


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()
__UpperCamelCase    :						Optional[Any]     =							logging.get_logger(__name__)





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      if "resnet-50" in model_name:
            __lowerCamelCase     :      Union[str, Any]       						=						ResNetConfig.from_pretrained("""microsoft/resnet-50"""      )
      elif "resnet-101" in model_name:
            __lowerCamelCase     :      Dict       						=						ResNetConfig.from_pretrained("""microsoft/resnet-101"""      )
      else:
            raise ValueError("""Model name should include either resnet50 or resnet101"""      )

      __lowerCamelCase     :      Union[str, Any]       						=						DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE_						,	backbone_config=SCREAMING_SNAKE_CASE_      )

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

      return config, is_panoptic





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      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   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						state_dict.pop(SCREAMING_SNAKE_CASE_      )
      __lowerCamelCase     :      List[Any]       						=						val





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			str=False      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						"http://images.cocodataset.org/val2017/000000039769.jpg"
      __lowerCamelCase     :      Union[str, Any]       						=						Image.open(requests.get(SCREAMING_SNAKE_CASE_						,	stream=SCREAMING_SNAKE_CASE_      ).raw      )

      return im





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			List[Any]=None						,	UpperCAmelCase      :			Union[str, Any]=False      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						get_detr_config(SCREAMING_SNAKE_CASE_      )

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

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

      # verify our conversion on an image
      __lowerCamelCase     :      List[Any]       						=						"coco_panoptic" if is_panoptic else "coco_detection"
      __lowerCamelCase     :      Optional[Any]       						=						DetrImageProcessor(format=SCREAMING_SNAKE_CASE_      )

      __lowerCamelCase     :      Any       						=						processor(images=prepare_img()						,	return_tensors="""pt"""      )
      __lowerCamelCase     :      Dict       						=						encoding["pixel_values"]

      __lowerCamelCase     :      List[str]       						=						detr(SCREAMING_SNAKE_CASE_      )
      __lowerCamelCase     :      Tuple       						=						model(SCREAMING_SNAKE_CASE_      )

      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(SCREAMING_SNAKE_CASE_      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
            model.save_pretrained(SCREAMING_SNAKE_CASE_      )
            processor.save_pretrained(SCREAMING_SNAKE_CASE_      )

      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__":
    __UpperCamelCase    :						List[str]     =							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.')
    __UpperCamelCase    :						List[str]     =							parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Any     =							{
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						int     =							[
        'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TimesformerModel',
        'TimesformerForVideoClassification',
        'TimesformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



