
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()
lowercase_											=	logging.get_logger(__name__)


def     a__			(    snake_case    ):





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

  return orig_key


def     a__			(    snake_case    ):





  """simple docstring"""
  # transformer decoder self-attention layers
  for i in range(6    ):
    # read in weights + bias of input projection layer of self-attention
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''    )
    __SCREAMING_SNAKE_CASE     :					str			=						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
    __SCREAMING_SNAKE_CASE     :					List[str]			=						in_proj_weight[:256, :]
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						in_proj_bias[:256]
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						in_proj_weight[256:512, :]
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						in_proj_bias[256:512]
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						in_proj_weight[-256:, :]
    __SCREAMING_SNAKE_CASE     :					int			=						in_proj_bias[-256:]


def     a__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						"http://images.cocodataset.org/val2017/000000039769.jpg"
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						Image.open(requests.get(A_   ,	stream=A_    ).raw    )

  return im


@torch.no_grad()
def     a__			(    snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	snake_case   ,	):





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

  # load image processor
  __SCREAMING_SNAKE_CASE     :					Tuple			=						DeformableDetrImageProcessor(format='''coco_detection'''    )

  # prepare image
  __SCREAMING_SNAKE_CASE     :					Dict			=						prepare_img()
  __SCREAMING_SNAKE_CASE     :					int			=						image_processor(images=A_   ,	return_tensors='''pt'''    )
  __SCREAMING_SNAKE_CASE     :					Any			=						encoding["pixel_values"]

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

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

  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						"cuda" if torch.cuda.is_available() else "cpu"
  model.to(A_    )
  # verify our conversion
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						model(pixel_values.to(A_    )    )

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

  if single_scale:
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.tensor(
        [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]    )
    __SCREAMING_SNAKE_CASE     :					int			=						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:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						torch.tensor(
        [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]    )
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]    )

  if with_box_refine:
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.tensor(
        [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]    )
    __SCREAMING_SNAKE_CASE     :					str			=						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:
    __SCREAMING_SNAKE_CASE     :					str			=						torch.tensor(
        [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]    )
    __SCREAMING_SNAKE_CASE     :					Any			=						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(A_    )   ,	atol=1E-4    )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,	expected_boxes.to(A_    )   ,	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(A_    ).mkdir(exist_ok=A_    )
  model.save_pretrained(A_    )
  image_processor.save_pretrained(A_    )

  # Push to hub
  if push_to_hub:
    __SCREAMING_SNAKE_CASE     :					int			=						"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=A_   ,	organization='''nielsr'''   ,	commit_message='''Add model'''    )


if __name__ == "__main__":
   lowercase_											=	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."""
   )
   lowercase_											=	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,
   )


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_											=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_											=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	VOCAB_FILES_NAMES
     lowerCAmelCase_     =	PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_     =	['''input_ids''', '''attention_mask''', '''token_type_ids''']
     lowerCAmelCase_     =	DebertaTokenizer



     def __init__(							self    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """simple docstring"""




       super().__init__(
           _A      ,		_A      ,		tokenizer_file=_A      ,		errors=_A      ,		bos_token=_A      ,		eos_token=_A      ,		unk_token=_A      ,		sep_token=_A      ,		cls_token=_A      ,		pad_token=_A      ,		mask_token=_A      ,		add_prefix_space=_A      ,		**_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       if self._mask_token is None:
         if self.verbose:
           logger.error('''Using mask_token, but it is not set yet.'''				)
         return None
       return str(self._mask_token				)



     @mask_token.setter
     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep				) * [0]
       return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.get('''is_split_into_words'''      ,		_A				)
       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.get('''is_split_into_words'''      ,		_A				)

       assert self.add_prefix_space or not is_split_into_words, (
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(_A      ,		add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids
