





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


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

  return orig_key


def 			lowerCamelCase_		(					lowerCAmelCase:		List[Any]			,							lowerCAmelCase:		Union[str, Any] )->						Any:
  for key in orig_state_dict.copy().keys():
    _snake_case      : Dict														=		orig_state_dict.pop(lowerCAmelCase )

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

  _snake_case      : Tuple														=		orig_state_dict['cls.predictions.decoder.bias']
  _snake_case      : List[Any]														=		torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

  return orig_state_dict


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		int )->						List[str]:
  _snake_case      : Optional[Any]														=		torch.load(lowerCAmelCase			,							map_location='cpu' )['model_state_dict']
  _snake_case      : Optional[Any]														=		MraConfig.from_json_file(lowerCAmelCase )
  _snake_case      : Any														=		MraForMaskedLM(lowerCAmelCase )

  _snake_case      : List[Any]														=		convert_checkpoint_helper(config.max_position_embeddings			,							lowerCAmelCase )

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

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


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





import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowerCAmelCase__							= datasets.utils.logging.get_logger(__name__)
@dataclass
class 				a_ (						datasets.BuilderConfig						):






 '''simple docstring'''






 UpperCAmelCase_	 =					None






class 				a_ (						datasets.ArrowBasedBuilder						):






 '''simple docstring'''






 UpperCAmelCase_	 =					ArrowConfig




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        return datasets.DatasetInfo(features=self.config.features)




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  str):







        '''simple docstring'''



        if not self.config.data_files:
               raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""")
        lowerCAmelCase__     	=					dl_manager.download_and_extract(self.config.data_files)
        if isinstance(lowercase__			,    (str, list, tuple)):
               lowerCAmelCase__     	=					data_files
               if isinstance(lowercase__			,    lowercase__):
                      lowerCAmelCase__     	=					[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowerCAmelCase__     	=					[dl_manager.iter_files(lowercase__) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,    gen_kwargs={'files': files})]
        lowerCAmelCase__     	=					[]
        for split_name, files in data_files.items():
               if isinstance(lowercase__			,    lowercase__):
                      lowerCAmelCase__     	=					[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowerCAmelCase__     	=					[dl_manager.iter_files(lowercase__) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(lowercase__):
                             with open(lowercase__			,    'rb') as f:
                                    lowerCAmelCase__     	=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(lowercase__).schema)
                             break
               splits.append(datasets.SplitGenerator(name=lowercase__			,    gen_kwargs={'files': files}))
        return splits




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  pa.Table):







        '''simple docstring'''



        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               lowerCAmelCase__     	=					table_cast(lowercase__			,    self.info.features.arrow_schema)
        return pa_table




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  str):







        '''simple docstring'''



        for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase__)):
               with open(lowercase__			,    'rb') as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(lowercase__)):
                                    lowerCAmelCase__     	=					pa.Table.from_batches([record_batch])
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield F"""{file_idx}_{batch_idx}""", self._cast_table(lowercase__)
                      except ValueError as e:
                             logger.error(F"""Failed to read file '{file}' with error {type(lowercase__)}: {e}""")
                             raise
