
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, SwinConfig
from transformers.utils import logging


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






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    SwinConfig(
         embed_dim=192	,		depths=(2, 2, 18, 2)	,		num_heads=(6, 12, 24, 48)	,		window_size=12	,		out_features=["""stage2""", """stage3""", """stage4"""]	,		)

     UpperCamelCase__				=    DetaConfig(
         backbone_config=__a	,		num_queries=900	,		encoder_ffn_dim=2_048	,		decoder_ffn_dim=2_048	,		num_feature_levels=5	,		assign_first_stage=__a	,		with_box_refine=__a	,		two_stage=__a	,		)

     # set labels
     UpperCamelCase__				=    """huggingface/label-files"""
     if "o365" in model_name:
          UpperCamelCase__				=    366
          UpperCamelCase__				=    """object365-id2label.json"""
     else:
          UpperCamelCase__				=    91
          UpperCamelCase__				=    """coco-detection-id2label.json"""

     UpperCamelCase__				=    num_labels
     UpperCamelCase__				=    json.load(open(cached_download(hf_hub_url(__a	,		__a	,		repo_type="""dataset"""						)						)	,		"""r"""						)						)
     UpperCamelCase__				=    {int(__a						): v for k, v in idalabel.items()}
     UpperCamelCase__				=    idalabel
     UpperCamelCase__				=    {v: k for k, v in idalabel.items()}

     return config






def 						__magic_name__			(   __a      :	Any						):



     '''simple docstring'''




     UpperCamelCase__				=    []

     # stem
     # fmt: off
     rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")						)
     rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")						)
     rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")						)
     rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")						)
     # stages
     for i in range(len(config.backbone_config.depths						)						):
          for j in range(config.backbone_config.depths[i]						):
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")						)

          if i < 3:
               rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")						)
               rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")						)

     rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")						)
     rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")						)
     rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")						)
     rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")						)
     rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")						)
     rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")						)

     # 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 						__magic_name__			(   __a      :	Any	,		__a      :	Tuple	,		__a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    dct.pop(__a						)
     UpperCamelCase__				=    val






def 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
     for i in range(len(backbone_config.depths						)						):
          UpperCamelCase__				=    num_features[i]
          for j in range(backbone_config.depths[i]						):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               UpperCamelCase__				=    state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"						)
               UpperCamelCase__				=    state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"						)
               # next, add query, keys and values (in that order) to the state dict
               UpperCamelCase__				=    in_proj_weight[:dim, :]
               UpperCamelCase__				=    in_proj_bias[: dim]
               UpperCamelCase__				=    in_proj_weight[
                   dim : dim * 2, :
               ]
               UpperCamelCase__				=    in_proj_bias[
                   dim : dim * 2
               ]
               UpperCamelCase__				=    in_proj_weight[
                   -dim :, :
               ]
               UpperCamelCase__				=    in_proj_bias[-dim :]
               # fmt: on






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Any						):



     '''simple docstring'''




     UpperCamelCase__				=    config.d_model
     for i in range(config.decoder_layers						):
          # read in weights + bias of input projection layer of self-attention
          UpperCamelCase__				=    state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"						)
          UpperCamelCase__				=    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
          UpperCamelCase__				=    in_proj_weight[:hidden_size, :]
          UpperCamelCase__				=    in_proj_bias[:hidden_size]
          UpperCamelCase__				=    in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          UpperCamelCase__				=    in_proj_bias[hidden_size : hidden_size * 2]
          UpperCamelCase__				=    in_proj_weight[-hidden_size:, :]
          UpperCamelCase__				=    in_proj_bias[-hidden_size:]






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCamelCase__				=    Image.open(requests.get(__a	,		stream=__a						).raw						)

     return im






@torch.no_grad()
def 						__magic_name__			(   __a      :	Any	,		__a      :	Dict	,		__a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    get_deta_config(__a						)

     # load original state dict
     if model_name == "deta-swin-large":
          UpperCamelCase__				=    hf_hub_download(repo_id="""nielsr/deta-checkpoints"""	,		filename="""adet_swin_ft.pth"""						)
     elif model_name == "deta-swin-large-o365":
          UpperCamelCase__				=    hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""	,		filename="""deta_swin_pt_o365.pth"""						)
     else:
          raise ValueError(f"Model name {model_name} not supported"						)

     UpperCamelCase__				=    torch.load(__a	,		map_location="""cpu"""						)["""model"""]

     # original state dict
     for name, param in state_dict.items():
          print(__a	,		param.shape						)

     # rename keys
     UpperCamelCase__				=    create_rename_keys(__a						)
     for src, dest in rename_keys:
          rename_key(__a	,		__a	,		__a						)
     read_in_swin_q_k_v(__a	,		config.backbone_config						)
     read_in_decoder_q_k_v(__a	,		__a						)

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               UpperCamelCase__				=    state_dict.pop(__a						)
               UpperCamelCase__				=    val
          if "input_proj" in key:
               UpperCamelCase__				=    state_dict.pop(__a						)
               UpperCamelCase__				=    val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               UpperCamelCase__				=    state_dict.pop(__a						)
               UpperCamelCase__				=    val

    # finally, create HuggingFace model and load state dict
     UpperCamelCase__				=    DetaForObjectDetection(__a						)
     model.load_state_dict(__a						)
     model.eval()

     UpperCamelCase__				=    """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(__a						)

     # load image processor
     UpperCamelCase__				=    DetaImageProcessor(format="""coco_detection"""						)

     # verify our conversion on image
     UpperCamelCase__				=    prepare_img()
     UpperCamelCase__				=    processor(images=__a	,		return_tensors="""pt"""						)
     UpperCamelCase__				=    encoding["""pixel_values"""]
     UpperCamelCase__				=    model(pixel_values.to(__a						)						)

     # verify logits
     print("""Logits:"""	,		outputs.logits[0, :3, :3]						)
     print("""Boxes:"""	,		outputs.pred_boxes[0, :3, :3]						)
     if model_name == "deta-swin-large":
          UpperCamelCase__				=    torch.tensor(
              [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]						)
          UpperCamelCase__				=    torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]						)
     elif model_name == "deta-swin-large-o365":
          UpperCamelCase__				=    torch.tensor(
              [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]						)
          UpperCamelCase__				=    torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]						)
     assert torch.allclose(outputs.logits[0, :3, :3]	,		expected_logits.to(__a						)	,		atol=1E-4						)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,		expected_boxes.to(__a						)	,		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(__a						).mkdir(exist_ok=__a						)
          model.save_pretrained(__a						)
          processor.save_pretrained(__a						)

     # 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__":
    lowerCamelCase_						     =					argparse.ArgumentParser()

    parser.add_argument(
        '''--model_name''',
        type=str,
        default='''deta-swin-large''',
        choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
        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.'''
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""	,      SCREAMING_SNAKE_CASE_	,      )
       super().__init__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)





