


import datasets

from .evaluate import evaluate


snake_case__				:	Optional[Any]					       =					'\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n'

snake_case__				:	str					       =					'\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'

snake_case__				:	List[Any]					       =					'\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the CUAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'aupr\': Area Under the Precision-Recall curve\n    \'prec_at_80_recall\': Precision at 80% recall\n    \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> cuad_metric = datasets.load_metric("cuad")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION						)
class   SCREAMING_SNAKE_CASE_			(datasets.Metric						):




       '''simple docstring'''



       def 				_lowerCAmelCase							(						self				:				Dict							)					->Any:
          return datasets.MetricInfo(
              description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(
                  {
                      """predictions""": {
                          """id""": datasets.Value("""string"""							),
                          """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""							)							),
                      },
                      """references""": {
                          """id""": datasets.Value("""string"""							),
                          """answers""": datasets.features.Sequence(
                              {
                                  """text""": datasets.Value("""string"""							),
                                  """answer_start""": datasets.Value("""int32"""							),
                              }							),
                      },
                  }							)		,				codebase_urls=["""https://www.atticusprojectai.org/cuad"""]		,				reference_urls=["""https://www.atticusprojectai.org/cuad"""]		,				)




       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				Tuple		,				__a				:				Tuple							)					->Optional[int]:
          lowerCamelCase_						:   Optional[Any]		= {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
          lowerCamelCase_						:   Dict		= [
              {
                  """paragraphs""": [
                      {
                          """qas""": [
                              {
                                  """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                  """id""": ref["""id"""],
                              }
                              for ref in references
                          ]
                      }
                  ]
              }
          ]
          lowerCamelCase_						:   str		= evaluate(dataset=__a		,				predictions=__a							)
          return score









import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def    __lowerCamelCase   (      A__						: int					)     ->    List[str]:
   lowerCamelCase_						:   int		= 384
   if "tiny" in model_name:
      lowerCamelCase_						:   str		= [3, 3, 9, 3]
      lowerCamelCase_						:   List[str]		= [96, 192, 384, 768]
   if "small" in model_name:
      lowerCamelCase_						:   Any		= [3, 3, 27, 3]
      lowerCamelCase_						:   List[Any]		= [96, 192, 384, 768]
   if "base" in model_name:
      lowerCamelCase_						:   Optional[Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   int		= [128, 256, 512, 1024]
      lowerCamelCase_						:   int		= 512
   if "large" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Dict		= [192, 384, 768, 1536]
      lowerCamelCase_						:   Union[str, Any]		= 768
   if "xlarge" in model_name:
      lowerCamelCase_						:   Union[str, Any]		= [3, 3, 27, 3]
      lowerCamelCase_						:   Optional[int]		= [256, 512, 1024, 2048]
      lowerCamelCase_						:   List[str]		= 1024

   # set label information
   lowerCamelCase_						:   str		= 150
   lowerCamelCase_						:   List[Any]		= """huggingface/label-files"""
   lowerCamelCase_						:   int		= """ade20k-id2label.json"""
   lowerCamelCase_						:   List[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   Optional[Any]		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   int		= {v: k for k, v in idalabel.items()}

   lowerCamelCase_						:   Dict		= ConvNextConfig(
       depths=A__						,						hidden_sizes=A__						,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   lowerCamelCase_						:   str		= UperNetConfig(
       backbone_config=A__						,						auxiliary_in_channels=A__						,						num_labels=A__						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Optional[Any]:
   lowerCamelCase_						:   Tuple		= []

   # fmt: off
   # stem
   rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")					)
   rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")					)
   rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")					)
   rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")					)
   # stages
   for i in range(len(config.backbone_config.depths					)					):
      for j in range(config.backbone_config.depths[i]					):
         rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')					)
         rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')					)
      if i > 0:
         rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')					)
         rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')					)
         rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')					)
         rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')					)

      rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')					)
      rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')					)

   # decode head
   rename_keys.extend(
       [
           ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
           ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
           ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
           ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
       ]					)
   # fmt: on

   return rename_keys


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: List[Any]						,						A__						: int					)     ->    Union[str, Any]:
   lowerCamelCase_						:   Dict		= dct.pop(A__					)
   lowerCamelCase_						:   int		= val


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: str						,						A__						: Any					)     ->    Optional[int]:
   lowerCamelCase_						:   int		= {
       """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
       """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
       """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
       """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
       """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
   }
   lowerCamelCase_						:   List[str]		= model_name_to_url[model_name]
   lowerCamelCase_						:   Dict		= torch.hub.load_state_dict_from_url(A__						,						map_location="""cpu"""					)["""state_dict"""]

   lowerCamelCase_						:   List[Any]		= get_upernet_config(A__					)
   lowerCamelCase_						:   Any		= UperNetForSemanticSegmentation(A__					)
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   Tuple		= state_dict.pop(A__					)
      if "bn" in key:
         lowerCamelCase_						:   Dict		= key.replace("""bn"""						,						"""batch_norm"""					)
      lowerCamelCase_						:   Tuple		= val

   # rename keys
   lowerCamelCase_						:   List[Any]		= create_rename_keys(A__					)
   for src, dest in rename_keys:
      rename_key(A__						,						A__						,						A__					)

   model.load_state_dict(A__					)

   # verify on image
   lowerCamelCase_						:   Any		= """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
   lowerCamelCase_						:   Optional[int]		= Image.open(requests.get(A__						,						stream=A__					).raw					).convert("""RGB"""					)

   lowerCamelCase_						:   List[str]		= SegformerImageProcessor()
   lowerCamelCase_						:   Union[str, Any]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   with torch.no_grad():
      lowerCamelCase_						:   List[str]		= model(A__					)

   if model_name == "upernet-convnext-tiny":
      lowerCamelCase_						:   Dict		= torch.tensor(
          [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]					)
   elif model_name == "upernet-convnext-small":
      lowerCamelCase_						:   Optional[Any]		= torch.tensor(
          [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]					)
   elif model_name == "upernet-convnext-base":
      lowerCamelCase_						:   str		= torch.tensor(
          [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]					)
   elif model_name == "upernet-convnext-large":
      lowerCamelCase_						:   List[str]		= torch.tensor(
          [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]					)
   elif model_name == "upernet-convnext-xlarge":
      lowerCamelCase_						:   Any		= torch.tensor(
          [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]					)
   print("""Logits:"""						,						outputs.logits[0, 0, :3, :3]					)
   assert torch.allclose(outputs.logits[0, 0, :3, :3]						,						A__						,						atol=1e-4					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(A__					)
      print(f'''Saving processor to {pytorch_dump_folder_path}'''					)
      processor.save_pretrained(A__					)

   if push_to_hub:
      print(f'''Pushing model and processor for {model_name} to hub'''					)
      model.push_to_hub(f'''openmmlab/{model_name}'''					)
      processor.push_to_hub(f'''openmmlab/{model_name}'''					)


if __name__ == "__main__":
      snake_case__				:	Dict					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='upernet-convnext-tiny',
          type=str,
          choices=[F'upernet-convnext-{size}' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
          help='Name of the ConvNext UperNet model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )

      snake_case__				:	List[Any]					       =					parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





