


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
   import torch

if is_vision_available():
   import PIL


_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : Union[int, Iterable[int]]      ,					SCREAMING_SNAKE_CASE__      : bool      ,					SCREAMING_SNAKE_CASE__      : int    ):
     def constraint_to_multiple_of(SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : List[str]=0      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None    ):
          __UpperCamelCase   							=round(val / multiple    ) * multiple

          if max_val is not None and x > max_val:
               __UpperCamelCase   							=math.floor(val / multiple    ) * multiple

          if x < min_val:
               __UpperCamelCase   							=math.ceil(val / multiple    ) * multiple

          return x

     __UpperCamelCase   							=(output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) else output_size

     __UpperCamelCase							,				__UpperCamelCase   							=get_image_size(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase							,				__UpperCamelCase   							=output_size

     # determine new height and width
     __UpperCamelCase   							=output_height / input_height
     __UpperCamelCase   							=output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width    ) < abs(1 - scale_height    ):
               # fit width
               __UpperCamelCase   							=scale_width
          else:
               # fit height
               __UpperCamelCase   							=scale_height

     __UpperCamelCase   							=constraint_to_multiple_of(scale_height * input_height      ,					multiple=SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=constraint_to_multiple_of(scale_width * input_width      ,					multiple=SCREAMING_SNAKE_CASE__    )

     return (new_height, new_width)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BILINEAR ,       A_ = False ,       A_ = 1 ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'height': 384, 'width': 384}
         __UpperCamelCase   							=get_size_dict(A_   )
         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=keep_aspect_ratio
         __UpperCamelCase   							=ensure_multiple_of
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else IMAGENET_STANDARD_STD






    def _a (      self ,       A_ ,       A_ ,       A_ = False ,       A_ = 1 ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
         __UpperCamelCase   							=get_resize_output_image_size(
             A_ ,       output_size=(size['height'], size['width']) ,       keep_aspect_ratio=A_ ,       multiple=A_ ,       )
         return resize(A_ ,       size=A_ ,       resample=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	int:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         return normalize(A_ ,       mean=A_ ,       std=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_   )
         __UpperCamelCase   							=keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         __UpperCamelCase   							=ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase   							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std

         __UpperCamelCase   							=make_list_of_images(A_   )

         if not valid_images(A_   ):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'   )

         if do_resize and size is None or resample is None:
              raise ValueError('Size and resample must be specified if do_resize is True.'   )

         if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

         # All transformations expect numpy arrays.
         __UpperCamelCase   							=[to_numpy_array(A_   ) for image in images]

         if do_resize:
              __UpperCamelCase   							=[self.resize(image=A_ ,       size=A_ ,       resample=A_   ) for image in images]

         if do_rescale:
              __UpperCamelCase   							=[self.rescale(image=A_ ,       scale=A_   ) for image in images]

         if do_normalize:
              __UpperCamelCase   							=[self.normalize(image=A_ ,       mean=A_ ,       std=A_   ) for image in images]

         __UpperCamelCase   							=[to_channel_dimension_format(A_ ,       A_   ) for image in images]

         __UpperCamelCase   							={'pixel_values': images}
         return BatchFeature(data=A_ ,       tensor_type=A_   )






    def _a (      self ,       A_ ,       A_ = None   )  ->	Union[str, Any]:
         __UpperCamelCase   							=outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
              if len(A_   ) != len(A_   ):
                   raise ValueError(
                       'Make sure that you pass in as many target sizes as the batch dimension of the logits'   )

              if is_torch_tensor(A_   ):
                   __UpperCamelCase   							=target_sizes.numpy()

              __UpperCamelCase   							=[]

              for idx in range(len(A_   )   ):
                   __UpperCamelCase   							=torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0   ) ,       size=target_sizes[idx] ,       mode='bilinear' ,       align_corners=A_   )
                   __UpperCamelCase   							=resized_logits[0].argmax(dim=0   )
                   semantic_segmentation.append(A_   )
         else:
              __UpperCamelCase   							=logits.argmax(dim=1   )
              __UpperCamelCase   							=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

         return semantic_segmentation









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()
_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=SwinConfig(
         embed_dim=1_92      ,					depths=(2, 2, 18, 2)      ,					num_heads=(6, 12, 24, 48)      ,					window_size=12      ,					out_features=['stage2', 'stage3', 'stage4']      ,					)

     __UpperCamelCase   							=DetaConfig(
         backbone_config=SCREAMING_SNAKE_CASE__      ,					num_queries=9_00      ,					encoder_ffn_dim=20_48      ,					decoder_ffn_dim=20_48      ,					num_feature_levels=5      ,					assign_first_stage=SCREAMING_SNAKE_CASE__      ,					with_box_refine=SCREAMING_SNAKE_CASE__      ,					two_stage=SCREAMING_SNAKE_CASE__      ,					)

     # set labels
     __UpperCamelCase   							='huggingface/label-files'
     if "o365" in model_name:
          __UpperCamelCase   							=3_66
          __UpperCamelCase   							='object365-id2label.json'
     else:
          __UpperCamelCase   							=91
          __UpperCamelCase   							='coco-detection-id2label.json'

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

     return config

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[Any]    ):
     __UpperCamelCase   							=dct.pop(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=val

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : str    ):
     __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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
     # transformer decoder self-attention layers
     __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 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							='http://images.cocodataset.org/val2017/000000039769.jpg'
     __UpperCamelCase   							=Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,					stream=SCREAMING_SNAKE_CASE__    ).raw    )

     return im

@torch.no_grad()
def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=get_deta_config(SCREAMING_SNAKE_CASE__    )

     # 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(SCREAMING_SNAKE_CASE__      ,					map_location='cpu'    )['model']

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

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

     # 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(SCREAMING_SNAKE_CASE__    )
               __UpperCamelCase   							=val
          if "input_proj" in key:
               __UpperCamelCase   							=state_dict.pop(SCREAMING_SNAKE_CASE__    )
               __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(SCREAMING_SNAKE_CASE__    )
               __UpperCamelCase   							=val

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

     __UpperCamelCase   							='cuda' if torch.cuda.is_available() else 'cpu'
     model.to(SCREAMING_SNAKE_CASE__    )

     # load image processor
     __UpperCamelCase   							=DetaImageProcessor(format='coco_detection'    )

     # verify our conversion on image
     __UpperCamelCase   							=prepare_img()
     __UpperCamelCase   							=processor(images=SCREAMING_SNAKE_CASE__      ,					return_tensors='pt'    )
     __UpperCamelCase   							=encoding['pixel_values']
     __UpperCamelCase   							=model(pixel_values.to(SCREAMING_SNAKE_CASE__    )    )

     # 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(
              [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]    )
          __UpperCamelCase   							=torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]    )
     elif model_name == "deta-swin-large-o365":
          __UpperCamelCase   							=torch.tensor(
              [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]    )
          __UpperCamelCase   							=torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]    )
     assert torch.allclose(outputs.logits[0, :3, :3]      ,					expected_logits.to(SCREAMING_SNAKE_CASE__    )      ,					atol=1E-4    )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,					expected_boxes.to(SCREAMING_SNAKE_CASE__    )      ,					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(SCREAMING_SNAKE_CASE__    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
          model.save_pretrained(SCREAMING_SNAKE_CASE__    )
          processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

     # 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__":
   _A			=						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.'
   )
   _A			=						parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





