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

     return orig_key







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Union[str, Any]:
     # transformer decoder self-attention layers
     for i in range(6       ):
          # read in weights + bias of input projection layer of self-attention
          a  :    Optional[int]				=						state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""       )
          a  :    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
          a  :    List[Any]				=						in_proj_weight[:2_5_6, :]
          a  :    int				=						in_proj_bias[:2_5_6]
          a  :    int				=						in_proj_weight[2_5_6:5_1_2, :]
          a  :    int				=						in_proj_bias[2_5_6:5_1_2]
          a  :    Any				=						in_proj_weight[-2_5_6:, :]
          a  :    Tuple				=						in_proj_bias[-2_5_6:]







def 	UpperCAmelCase       (		)	->	int:
     a  :    Tuple				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     a  :    Optional[int]				=						Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_       ).raw       )

     return im







@torch.no_grad()
def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )	->	Tuple:
     a  :    str				=						DeformableDetrConfig()
     # set config attributes
     if single_scale:
          a  :    List[Any]				=						1
     a  :    Optional[int]				=						dilation
     a  :    List[Any]				=						with_box_refine
     a  :    Any				=						two_stage
     # set labels
     a  :    int				=						9_1
     a  :    Union[str, Any]				=						'''huggingface/label-files'''
     a  :    Union[str, Any]				=						'''coco-detection-id2label.json'''
     a  :    Optional[int]				=						json.load(open(cached_download(hf_hub_url(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='''dataset'''       )       )						,      '''r'''       )       )
     a  :    Optional[Any]				=						{int(lowerCAmelCase_       ): v for k, v in idalabel.items()}
     a  :    str				=						idalabel
     a  :    List[Any]				=						{v: k for k, v in idalabel.items()}

     # load image processor
     a  :    Union[str, Any]				=						DeformableDetrImageProcessor(format='''coco_detection'''       )

     # prepare image
     a  :    Union[str, Any]				=						prepare_img()
     a  :    Union[str, Any]				=						image_processor(images=lowerCAmelCase_						,      return_tensors='''pt'''       )
     a  :    List[str]				=						encoding['''pixel_values''']

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

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

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

     a  :    Optional[int]				=						torch.tensor(
         [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]       )
     a  :    Tuple				=						torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]       )

     if single_scale:
          a  :    str				=						torch.tensor(
              [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]       )
          a  :    Optional[Any]				=						torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]       )

     if single_scale and dilation:
          a  :    str				=						torch.tensor(
              [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]       )
          a  :    List[str]				=						torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]       )

     if with_box_refine:
          a  :    List[Any]				=						torch.tensor(
              [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]       )
          a  :    Any				=						torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]       )

     if with_box_refine and two_stage:
          a  :    List[Any]				=						torch.tensor(
              [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]       )
          a  :    List[str]				=						torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]       )

     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:
          a  :    List[str]				=						'''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,
  )
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]