





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 				=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
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}.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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :Optional[int]			,							snake_case__  :Union[str, Any]     )      ->						int:
  _lowercase							= state_dict.pop(snake_case__     )
  _lowercase							= val

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     )      ->						Optional[Any]:
  _lowercase							= OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      _lowercase							= key.replace('backbone.0.body'			,							'backbone.conv_encoder.model'     )
      _lowercase							= value
    else:
      _lowercase							= value

  return new_state_dict

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :int=False     )      ->						Optional[Any]:
  _lowercase							= ''
  if is_panoptic:
    _lowercase							= '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)
    _lowercase							= state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""     )
    _lowercase							= 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
    _lowercase							= in_proj_weight[:256, :]
    _lowercase							= in_proj_bias[:256]
    _lowercase							= in_proj_weight[256:512, :]
    _lowercase							= in_proj_bias[256:512]
    _lowercase							= in_proj_weight[-256:, :]
    _lowercase							= in_proj_bias[-256:]

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

  return im

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

  # load image processor
  _lowercase							= 'coco_panoptic' if is_panoptic else 'coco_detection'
  _lowercase							= ConditionalDetrImageProcessor(format=snake_case__     )

  # prepare image
  _lowercase							= prepare_img()
  _lowercase							= image_processor(images=snake_case__			,							return_tensors='pt'     )
  _lowercase							= encoding['pixel_values']

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

  # load original model from torch hub
  _lowercase							= torch.hub.load('DeppMeng/ConditionalDETR'			,							snake_case__			,							pretrained=snake_case__     ).eval()
  _lowercase							= conditional_detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      _lowercase							= 'conditional_detr.' + src
    rename_key(snake_case__			,							snake_case__			,							snake_case__     )
  _lowercase							= rename_backbone_keys(snake_case__     )
  # query, key and value matrices need special treatment
  read_in_q_k_v(snake_case__			,							is_panoptic=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
  _lowercase							= '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'     )
      ):
        _lowercase							= state_dict.pop(snake_case__     )
        _lowercase							= val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        _lowercase							= state_dict.pop(snake_case__     )
        _lowercase							= val
      elif key.startswith('bbox_attention'     ) or key.startswith('mask_head'     ):
        continue
      else:
        _lowercase							= state_dict.pop(snake_case__     )
        _lowercase							= val
    else:
      if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
        _lowercase							= state_dict.pop(snake_case__     )
        _lowercase							= val
    # finally, create HuggingFace model and load state dict
  _lowercase							= ConditionalDetrForSegmentation(snake_case__     ) if is_panoptic else ConditionalDetrForObjectDetection(snake_case__     )
  model.load_state_dict(snake_case__     )
  model.eval()
  model.push_to_hub(repo_id=snake_case__			,							organization='DepuMeng'			,							commit_message='Add model'     )
  # verify our conversion
  _lowercase							= conditional_detr(snake_case__     )
  _lowercase							= model(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(snake_case__     ).mkdir(exist_ok=snake_case__     )
  model.save_pretrained(snake_case__     )
  image_processor.save_pretrained(snake_case__     )


if __name__ == "__main__":
      snake_case 				=       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 				=       parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)