
'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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


def 		_UpperCAmelCase			(					)      ->  Dict:
  _lowerCAmelCase    :					Dict							=   DetaConfig(
      num_queries=9_00					, encoder_ffn_dim=20_48					, decoder_ffn_dim=20_48					, num_feature_levels=5					, assign_first_stage=_lowerCamelCase					, with_box_refine=_lowerCamelCase					, two_stage=_lowerCamelCase					, )

  # set labels
  _lowerCAmelCase    :					List[str]							=   91
  _lowerCAmelCase    :					Optional[int]							=   """huggingface/label-files"""
  _lowerCAmelCase    :					Union[str, Any]							=   """coco-detection-id2label.json"""
  _lowerCAmelCase    :					List[Any]							=   json.load(open(cached_download(hf_hub_url(_lowerCamelCase					, _lowerCamelCase					, repo_type="""dataset"""						)						)					, """r"""						)						)
  _lowerCAmelCase    :					Tuple							=   {int(_lowerCamelCase						): v for k, v in idalabel.items()}
  _lowerCAmelCase    :					List[Any]							=   idalabel
  _lowerCAmelCase    :					Dict							=   {v: k for k, v in idalabel.items()}

  return config


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[str]						)      ->  Tuple:
  _lowerCAmelCase    :					Optional[int]							=   []

  # stem
  # fmt: off
  rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")						)
  rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")						)
  rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")						)
  rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")						)
  rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")						)
  # stages
  for stage_idx in range(len(config.backbone_config.depths						)						):
    for layer_idx in range(config.backbone_config.depths[stage_idx]						):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
            )						)
      # 3 convs
      for i in range(3						):
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
            )						)
        rename_keys.append(
            (
                f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
            )						)
    # transformer encoder
  for i in range(config.encoder_layers						):
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')						)
    rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')						)

  # transformer decoder
  for i in range(config.decoder_layers						):
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')						)
    rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')						)

  # fmt: on

  return rename_keys


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int					, _lowerCamelCase  :    str					, _lowerCamelCase  :    Tuple						)      ->  Dict:
  _lowerCAmelCase    :					Optional[int]							=   dct.pop(_lowerCamelCase						)
  _lowerCAmelCase    :					Dict							=   val


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    str						)      ->  str:
  # transformer decoder self-attention layers
  _lowerCAmelCase    :					int							=   config.d_model
  for i in range(config.decoder_layers						):
    # read in weights + bias of input projection layer of self-attention
    _lowerCAmelCase    :					Tuple							=   state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'						)
    _lowerCAmelCase    :					Tuple							=   state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'						)
    # next, add query, keys and values (in that order) to the state dict
    _lowerCAmelCase    :					List[str]							=   in_proj_weight[:hidden_size, :]
    _lowerCAmelCase    :					Union[str, Any]							=   in_proj_bias[:hidden_size]
    _lowerCAmelCase    :					str							=   in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _lowerCAmelCase    :					Dict							=   in_proj_bias[hidden_size : hidden_size * 2]
    _lowerCAmelCase    :					Optional[Any]							=   in_proj_weight[-hidden_size:, :]
    _lowerCAmelCase    :					Tuple							=   in_proj_bias[-hidden_size:]


def 		_UpperCAmelCase			(					)      ->  List[str]:
  _lowerCAmelCase    :					Union[str, Any]							=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _lowerCAmelCase    :					Dict							=   Image.open(requests.get(_lowerCamelCase					, stream=_lowerCamelCase						).raw						)

  return im


@torch.no_grad()
def 		_UpperCAmelCase			(					_lowerCamelCase  :    int					, _lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Tuple						)      ->  Optional[int]:
  _lowerCAmelCase    :					str							=   get_deta_config()

  # load original state dict
  if model_name == "deta-resnet-50":
    _lowerCAmelCase    :					List[str]							=   """adet_checkpoint0011.pth"""
  elif model_name == "deta-resnet-50-24-epochs":
    _lowerCAmelCase    :					List[str]							=   """adet_2x_checkpoint0023.pth"""
  else:
    raise ValueError(f'Model name {model_name} not supported'						)
  _lowerCAmelCase    :					List[str]							=   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""					, filename=_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   torch.load(_lowerCamelCase					, map_location="""cpu"""						)["""model"""]

  # rename keys
  _lowerCAmelCase    :					List[str]							=   create_rename_keys(_lowerCamelCase						)
  for src, dest in rename_keys:
    rename_key(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)
  read_in_decoder_q_k_v(_lowerCamelCase					, _lowerCamelCase						)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      _lowerCAmelCase    :					Optional[Any]							=   state_dict.pop(_lowerCamelCase						)
      _lowerCAmelCase    :					str							=   val
    if "input_proj" in key:
      _lowerCAmelCase    :					List[Any]							=   state_dict.pop(_lowerCamelCase						)
      _lowerCAmelCase    :					Tuple							=   val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _lowerCAmelCase    :					Optional[Any]							=   state_dict.pop(_lowerCamelCase						)
      _lowerCAmelCase    :					Dict							=   val

    # finally, create HuggingFace model and load state dict
  _lowerCAmelCase    :					Union[str, Any]							=   DetaForObjectDetection(_lowerCamelCase						)
  model.load_state_dict(_lowerCamelCase						)
  model.eval()

  _lowerCAmelCase    :					Dict							=   """cuda""" if torch.cuda.is_available() else """cpu"""
  model.to(_lowerCamelCase						)

  # load image processor
  _lowerCAmelCase    :					Tuple							=   DetaImageProcessor(format="""coco_detection"""						)

  # verify our conversion on image
  _lowerCAmelCase    :					List[Any]							=   prepare_img()
  _lowerCAmelCase    :					int							=   processor(images=_lowerCamelCase					, return_tensors="""pt"""						)
  _lowerCAmelCase    :					Dict							=   encoding["""pixel_values"""]
  _lowerCAmelCase    :					Optional[int]							=   model(pixel_values.to(_lowerCamelCase						)						)

  # verify logits
  if model_name == "deta-resnet-50":
    _lowerCAmelCase    :					Any							=   torch.tensor(
        [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]						)
    _lowerCAmelCase    :					str							=   torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]						)
  elif model_name == "deta-resnet-50-24-epochs":
    _lowerCAmelCase    :					str							=   torch.tensor(
        [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]						)
    _lowerCAmelCase    :					List[Any]							=   torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]						)

  assert torch.allclose(outputs.logits[0, :3, :3]					, expected_logits.to(_lowerCamelCase						)					, atol=1e-4						)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]					, expected_boxes.to(_lowerCamelCase						)					, atol=1e-4						)
  print("""Everything ok!"""						)

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'						)
    Path(_lowerCamelCase						).mkdir(exist_ok=_lowerCamelCase						)
    model.save_pretrained(_lowerCamelCase						)
    processor.save_pretrained(_lowerCamelCase						)

  # Push to hub
  if push_to_hub:
    print("""Pushing model and processor to hub..."""						)
    model.push_to_hub(f'jozhang97/{model_name}'						)
    processor.push_to_hub(f'jozhang97/{model_name}'						)


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

     parser.add_argument(
         """--model_name""",
         type=str,
         default="""deta-resnet-50""",
         choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
         help="""Name of the 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.""",
     )
     parser.add_argument(
         """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
     )
     UpperCamelCase_            =    parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''

import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]						)      ->  Optional[Any]:
  return EnvironmentCommand()



class 					a_					(_a      ):




       @staticmethod
       def 							__UpperCamelCase    (       snake_case_					):
         _lowerCAmelCase    :					Optional[Any]							=   parser.add_parser("""env"""				,				help="""Print relevant system environment info."""					)
         download_parser.set_defaults(func=snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[Any]							=   {
             """`datasets` version""": version,
             """Platform""": platform.platform(),
             """Python version""": platform.python_version(),
             """Huggingface_hub version""": huggingface_hub.__version__,
             """PyArrow version""": pyarrow.__version__,
             """Pandas version""": pandas.__version__,
         }

         print("""\nCopy-and-paste the text below in your GitHub issue.\n"""					)
         print(self.format_dict(snake_case_					)					)

         return info







       @staticmethod
       def 							__UpperCamelCase    (       snake_case_					):
         return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]					) + "\n"


