
'''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()
__snake_case:  List[str]								=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case:  Optional[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}.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_       :    Optional[int]     ,		A_       :    Tuple     ,		A_       :    Optional[int]   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Tuple   ):
  """simple docstring"""







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

  return new_state_dict






def 	_snake_case						(      A_       :    Dict     ,		A_       :    str=False   ):
  """simple docstring"""







  a_      :	Tuple      =							""""""
  if is_panoptic:
    a_      :	List[str]      =							"""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)
    a_      :	List[Any]      =							state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''   )
    a_      :	str      =							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_      :	List[str]      =							in_proj_weight[:256, :]
    a_      :	Any      =							in_proj_bias[:256]
    a_      :	Optional[int]      =							in_proj_weight[256:512, :]
    a_      :	List[Any]      =							in_proj_bias[256:512]
    a_      :	Union[str, Any]      =							in_proj_weight[-256:, :]
    a_      :	Union[str, Any]      =							in_proj_bias[-256:]






def 	_snake_case						(      ):
  """simple docstring"""







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

  return im






@torch.no_grad()
def 	_snake_case						(      A_       :    List[str]     ,		A_       :    Union[str, Any]   ):
  """simple docstring"""







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

  # load image processor
  a_      :	Dict      =							"""coco_panoptic""" if is_panoptic else """coco_detection"""
  a_      :	Any      =							ConditionalDetrImageProcessor(format=A_   )

  # prepare image
  a_      :	str      =							prepare_img()
  a_      :	int      =							image_processor(images=A_     ,		return_tensors="""pt"""   )
  a_      :	Optional[int]      =							encoding["""pixel_values"""]

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

  # load original model from torch hub
  a_      :	int      =							torch.hub.load("""DeppMeng/ConditionalDETR"""     ,		A_     ,		pretrained=A_   ).eval()
  a_      :	Dict      =							conditional_detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      a_      :	str      =							"""conditional_detr.""" + src
    rename_key(A_     ,		A_     ,		A_   )
  a_      :	List[str]      =							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
  a_      :	List[Any]      =							"""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"""   )
      ):
        a_      :	int      =							state_dict.pop(A_   )
        a_      :	Any      =							val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        a_      :	Tuple      =							state_dict.pop(A_   )
        a_      :	Dict      =							val
      elif key.startswith("""bbox_attention"""   ) or key.startswith("""mask_head"""   ):
        continue
      else:
        a_      :	Union[str, Any]      =							state_dict.pop(A_   )
        a_      :	Any      =							val
    else:
      if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
        a_      :	str      =							state_dict.pop(A_   )
        a_      :	List[str]      =							val
    # finally, create HuggingFace model and load state dict
  a_      :	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
  a_      :	Optional[int]      =							conditional_detr(A_   )
  a_      :	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__":
    __snake_case:  Tuple								=			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."
    )
    __snake_case:  Optional[int]								=			parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




'''simple docstring'''

from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__snake_case:  Optional[Any]								=			logging.get_logger(__name__)


__snake_case:  Tuple								=			TypeVar("DatasetType", Dataset, IterableDataset)






def 	_snake_case						(      A_       :    List[DatasetType]     ,		A_       :    Optional[List[float]] = None     ,		A_       :    Optional[int] = None     ,		A_       :    Optional[DatasetInfo] = None     ,		A_       :    Optional[NamedSplit] = None     ,		A_       :    Literal["first_exhausted", "all_exhausted"] = "first_exhausted"     ,		):
  """simple docstring"""







  from .arrow_dataset import Dataset
  from .iterable_dataset import IterableDataset

  if not datasets:
    raise ValueError("""Unable to interleave an empty list of datasets."""   )
  for i, dataset in enumerate(A_   ):
    if not isinstance(A_     ,		(Dataset, IterableDataset)   ):
      if isinstance(A_     ,		(DatasetDict, IterableDatasetDict)   ):
        if not dataset:
          raise ValueError(
              f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
              """is an empty dataset dictionary."""   )
        raise ValueError(
            f'''Dataset at position {i} has at least one split: {list(A_   )}\n'''
            f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A_   )   )}\']'''   )
      raise ValueError(
          f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A_   ).__name__}.'''   )
    if i == 0:
      a_      , a_      :	Union[str, Any]      =							(
          (Dataset, IterableDataset) if isinstance(A_     ,		A_   ) else (IterableDataset, Dataset)
      )
    elif not isinstance(A_     ,		A_   ):
      raise ValueError(
          f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''   )
  if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
    raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'''   )
  if dataset_type is Dataset:
    return _interleave_map_style_datasets(
        A_     ,		A_     ,		A_     ,		info=A_     ,		split=A_     ,		stopping_strategy=A_   )
  else:
    return _interleave_iterable_datasets(
        A_     ,		A_     ,		A_     ,		info=A_     ,		split=A_     ,		stopping_strategy=A_   )






def 	_snake_case						(      A_       :    List[DatasetType]     ,		A_       :    Optional[DatasetInfo] = None     ,		A_       :    Optional[NamedSplit] = None     ,		A_       :    int = 0     ,		):
  """simple docstring"""







  if not dsets:
    raise ValueError("""Unable to concatenate an empty list of datasets."""   )
  for i, dataset in enumerate(A_   ):
    if not isinstance(A_     ,		(Dataset, IterableDataset)   ):
      if isinstance(A_     ,		(DatasetDict, IterableDatasetDict)   ):
        if not dataset:
          raise ValueError(
              f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
              """is an empty dataset dictionary."""   )
        raise ValueError(
            f'''Dataset at position {i} has at least one split: {list(A_   )}\n'''
            f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A_   )   )}\']'''   )
      raise ValueError(
          f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A_   ).__name__}.'''   )
    if i == 0:
      a_      , a_      :	str      =							(
          (Dataset, IterableDataset) if isinstance(A_     ,		A_   ) else (IterableDataset, Dataset)
      )
    elif not isinstance(A_     ,		A_   ):
      raise ValueError(
          f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''   )
  if dataset_type is Dataset:
    return _concatenate_map_style_datasets(A_     ,		info=A_     ,		split=A_     ,		axis=A_   )
  else:
    return _concatenate_iterable_datasets(A_     ,		info=A_     ,		split=A_     ,		axis=A_   )


