'''simple docstring'''
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



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

    return orig_key







def 			_UpperCamelCase			(  _a						:						Dict					,  _a						:						List[str]      ):




    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        __UpperCamelCase						:						Optional[int]		     =	orig_state_dict.pop(_a      )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase						:						Optional[int]		     =	val

    __UpperCamelCase						:						List[str]		     =	orig_state_dict['cls.predictions.decoder.bias']
    __UpperCamelCase						:						List[Any]		     =	torch.arange(_a      ).expand((1, -1)      ) + 2

    return orig_state_dict







def 			_UpperCamelCase			(  _a						:						Any					,  _a						:						List[Any]					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						List[Any]		     =	torch.load(_a					,  map_location='cpu'      )['model_state_dict']
    __UpperCamelCase						:						Tuple		     =	MraConfig.from_json_file(_a      )
    __UpperCamelCase						:						int		     =	MraForMaskedLM(_a      )

    __UpperCamelCase						:						Optional[int]		     =	convert_checkpoint_helper(config.max_position_embeddings					,  _a      )

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

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


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




'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



