
"""simple docstring"""







import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_						    =					"\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"



class 		UpperCAmelCase_	(unittest.TestCase  , lowerCamelCase_						):
 """simple docstring"""




 def   a      ( self       : Optional[int] )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  load_tool("""summarization""" )
      self.tool.setup()
      UpperCAmelCase_						:    Optional[int]    					=  load_tool("""summarization"""     ,   remote=a_ )




 def   a      ( self       : Dict )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.tool(a_ )
      self.assertEqual(
          a_     ,   """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""     ,   )




 def   a      ( self       : List[str] )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.remote_tool(a_ )
      self.assertEqual(
          a_     ,   """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""     ,   )




 def   a      ( self       : Tuple )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.tool(text=a_ )
      self.assertEqual(
          a_     ,   """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""     ,   )






 def   a      ( self       : Tuple )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.remote_tool(text=a_ )
      self.assertEqual(
          a_     ,   """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""     ,   )



"""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()
lowercase_						    =					logging.get_logger(__name__)




def 			A_			(					lowercase      )     ->							List[Any]:


     """simple docstring"""







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

     return orig_key




def 			A_			(					lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







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




def 			A_			(					)     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCAmelCase_						:    Union[str, Any]    					=  Image.open(requests.get(lowercase						, stream=lowercase      ).raw      )

     return im




@torch.no_grad()
def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, )     ->							List[Any]:


     """simple docstring"""







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

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

     # prepare image
     UpperCAmelCase_						:    Tuple    					=  prepare_img()
     UpperCAmelCase_						:    Optional[int]    					=  image_processor(images=lowercase						, return_tensors="""pt"""      )
     UpperCAmelCase_						:    List[str]    					=  encoding["""pixel_values"""]

     logger.info("""Converting model..."""      )

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

     UpperCAmelCase_						:    Optional[Any]    					=  """cuda""" if torch.cuda.is_available() else """cpu"""
     model.to(lowercase      )
     # verify our conversion
     UpperCAmelCase_						:    Dict    					=  model(pixel_values.to(lowercase      )      )

     UpperCAmelCase_						:    Optional[int]    					=  torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
     UpperCAmelCase_						:    Dict    					=  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )

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

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

     # Push to hub
     if push_to_hub:
          UpperCAmelCase_						:    Any    					=  """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=lowercase						, 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,
       )

