"""simple docstring"""



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()
SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a							):
       __a     				=  SwinConfig(
           embed_dim=1_9_2 ,  depths=(2, 2, 1_8, 2) ,  num_heads=(6, 1_2, 2_4, 4_8) ,  window_size=1_2 ,  out_features=["stage2", "stage3", "stage4"] ,  )

       __a     				=  DetaConfig(
           backbone_config=a ,  num_queries=9_0_0 ,  encoder_ffn_dim=2_0_4_8 ,  decoder_ffn_dim=2_0_4_8 ,  num_feature_levels=5 ,  assign_first_stage=a ,  with_box_refine=a ,  two_stage=a ,  )

       # set labels
       __a     				=  """huggingface/label-files"""
       if "o365" in model_name:
              __a     				=  3_6_6
              __a     				=  """object365-id2label.json"""
       else:
              __a     				=  9_1
              __a     				=  """coco-detection-id2label.json"""

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

       return config



def 					_lowerCamelCase(					a							):
       __a     				=  []

       # 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 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  dct.pop(a							)
       __a     				=  val



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
       for i in range(len(backbone_config.depths							)							):
              __a     				=  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)
                     __a     				=  state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"							)
                     __a     				=  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
                     __a     				=  in_proj_weight[:dim, :]
                     __a     				=  in_proj_bias[: dim]
                     __a     				=  in_proj_weight[
                         dim : dim * 2, :
                     ]
                     __a     				=  in_proj_bias[
                         dim : dim * 2
                     ]
                     __a     				=  in_proj_weight[
                         -dim :, :
                     ]
                     __a     				=  in_proj_bias[-dim :]
                     # fmt: on



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



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

       return im



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  get_deta_config(a							)

       # load original state dict
       if model_name == "deta-swin-large":
              __a     				=  hf_hub_download(repo_id="nielsr/deta-checkpoints" ,  filename="adet_swin_ft.pth"							)
       elif model_name == "deta-swin-large-o365":
              __a     				=  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"							)

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

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

       # rename keys
       __a     				=  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:
                     __a     				=  state_dict.pop(a							)
                     __a     				=  val
              if "input_proj" in key:
                     __a     				=  state_dict.pop(a							)
                     __a     				=  val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     __a     				=  state_dict.pop(a							)
                     __a     				=  val

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

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

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

       # verify our conversion on image
       __a     				=  prepare_img()
       __a     				=  processor(images=a ,  return_tensors="pt"							)
       __a     				=  encoding["""pixel_values"""]
       __a     				=  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":
              __a     				=  torch.tensor(
                  [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]							)
              __a     				=  torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]							)
       elif model_name == "deta-swin-large-o365":
              __a     				=  torch.tensor(
                  [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]							)
              __a     				=  torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]							)
       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__":
   SCREAMING_SNAKE_CASE__:int			=	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."""
   )
   SCREAMING_SNAKE_CASE__:Tuple			=	parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
