
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()
a_								=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_								=   []
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__     (						_UpperCamelCase     :	List[str]	,_UpperCamelCase     :	int	,_UpperCamelCase     :	List[Any]							):
   __lowerCamelCase  							=      state_dict.pop(__lowercase							)
   __lowerCamelCase  							=      val





def 						a__     (						_UpperCamelCase     :	Optional[int]							):
   __lowerCamelCase  							=      OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         __lowerCamelCase  							=      key.replace('''backbone.0.body'''	,'''backbone.conv_encoder.model'''							)
         __lowerCamelCase  							=      value
      else:
         __lowerCamelCase  							=      value

   return new_state_dict





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





def 						a__     (						):
   __lowerCamelCase  							=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   __lowerCamelCase  							=      Image.open(requests.get(__lowercase	,stream=__lowercase							).raw							)

   return im





@torch.no_grad()
def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	List[Any]							):
   __lowerCamelCase  							=      DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      __lowerCamelCase  							=      '''resnet101'''
   if "dc5" in model_name:
      __lowerCamelCase  							=      True
   __lowerCamelCase  							=      '''panoptic''' in model_name
   if is_panoptic:
      __lowerCamelCase  							=      2_50
   else:
      __lowerCamelCase  							=      91
      __lowerCamelCase  							=      '''huggingface/label-files'''
      __lowerCamelCase  							=      '''coco-detection-id2label.json'''
      __lowerCamelCase  							=      json.load(open(hf_hub_download(__lowercase	,__lowercase	,repo_type='''dataset'''							)	,'''r'''							)							)
      __lowerCamelCase  							=      {int(__lowercase							): v for k, v in idalabel.items()}
      __lowerCamelCase  							=      idalabel
      __lowerCamelCase  							=      {v: k for k, v in idalabel.items()}

   # load image processor
   __lowerCamelCase  							=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
   __lowerCamelCase  							=      DetrImageProcessor(format=__lowercase							)

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

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

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


if __name__ == "__main__":
  a_								=   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."""
  )
  a_								=   parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)











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 						a__     (						_UpperCamelCase     :	Optional[Any]							):
   return EnvironmentCommand()





class 	__lowerCAmelCase       (		lowerCAmelCase__				):




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      parser.add_parser('''env'''		,			help='''Print relevant system environment info.'''			)
    download_parser.set_defaults(func=__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      {
        '''`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(__UpperCAmelCase			)			)

    return info




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"






