




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				if "model" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""model."""						,			""""""   )
				if "norm1" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""norm1"""						,			"""attention.output.LayerNorm"""   )
				if "norm2" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""norm2"""						,			"""output.LayerNorm"""   )
				if "norm" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""norm"""						,			"""LayerNorm"""   )
				if "transformer" in orig_key:
								__lowerCAmelCase					=  orig_key.split("""."""   )[0].split("""_"""   )[-1]
								__lowerCAmelCase					=  orig_key.replace(F'''transformer_{layer_num}'''						,			F'''encoder.layer.{layer_num}'''   )
				if "mha.attn" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""mha.attn"""						,			"""attention.self"""   )
				if "mha" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""mha"""						,			"""attention"""   )
				if "W_q" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""W_q"""						,			"""self.query"""   )
				if "W_k" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""W_k"""						,			"""self.key"""   )
				if "W_v" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""W_v"""						,			"""self.value"""   )
				if "ff.0" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""ff.0"""						,			"""intermediate.dense"""   )
				if "ff.2" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""ff.2"""						,			"""output.dense"""   )
				if "ff" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""ff"""						,			"""output.dense"""   )
				if "mlm_class" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""mlm.mlm_class"""						,			"""cls.predictions.decoder"""   )
				if "mlm" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""mlm"""						,			"""cls.predictions.transform"""   )
				if "backbone.backbone.encoders" in orig_key:
								__lowerCAmelCase					=  orig_key.replace("""backbone.backbone.encoders"""						,			"""encoder.layer"""   )
				if "cls" not in orig_key:
								__lowerCAmelCase					=  """mra.""" + orig_key

				return orig_key





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				for key in orig_state_dict.copy().keys():
								__lowerCAmelCase					=  orig_state_dict.pop(UpperCamelCase__   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__lowerCAmelCase					=  val

				__lowerCAmelCase					=  orig_state_dict["""cls.predictions.decoder.bias"""]
				__lowerCAmelCase					=  torch.arange(UpperCamelCase__   ).expand((1, -1)   ) + 2

				return orig_state_dict





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[int]:







				'''simple docstring'''


				__lowerCAmelCase					=  torch.load(UpperCamelCase__						,			map_location="""cpu"""   )["""model_state_dict"""]
				__lowerCAmelCase					=  MraConfig.from_json_file(UpperCamelCase__   )
				__lowerCAmelCase					=  MraForMaskedLM(UpperCamelCase__   )

				__lowerCAmelCase					=  convert_checkpoint_helper(config.max_position_embeddings						,			UpperCamelCase__   )

				print(model.load_state_dict(UpperCamelCase__   )   )
				model.eval()
				model.save_pretrained(UpperCamelCase__   )

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''   )


if __name__ == "__main__":
	__A						:		str          =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
	)
	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The json file for Mra model config.",
	)
	parser.add_argument(
	    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	__A						:		Tuple          =						parser.parse_args()
	convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase					=  min(re.search("""^ +"""						,			UpperCamelCase__   ).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(UpperCamelCase__   )]   )


__A						:		str          =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A						:		int          =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A						:		Any          =						"\\n# Begin of markdown\n\nSome cool dataset card\n"

class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


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

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

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertEqual(_lowercase,     {})







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  _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(_lowercase)

									__lowerCAmelCase					=  _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(_lowercase):
													DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _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(_lowercase)





