,	F"""{key}.dat""")
          return load_offloaded_weight(_UpperCamelCase     ,	_UpperCamelCase)



      def __iter__(       self  :					Dict)					->List[Any]:


          """simple docstring"""


          return iter(self.all_keys)




      def __len__(       self  :					int)					->Any:


          """simple docstring"""


          return len(self.all_keys)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		str			    =    {}
    for module_name in submodule_names:
        # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
        # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
        result.update(
            {
                key: param
                for key, param in state_dict.items()
                if key == module_name or key.startswith(module_name + """."""						)
            }						)
    return result




