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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->int:

  '''simple docstring'''




  a__     		=     state_dict.pop(lowerCamelCase__					)
  a__     		=     val
def 				__lowercase	(						lowerCamelCase__					)				->Dict:

  '''simple docstring'''




  a__     		=     OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      a__     		=     key.replace("backbone.0.body" ,				"backbone.conv_encoder.model"					)
      a__     		=     value
    else:
      a__     		=     value

  return new_state_dict
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__=False					)				->Any:

  '''simple docstring'''




  a__     		=     ""
  if is_panoptic:
    a__     		=     "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__     		=     state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"					)
    a__     		=     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__     		=     in_proj_weight[:2_56, :]
    a__     		=     in_proj_bias[:2_56]
    a__     		=     in_proj_weight[2_56:5_12, :]
    a__     		=     in_proj_bias[2_56:5_12]
    a__     		=     in_proj_weight[-2_56:, :]
    a__     		=     in_proj_bias[-2_56:]
  # 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__     		=     state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
    a__     		=     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__     		=     in_proj_weight[:2_56, :]
    a__     		=     in_proj_bias[:2_56]
    a__     		=     in_proj_weight[2_56:5_12, :]
    a__     		=     in_proj_bias[2_56:5_12]
    a__     		=     in_proj_weight[-2_56:, :]
    a__     		=     in_proj_bias[-2_56:]
    # read in weights + bias of input projection layer of cross-attention
    a__     		=     state_dict.pop(
        f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"					)
    a__     		=     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__     		=     in_proj_weight_cross_attn[:2_56, :]
    a__     		=     in_proj_bias_cross_attn[:2_56]
    a__     		=     in_proj_weight_cross_attn[2_56:5_12, :]
    a__     		=     in_proj_bias_cross_attn[2_56:5_12]
    a__     		=     in_proj_weight_cross_attn[-2_56:, :]
    a__     		=     in_proj_bias_cross_attn[-2_56:]
def 				__lowercase	(						)				->Optional[int]:

  '''simple docstring'''




  a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
  a__     		=     Image.open(requests.get(lowerCamelCase__ ,				stream=lowerCamelCase__					).raw					)

  return im
@torch.no_grad()
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Union[str, Any]:

  '''simple docstring'''




  a__     		=     DetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    a__     		=     "resnet101"
  if "dc5" in model_name:
    a__     		=     True
  a__     		=     "panoptic" in model_name
  if is_panoptic:
    a__     		=     2_50
  else:
    a__     		=     91
    a__     		=     "huggingface/label-files"
    a__     		=     "coco-detection-id2label.json"
    a__     		=     json.load(open(hf_hub_download(lowerCamelCase__ ,				lowerCamelCase__ ,				repo_type="dataset"					) ,				"r"					)					)
    a__     		=     {int(lowerCamelCase__					): v for k, v in idalabel.items()}
    a__     		=     idalabel
    a__     		=     {v: k for k, v in idalabel.items()}

  # load image processor
  a__     		=     "coco_panoptic" if is_panoptic else "coco_detection"
  a__     		=     DetrImageProcessor(format=lowerCamelCase__					)

  # prepare image
  a__     		=     prepare_img()
  a__     		=     image_processor(images=lowerCamelCase__ ,				return_tensors="pt"					)
  a__     		=     encoding["pixel_values"]

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

  # load original model from torch hub
  a__     		=     torch.hub.load("facebookresearch/detr" ,				lowerCamelCase__ ,				pretrained=lowerCamelCase__					).eval()
  a__     		=     detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      a__     		=     "detr." + src
    rename_key(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)
  a__     		=     rename_backbone_keys(lowerCamelCase__					)
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowerCamelCase__ ,				is_panoptic=lowerCamelCase__					)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  a__     		=     "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__     		=     state_dict.pop(lowerCamelCase__					)
        a__     		=     val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        a__     		=     state_dict.pop(lowerCamelCase__					)
        a__     		=     val
      elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
        continue
      else:
        a__     		=     state_dict.pop(lowerCamelCase__					)
        a__     		=     val
    else:
      if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
        a__     		=     state_dict.pop(lowerCamelCase__					)
        a__     		=     val
    # finally, create HuggingFace model and load state dict
  a__     		=     DetrForSegmentation(lowerCamelCase__					) if is_panoptic else DetrForObjectDetection(lowerCamelCase__					)
  model.load_state_dict(lowerCamelCase__					)
  model.eval()
  # verify our conversion
  a__     		=     detr(lowerCamelCase__					)
  a__     		=     model(lowerCamelCase__					)
  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(lowerCamelCase__					).mkdir(exist_ok=lowerCamelCase__					)
  model.save_pretrained(lowerCamelCase__					)
  image_processor.save_pretrained(lowerCamelCase__					)


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)


def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:

  '''simple docstring'''




  sequence.append(lowerCamelCase__					)

  a__     		=     0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(lowerCamelCase__					):
    return None

  return index


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   						=    input("""Enter numbers separated by comma:\n""").strip()
     _SCREAMING_SNAKE_CASE   						=    [int(item) for item in user_input.split(""",""")]

     _SCREAMING_SNAKE_CASE   						=    input("""Enter a single number to be found in the list:\n""")
     _SCREAMING_SNAKE_CASE   						=    int(target_input)
     _SCREAMING_SNAKE_CASE   						=    sentinel_linear_search(sequence, target)
     if result is not None:
          print(F"""{target} found at positions: {result}""")
     else:
          print("""Not found""")

