
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()
a__				:      Optional[int]    		=     logging.get_logger(__name__)







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return orig_key







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Tuple				      = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = Image.open(requests.get(a__ ,						stream=a__   ).raw   )

    return im







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



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DeformableDetrConfig()
    # set config attributes
    if single_scale:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = 1
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dilation
    SCREAMING_SNAKE_CASE				:				str				      = with_box_refine
    SCREAMING_SNAKE_CASE				:				Tuple				      = two_stage
    # set labels
    SCREAMING_SNAKE_CASE				:				str				      = 91
    SCREAMING_SNAKE_CASE				:				List[str]				      = '''huggingface/label-files'''
    SCREAMING_SNAKE_CASE				:				Dict				      = '''coco-detection-id2label.json'''
    SCREAMING_SNAKE_CASE				:				str				      = json.load(open(cached_download(hf_hub_url(a__ ,						a__ ,						repo_type='''dataset'''   )   ) ,						'''r'''   )   )
    SCREAMING_SNAKE_CASE				:				int				      = {int(a__   ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = idalabel
    SCREAMING_SNAKE_CASE				:				Optional[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				:				str				      = prepare_img()
    SCREAMING_SNAKE_CASE				:				Dict				      = image_processor(images=a__ ,						return_tensors='''pt'''   )
    SCREAMING_SNAKE_CASE				:				str				      = encoding['''pixel_values''']

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

    # load original state dict
    SCREAMING_SNAKE_CASE				:				Dict				      = torch.load(a__ ,						map_location='''cpu'''   )['''model''']
    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = state_dict.pop(a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = 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				:				Tuple				      = '''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith('''class_embed'''   ) and not key.startswith('''bbox_embed'''   ):
            SCREAMING_SNAKE_CASE				:				int				      = state_dict.pop(a__   )
            SCREAMING_SNAKE_CASE				:				Tuple				      = val
    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE				:				Tuple				      = DeformableDetrForObjectDetection(a__   )
    model.load_state_dict(a__   )
    model.eval()

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(a__   )
    # verify our conversion
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(pixel_values.to(a__   )   )

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.tensor(
        [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]   )

    if single_scale:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
            [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]   )

    if single_scale and dilation:
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor(
            [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]   )

    if with_box_refine:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = torch.tensor(
            [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]   )

    if with_box_refine and two_stage:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor(
            [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]   )
        SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]   )

    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				:				Tuple				      = '''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__":
      a__				:      List[Any]    		=     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.'''
      )
      a__				:      Tuple    		=     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,
      )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				:      List[Any]    		=     {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     [
          '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DecisionTransformerGPT2Model''',
          '''DecisionTransformerGPT2PreTrainedModel''',
          '''DecisionTransformerModel''',
          '''DecisionTransformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      a__				:      str    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
