import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def     A__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    min(re.search("""^ +""" ,			_snake_case						).end() if t.startswith(""" """						) else 0 for t in string.splitlines()						)
    return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_snake_case						)]						)


lowerCAmelCase      :				str     			='''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

lowerCAmelCase      :				int     			='''\
---
---
# Begin of markdown

Some cool dataset card
'''


lowerCAmelCase      :				List[Any]     			='''\
# Begin of markdown

Some cool dataset card
'''




class 						__snake_case						(			unittest.TestCase		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->List[str]:


          """simple docstring"""


          with tempfile.TemporaryDirectory() as tmp_dir:
              _lowerCamelCase  :		List[str]			    =    Path(UpperCamelCase_) / '''README.md'''
              with open(UpperCamelCase_     ,	"""w+""") as readme_file:
                  readme_file.write(UpperCamelCase_)
              _lowerCamelCase  :		Optional[int]			    =    DatasetMetadata.from_readme(UpperCamelCase_)
              self.assertDictEqual(UpperCamelCase_     ,	{"""language""": ["""zh""", """en"""], """task_ids""": ["""sentiment-classification"""]})

              with open(UpperCamelCase_     ,	"""w+""") as readme_file:
                  readme_file.write(UpperCamelCase_)
              _lowerCamelCase  :		Dict			    =    DatasetMetadata.from_readme(UpperCamelCase_)
              self.assertDictEqual(UpperCamelCase_     ,	{})

              with open(UpperCamelCase_     ,	"""w+""") as readme_file:
                  readme_file.write(UpperCamelCase_)
              _lowerCamelCase  :		List[str]			    =    DatasetMetadata.from_readme(UpperCamelCase_)
              self.assertEqual(UpperCamelCase_     ,	{})




      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    _dedent(
              """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            """)
          assert DatasetMetadata.from_yaml_string(UpperCamelCase_)

          _lowerCamelCase  :		Optional[Any]			    =    _dedent(
              """\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            """)
          with self.assertRaises(UpperCamelCase_):
              DatasetMetadata.from_yaml_string(UpperCamelCase_)

          _lowerCamelCase  :		Union[str, Any]			    =    _dedent(
              """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            """)
          assert DatasetMetadata.from_yaml_string(UpperCamelCase_)








"""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()
snake_case__		:  List[str]     					=					logging.get_logger(__name__)


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

      return orig_key


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


def   _snake_case    (    ):
      lowerCAmelCase       :						List[str]							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase       :						Dict							= Image.open(requests.get(_snake_case			, stream=_snake_case      ).raw      )

      return im


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

      # load image processor
      lowerCAmelCase       :						str							= DeformableDetrImageProcessor(format='''coco_detection'''      )

      # prepare image
      lowerCAmelCase       :						Tuple							= prepare_img()
      lowerCAmelCase       :						int							= image_processor(images=_snake_case			, return_tensors='''pt'''      )
      lowerCAmelCase       :						int							= encoding['''pixel_values''']

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

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

      lowerCAmelCase       :						List[str]							= '''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(_snake_case      )
      # verify our conversion
      lowerCAmelCase       :						Dict							= model(pixel_values.to(_snake_case      )      )

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

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

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

      # Push to hub
      if push_to_hub:
            lowerCAmelCase       :						Optional[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=_snake_case			, organization='''nielsr'''			, commit_message='''Add model'''      )


if __name__ == "__main__":
 snake_case__		:  Union[str, 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.'''
 )
 snake_case__		:  Union[str, Any]     					=					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,
 )


