


import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE :					List[Any]			=	[]
for i in range(6):
   # 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"""),
    ]
)







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					Any			)  ->   List[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     state_dict.pop(__lowerCamelCase			)
     UpperCamelCase__							:  Optional[int]								=     val







def 						_lowercase       (     __lowerCamelCase			:					List[Any]			)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               UpperCamelCase__							:  Dict								=     key.replace('''backbone.0.body'''		,'''backbone.conv_encoder.model'''			)
               UpperCamelCase__							:  Optional[int]								=     value
          else:
               UpperCamelCase__							:  Optional[int]								=     value

     return new_state_dict







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Optional[Any]=False			)  ->   Any:





     '''simple docstring'''





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







def 						_lowercase       (     )  ->   int:





     '''simple docstring'''





     UpperCamelCase__							:  int								=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCamelCase__							:  str								=     Image.open(requests.get(__lowerCamelCase		,stream=__lowerCamelCase			).raw			)

     return im







@torch.no_grad()
def 						_lowercase       (     __lowerCamelCase			:					Tuple		,__lowerCamelCase			:					Any			)  ->   Any:





     '''simple docstring'''





     UpperCamelCase__							:  List[Any]								=     DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          UpperCamelCase__							:  List[Any]								=     '''resnet101'''
     if "dc5" in model_name:
          UpperCamelCase__							:  str								=     True
     UpperCamelCase__							:  List[Any]								=     '''panoptic''' in model_name
     if is_panoptic:
          UpperCamelCase__							:  Tuple								=     250
     else:
          UpperCamelCase__							:  Dict								=     91
          UpperCamelCase__							:  Union[str, Any]								=     '''huggingface/label-files'''
          UpperCamelCase__							:  Optional[int]								=     '''coco-detection-id2label.json'''
          UpperCamelCase__							:  List[str]								=     json.load(open(hf_hub_download(__lowerCamelCase		,__lowerCamelCase		,repo_type='''dataset'''			)		,'''r'''			)			)
          UpperCamelCase__							:  Any								=     {int(__lowerCamelCase			): v for k, v in idalabel.items()}
          UpperCamelCase__							:  str								=     idalabel
          UpperCamelCase__							:  List[Any]								=     {v: k for k, v in idalabel.items()}

     # load image processor
     UpperCamelCase__							:  str								=     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
     UpperCamelCase__							:  Tuple								=     DetrImageProcessor(format=__lowerCamelCase			)

     # prepare image
     UpperCamelCase__							:  int								=     prepare_img()
     UpperCamelCase__							:  Optional[Any]								=     image_processor(images=__lowerCamelCase		,return_tensors='''pt'''			)
     UpperCamelCase__							:  List[Any]								=     encoding['''pixel_values''']

     logger.info(F'Converting model {model_name}...'			)

     # load original model from torch hub
     UpperCamelCase__							:  Optional[Any]								=     torch.hub.load('''facebookresearch/detr'''		,__lowerCamelCase		,pretrained=__lowerCamelCase			).eval()
     UpperCamelCase__							:  Dict								=     detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               UpperCamelCase__							:  Union[str, Any]								=     '''detr.''' + src
          rename_key(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
     UpperCamelCase__							:  Dict								=     rename_backbone_keys(__lowerCamelCase			)
     # query, key and value matrices need special treatment
     read_in_q_k_v(__lowerCamelCase		,is_panoptic=__lowerCamelCase			)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCamelCase__							:  Union[str, Any]								=     '''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'''			)
               ):
                    UpperCamelCase__							:  int								=     state_dict.pop(__lowerCamelCase			)
                    UpperCamelCase__							:  Dict								=     val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCamelCase__							:  Dict								=     state_dict.pop(__lowerCamelCase			)
                    UpperCamelCase__							:  Optional[int]								=     val
               elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
                    continue
               else:
                    UpperCamelCase__							:  int								=     state_dict.pop(__lowerCamelCase			)
                    UpperCamelCase__							:  str								=     val
          else:
               if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
                    UpperCamelCase__							:  int								=     state_dict.pop(__lowerCamelCase			)
                    UpperCamelCase__							:  str								=     val
    # finally, create HuggingFace model and load state dict
     UpperCamelCase__							:  Dict								=     DetrForSegmentation(__lowerCamelCase			) if is_panoptic else DetrForObjectDetection(__lowerCamelCase			)
     model.load_state_dict(__lowerCamelCase			)
     model.eval()
     # verify our conversion
     UpperCamelCase__							:  Dict								=     detr(__lowerCamelCase			)
     UpperCamelCase__							:  Optional[Any]								=     model(__lowerCamelCase			)
     assert torch.allclose(outputs.logits		,original_outputs['''pred_logits''']		,atol=1e-4			)
     assert torch.allclose(outputs.pred_boxes		,original_outputs['''pred_boxes''']		,atol=1e-4			)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks		,original_outputs['''pred_masks''']		,atol=1e-4			)

     # 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			)


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE :					Optional[Any]			=	argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""", default="""detr_resnet50""", type=str, 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."""
   )
   _SCREAMING_SNAKE_CASE :					List[str]			=	parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					str			=	{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	[
       """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DonutSwinModel""",
       """DonutSwinPreTrainedModel""",
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	["""DonutFeatureExtractor"""]
   _SCREAMING_SNAKE_CASE :					Tuple			=	["""DonutImageProcessor"""]


if TYPE_CHECKING:
   from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
   from .processing_donut import DonutProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_donut_swin import (
          DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          DonutSwinModel,
          DonutSwinPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_donut import DonutFeatureExtractor
      from .image_processing_donut import DonutImageProcessor

else:
   import sys

   _SCREAMING_SNAKE_CASE :					Optional[Any]			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




