
'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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

def   A			(							A_				:       int							):






						if "backbone.0.body" in orig_key:
												snake_case							:  List[str]			=     orig_key.replace('''backbone.0.body'''     ,				'''backbone.conv_encoder.model'''							)
						if "transformer" in orig_key:
												snake_case							:  Dict			=     orig_key.replace('''transformer.'''     ,				''''''							)
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		snake_case							:  Tuple			=     orig_key.replace('''norm1'''     ,				'''self_attn_layer_norm'''							)
												else:
																		snake_case							:  Union[str, Any]			=     orig_key.replace('''norm1'''     ,				'''encoder_attn_layer_norm'''							)
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		snake_case							:  List[Any]			=     orig_key.replace('''norm2'''     ,				'''final_layer_norm'''							)
												else:
																		snake_case							:  int			=     orig_key.replace('''norm2'''     ,				'''self_attn_layer_norm'''							)
						if "norm3" in orig_key:
												snake_case							:  Optional[Any]			=     orig_key.replace('''norm3'''     ,				'''final_layer_norm'''							)
						if "linear1" in orig_key:
												snake_case							:  Tuple			=     orig_key.replace('''linear1'''     ,				'''fc1'''							)
						if "linear2" in orig_key:
												snake_case							:  Optional[Any]			=     orig_key.replace('''linear2'''     ,				'''fc2'''							)
						if "query_embed" in orig_key:
												snake_case							:  str			=     orig_key.replace('''query_embed'''     ,				'''query_position_embeddings'''							)
						if "cross_attn" in orig_key:
												snake_case							:  Any			=     orig_key.replace('''cross_attn'''     ,				'''encoder_attn'''							)

						return orig_key

def   A			(							A_				:       Any							):






						for i in range(6							):
												# read in weights + bias of input projection layer of self-attention
												snake_case							:  Optional[Any]			=     state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""							)
												snake_case							:  List[Any]			=     state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""							)
												# next, add query, keys and values (in that order) to the state dict
												snake_case							:  Dict			=     in_proj_weight[:256, :]
												snake_case							:  Optional[int]			=     in_proj_bias[:256]
												snake_case							:  Union[str, Any]			=     in_proj_weight[256:512, :]
												snake_case							:  List[Any]			=     in_proj_bias[256:512]
												snake_case							:  int			=     in_proj_weight[-256:, :]
												snake_case							:  List[str]			=     in_proj_bias[-256:]

def   A			(							):
						snake_case							:  List[str]			=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case							:  int			=     Image.open(requests.get(__lowerCamelCase     ,				stream=__lowerCamelCase							).raw							)

						return im

@torch.no_grad()
def   A			(							A_				:       str     ,				A_				:       str     ,				A_				:       Dict     ,				A_				:       Optional[Any]     ,				A_				:       Union[str, Any]     ,				A_				:       Union[str, Any]     ,				A_				:       Dict     ,				):
						snake_case							:  Any			=     DeformableDetrConfig()
						# set config attributes
						if single_scale:
												snake_case							:  Any			=     1
						snake_case							:  Optional[Any]			=     dilation
						snake_case							:  Optional[int]			=     with_box_refine
						snake_case							:  Optional[int]			=     two_stage
						# set labels
						snake_case							:  Optional[int]			=     91
						snake_case							:  Dict			=     '''huggingface/label-files'''
						snake_case							:  List[str]			=     '''coco-detection-id2label.json'''
						snake_case							:  List[Any]			=     json.load(open(cached_download(hf_hub_url(__lowerCamelCase     ,				__lowerCamelCase     ,				repo_type='''dataset'''							)							)     ,				'''r'''							)							)
						snake_case							:  Union[str, Any]			=     {int(__lowerCamelCase							): v for k, v in idalabel.items()}
						snake_case							:  int			=     idalabel
						snake_case							:  Optional[Any]			=     {v: k for k, v in idalabel.items()}

						# load image processor
						snake_case							:  str			=     DeformableDetrImageProcessor(format='''coco_detection'''							)

						# prepare image
						snake_case							:  Any			=     prepare_img()
						snake_case							:  Tuple			=     image_processor(images=__lowerCamelCase     ,				return_tensors='''pt'''							)
						snake_case							:  List[str]			=     encoding['''pixel_values''']

						logger.info('''Converting model...'''							)

						# load original state dict
						snake_case							:  int			=     torch.load(__lowerCamelCase     ,				map_location='''cpu'''							)['''model''']
						# rename keys
						for key in state_dict.copy().keys():
												snake_case							:  str			=     state_dict.pop(__lowerCamelCase							)
												snake_case							:  List[Any]			=     val
						# query, key and value matrices need special treatment
						read_in_q_k_v(__lowerCamelCase							)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						snake_case							:  Any			=     '''model.'''
						for key in state_dict.copy().keys():
												if not key.startswith('''class_embed'''							) and not key.startswith('''bbox_embed'''							):
																		snake_case							:  Optional[int]			=     state_dict.pop(__lowerCamelCase							)
																		snake_case							:  Optional[int]			=     val
    # finally, create HuggingFace model and load state dict
						snake_case							:  Union[str, Any]			=     DeformableDetrForObjectDetection(__lowerCamelCase							)
						model.load_state_dict(__lowerCamelCase							)
						model.eval()

						snake_case							:  Union[str, Any]			=     '''cuda''' if torch.cuda.is_available() else '''cpu'''
						model.to(__lowerCamelCase							)
						# verify our conversion
						snake_case							:  int			=     model(pixel_values.to(__lowerCamelCase							)							)

						snake_case							:  Tuple			=     torch.tensor(
						    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]							)
						snake_case							:  List[Any]			=     torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]							)

						if single_scale:
												snake_case							:  Tuple			=     torch.tensor(
												    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]							)
												snake_case							:  List[Any]			=     torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]							)

						if single_scale and dilation:
												snake_case							:  str			=     torch.tensor(
												    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]							)
												snake_case							:  int			=     torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]							)

						if with_box_refine:
												snake_case							:  List[str]			=     torch.tensor(
												    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]							)
												snake_case							:  Tuple			=     torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]							)

						if with_box_refine and two_stage:
												snake_case							:  int			=     torch.tensor(
												    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]							)
												snake_case							:  str			=     torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]							)

						print('''Logits:'''     ,				outputs.logits[0, :3, :3]							)

						assert torch.allclose(outputs.logits[0, :3, :3]     ,				expected_logits.to(__lowerCamelCase							)     ,				atol=1e-4							)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,				expected_boxes.to(__lowerCamelCase							)     ,				atol=1e-4							)

						print('''Everything ok!'''							)

						# Save model and image processor
						logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
						Path(__lowerCamelCase							).mkdir(exist_ok=__lowerCamelCase							)
						model.save_pretrained(__lowerCamelCase							)
						image_processor.save_pretrained(__lowerCamelCase							)

						# Push to hub
						if push_to_hub:
												snake_case							:  Dict			=     '''deformable-detr'''
												model_name += "-single-scale" if single_scale else ""
												model_name += "-dc5" if dilation else ""
												model_name += "-with-box-refine" if with_box_refine else ""
												model_name += "-two-stage" if two_stage else ""
												print('''Pushing model to hub...'''							)
												model.push_to_hub(repo_path_or_name=__lowerCamelCase     ,				organization='''nielsr'''     ,				commit_message='''Add model'''							)


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

						parser.add_argument(
						    "--checkpoint_path",
						    type=str,
						    default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
						    help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
						)
						parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
						parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
						parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
						parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    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."
						)
						UpperCAmelCase            = parser.parse_args()
						convert_deformable_detr_checkpoint(
						    args.checkpoint_path,
						    args.single_scale,
						    args.dilation,
						    args.with_box_refine,
						    args.two_stage,
						    args.pytorch_dump_folder_path,
						    args.push_to_hub,
						)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__					  =		{
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		[
       "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Swin2SRForImageSuperResolution",
       "Swin2SRModel",
       "Swin2SRPreTrainedModel",
   ]


try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		["Swin2SRImageProcessor"]


if TYPE_CHECKING:
   from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swinasr import (
          SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinaSRForImageSuperResolution,
          SwinaSRModel,
          SwinaSRPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_swinasr import SwinaSRImageProcessor


else:
   import sys

   lowercase__					  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

