'''simple docstring'''

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 					UpperCamelCase_						( snake_case_	:	List[Any]				) ->					Dict:






 '''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_						( snake_case_	:	Optional[Any]						,		snake_case_	:	List[Any]				) ->					Tuple:






 '''simple docstring'''

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

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

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

 return orig_state_dict







def 					UpperCamelCase_						( snake_case_	:	int						,		snake_case_	:	Optional[int]						,		snake_case_	:	Any				) ->					int:






 '''simple docstring'''

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

 __lowerCAmelCase    							=						convert_checkpoint_helper(config.max_position_embeddings						,		snake_case_				)

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

 print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""				)


if __name__ == "__main__":
 _A      :				Tuple 				=    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      :				Any 				=    parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(snake_case_						,		snake_case_						,		snake_case_				)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to a folder where the PyTorch model will be placed.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






