



'''simple docstring'''





import argparse
import json
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, ResNetConfig
from transformers.utils import logging


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





def 		__snake_case(							_lowerCAmelCase						)						->					Dict:
      # initialize config
      if "resnet-50" in model_name:
            snake_case__    :       Optional[int]       		= ResNetConfig.from_pretrained("""microsoft/resnet-50"""						)
      elif "resnet-101" in model_name:
            snake_case__    :       Union[str, Any]       		= ResNetConfig.from_pretrained("""microsoft/resnet-101"""						)
      else:
            raise ValueError("""Model name should include either resnet50 or resnet101"""						)

      snake_case__    :       Union[str, Any]       		= DetrConfig(use_timm_backbone=__UpperCamelCase	,  backbone_config=__UpperCamelCase						)

      # set label attributes
      snake_case__    :       Dict       		= "panoptic" in model_name
      if is_panoptic:
            snake_case__    :       Tuple       		= 250
      else:
            snake_case__    :       Any       		= 91
            snake_case__    :       Tuple       		= "huggingface/label-files"
            snake_case__    :       str       		= "coco-detection-id2label.json"
            snake_case__    :       Optional[Any]       		= json.load(open(hf_hub_download(__UpperCamelCase	,  __UpperCamelCase	,  repo_type="""dataset"""						)	,  """r"""						)						)
            snake_case__    :       Any       		= {int(__UpperCamelCase						): v for k, v in idalabel.items()}
            snake_case__    :       int       		= idalabel
            snake_case__    :       Optional[int]       		= {v: k for k, v in idalabel.items()}

      return config, is_panoptic





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      # here we list all keys to be renamed (original name on the left, our name on the right)
      snake_case__    :       Tuple       		= []

      # stem
      # fmt: off
      rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")						)
      rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")						)
      rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")						)
      rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")						)
      rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                            )						)
    # fmt: on

      for i in range(config.encoder_layers						):
            # 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"""),
          ]						)

      return rename_keys





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Tuple:
      snake_case__    :       Union[str, Any]       		= state_dict.pop(__UpperCamelCase						)
      snake_case__    :       int       		= val





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=False						)						->					Tuple:
      snake_case__    :       Optional[int]       		= ""
      if is_panoptic:
            snake_case__    :       List[str]       		= "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__    :       Optional[Any]       		= state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"						)
            snake_case__    :       Dict       		= 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__    :       Tuple       		= in_proj_weight[:256, :]
            snake_case__    :       List[Any]       		= in_proj_bias[:256]
            snake_case__    :       Tuple       		= in_proj_weight[256:512, :]
            snake_case__    :       Tuple       		= in_proj_bias[256:512]
            snake_case__    :       int       		= in_proj_weight[-256:, :]
            snake_case__    :       Union[str, Any]       		= 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__    :       Optional[int]       		= state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"						)
            snake_case__    :       str       		= 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__    :       Optional[Any]       		= in_proj_weight[:256, :]
            snake_case__    :       List[Any]       		= in_proj_bias[:256]
            snake_case__    :       Any       		= in_proj_weight[256:512, :]
            snake_case__    :       Any       		= in_proj_bias[256:512]
            snake_case__    :       Dict       		= in_proj_weight[-256:, :]
            snake_case__    :       List[Any]       		= in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            snake_case__    :       List[Any]       		= state_dict.pop(
                f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"						)
            snake_case__    :       Dict       		= 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__    :       Any       		= in_proj_weight_cross_attn[:256, :]
            snake_case__    :       List[Any]       		= in_proj_bias_cross_attn[:256]
            snake_case__    :       List[str]       		= in_proj_weight_cross_attn[256:512, :]
            snake_case__    :       int       		= in_proj_bias_cross_attn[256:512]
            snake_case__    :       Dict       		= in_proj_weight_cross_attn[-256:, :]
            snake_case__    :       str       		= in_proj_bias_cross_attn[-256:]





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

      return im





@torch.no_grad()
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=None	,  _lowerCAmelCase=False						)						->					Union[str, Any]:
      snake_case__    :       Tuple       		= get_detr_config(__UpperCamelCase						)

      # load original model from torch hub
      snake_case__    :       Tuple       		= {
          "detr-resnet-50": "detr_resnet50",
          "detr-resnet-101": "detr_resnet101",
      }
      logger.info(f"Converting model {model_name}..."						)
      snake_case__    :       Optional[int]       		= torch.hub.load("""facebookresearch/detr"""	,  model_name_to_original_name[model_name]	,  pretrained=__UpperCamelCase						).eval()
      snake_case__    :       Any       		= detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(__UpperCamelCase						):
            if is_panoptic:
                  snake_case__    :       Optional[int]       		= "detr." + src
            rename_key(__UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase						)
      # query, key and value matrices need special treatment
      read_in_q_k_v(__UpperCamelCase	,  is_panoptic=__UpperCamelCase						)
      # 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__    :       List[Any]       		= "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__    :       int       		= state_dict.pop(__UpperCamelCase						)
                        snake_case__    :       Any       		= val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        snake_case__    :       List[Any]       		= state_dict.pop(__UpperCamelCase						)
                        snake_case__    :       Dict       		= val
                  elif key.startswith("""bbox_attention"""						) or key.startswith("""mask_head"""						):
                        continue
                  else:
                        snake_case__    :       List[Any]       		= state_dict.pop(__UpperCamelCase						)
                        snake_case__    :       Optional[Any]       		= val
            else:
                  if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
                        snake_case__    :       Union[str, Any]       		= state_dict.pop(__UpperCamelCase						)
                        snake_case__    :       Tuple       		= val

    # finally, create HuggingFace model and load state dict
      snake_case__    :       Tuple       		= DetrForSegmentation(__UpperCamelCase						) if is_panoptic else DetrForObjectDetection(__UpperCamelCase						)
      model.load_state_dict(__UpperCamelCase						)
      model.eval()

      # verify our conversion on an image
      snake_case__    :       Optional[int]       		= "coco_panoptic" if is_panoptic else "coco_detection"
      snake_case__    :       int       		= DetrImageProcessor(format=__UpperCamelCase						)

      snake_case__    :       Optional[Any]       		= processor(images=prepare_img()	,  return_tensors="""pt"""						)
      snake_case__    :       str       		= encoding["pixel_values"]

      snake_case__    :       List[str]       		= detr(__UpperCamelCase						)
      snake_case__    :       Tuple       		= model(__UpperCamelCase						)

      assert torch.allclose(outputs.logits	,  original_outputs["""pred_logits"""]	,  atol=1e-3						)
      assert torch.allclose(outputs.pred_boxes	,  original_outputs["""pred_boxes"""]	,  atol=1e-3						)
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks	,  original_outputs["""pred_masks"""]	,  atol=1e-4						)
      print("""Looks ok!"""						)

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."						)
            Path(__UpperCamelCase						).mkdir(exist_ok=__UpperCamelCase						)
            model.save_pretrained(__UpperCamelCase						)
            processor.save_pretrained(__UpperCamelCase						)

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info("""Uploading PyTorch model and image processor to the hub..."""						)
            model.push_to_hub(f"nielsr/{model_name}"						)
            processor.push_to_hub(f"nielsr/{model_name}"						)


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

       parser.add_argument(
           "--model_name",
           default="detr-resnet-50",
           type=str,
           choices=["detr-resnet-50", "detr-resnet-101"],
           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."
       )
       parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
       __a  			=						parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase		       =      subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase		       =      subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

lowerCamelCase		       =      """|""".join(sys.argv[1:])
lowerCamelCase		       =      re.compile(RF'^({joined_dirs}).*?\.py$')

lowerCamelCase		       =      [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


