


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()
UpperCAmelCase			=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase			=    []
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        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      snake_case_       =      state_dict.pop(SCREAMING_SNAKE_CASE					)
      snake_case_       =      val






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Union[str, Any]:




      """simple docstring"""

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

      return new_state_dict






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=False					)->  Dict:




      """simple docstring"""

      snake_case_       =      ''''''
      if is_panoptic:
            snake_case_       =      '''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)
            snake_case_       =      state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
            snake_case_       =      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
            snake_case_       =      in_proj_weight[:256, :]
            snake_case_       =      in_proj_bias[:256]
            snake_case_       =      in_proj_weight[256:512, :]
            snake_case_       =      in_proj_bias[256:512]
            snake_case_       =      in_proj_weight[-256:, :]
            snake_case_       =      in_proj_bias[-256:]
      # 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
            snake_case_       =      state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
            snake_case_       =      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
            snake_case_       =      in_proj_weight[:256, :]
            snake_case_       =      in_proj_bias[:256]
            snake_case_       =      in_proj_weight[256:512, :]
            snake_case_       =      in_proj_bias[256:512]
            snake_case_       =      in_proj_weight[-256:, :]
            snake_case_       =      in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            snake_case_       =      state_dict.pop(
                f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
            snake_case_       =      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
            snake_case_       =      in_proj_weight_cross_attn[:256, :]
            snake_case_       =      in_proj_bias_cross_attn[:256]
            snake_case_       =      in_proj_weight_cross_attn[256:512, :]
            snake_case_       =      in_proj_bias_cross_attn[256:512]
            snake_case_       =      in_proj_weight_cross_attn[-256:, :]
            snake_case_       =      in_proj_bias_cross_attn[-256:]






def        __lowerCAmelCase    ()->  int:




      """simple docstring"""

      snake_case_       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_       =      Image.open(requests.get(SCREAMING_SNAKE_CASE					,					stream=SCREAMING_SNAKE_CASE					).raw					)

      return im






@torch.no_grad()
def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Any:




      """simple docstring"""

      snake_case_       =      DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            snake_case_       =      '''resnet101'''
      if "dc5" in model_name:
            snake_case_       =      True
      snake_case_       =      '''panoptic''' in model_name
      if is_panoptic:
            snake_case_       =      250
      else:
            snake_case_       =      91
            snake_case_       =      '''huggingface/label-files'''
            snake_case_       =      '''coco-detection-id2label.json'''
            snake_case_       =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					repo_type='''dataset'''					)					,					'''r'''					)					)
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
            snake_case_       =      idalabel
            snake_case_       =      {v: k for k, v in idalabel.items()}

      # load image processor
      snake_case_       =      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
      snake_case_       =      DetrImageProcessor(format=SCREAMING_SNAKE_CASE					)

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

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

      # load original model from torch hub
      snake_case_       =      torch.hub.load('''facebookresearch/detr'''					,					SCREAMING_SNAKE_CASE					,					pretrained=SCREAMING_SNAKE_CASE					).eval()
      snake_case_       =      detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  snake_case_       =      '''detr.''' + src
            rename_key(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)
      snake_case_       =      rename_backbone_keys(SCREAMING_SNAKE_CASE					)
      # query, key and value matrices need special treatment
      read_in_q_k_v(SCREAMING_SNAKE_CASE					,					is_panoptic=SCREAMING_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
      snake_case_       =      '''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'''					)
                  ):
                        snake_case_       =      state_dict.pop(SCREAMING_SNAKE_CASE					)
                        snake_case_       =      val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        snake_case_       =      state_dict.pop(SCREAMING_SNAKE_CASE					)
                        snake_case_       =      val
                  elif key.startswith('''bbox_attention'''					) or key.startswith('''mask_head'''					):
                        continue
                  else:
                        snake_case_       =      state_dict.pop(SCREAMING_SNAKE_CASE					)
                        snake_case_       =      val
            else:
                  if not key.startswith('''class_labels_classifier'''					) and not key.startswith('''bbox_predictor'''					):
                        snake_case_       =      state_dict.pop(SCREAMING_SNAKE_CASE					)
                        snake_case_       =      val
    # finally, create HuggingFace model and load state dict
      snake_case_       =      DetrForSegmentation(SCREAMING_SNAKE_CASE					) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE					)
      model.load_state_dict(SCREAMING_SNAKE_CASE					)
      model.eval()
      # verify our conversion
      snake_case_       =      detr(SCREAMING_SNAKE_CASE					)
      snake_case_       =      model(SCREAMING_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(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
      model.save_pretrained(SCREAMING_SNAKE_CASE					)
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)


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



import argparse
import os


UpperCAmelCase			=    {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
   UpperCAmelCase			=    argparse.ArgumentParser("""Choose the framework and version to install""")
   parser.add_argument(
       """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
   )
   parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
   UpperCAmelCase			=    parser.parse_args()

   UpperCAmelCase			=    past_versions_testing[args.framework][args.version]

   os.system(f'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')
   print(f'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')

   UpperCAmelCase			=    """"""
   if args.framework == "pytorch":
      UpperCAmelCase			=    info["""cuda"""]
   os.system(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')
   print(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')