code: 
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional, Tuple
  
  from .training_args import TrainingArguments
  from .utils import cached_property, is_tf_available, logging, requires_backends
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)
  
  if is_tf_available():
    import tensorflow as tf
  
  
  
  
  
  
  @dataclass
  class     __a			(					lowerCAmelCase_			):
         _lowerCamelCase							:							List[Any]       =			'tf'
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Name of TPU'}				,	)
  
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Zone of TPU'}				,	)
  
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Name of Cloud TPU-enabled project'}				,	)
  
         _lowerCamelCase							:							float       =			field(
             default=1.0				,	metadata={'help': 'Power for the Polynomial decay LR scheduler.'}				,	)
  
         _lowerCamelCase							:							bool       =			field(default=lowerCAmelCase_				,	metadata={'help': 'Whether to activate the XLA compilation or not'}			)
  
  
  
  
  
  
  
         @cached_property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            logger.info("Tensorflow: setting up strategy"		)
  
            _UpperCAmelCase   	=					tf.config.list_physical_devices("GPU"		)
  
            # Set to float16 at first
            if self.fpaa:
               tf.keras.mixed_precision.set_global_policy("mixed_float16"		)
  
            if self.no_cuda:
               _UpperCAmelCase   	=					tf.distribute.OneDeviceStrategy(device="/cpu:0"		)
            else:
               try:
                  if self.tpu_name:
                     _UpperCAmelCase   	=					tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name							,zone=self.tpu_zone							,project=self.gcp_project		)
                  else:
                     _UpperCAmelCase   	=					tf.distribute.cluster_resolver.TPUClusterResolver()
               except ValueError:
                  if self.tpu_name:
                     raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''		)
                  else:
                     _UpperCAmelCase   	=					None
  
               if tpu:
                  # Set to bfloat16 in case of TPU
                  if self.fpaa:
                     tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"		)
  
                  tf.config.experimental_connect_to_cluster(lowerCamelCase_		)
                  tf.tpu.experimental.initialize_tpu_system(lowerCamelCase_		)
  
                  _UpperCAmelCase   	=					tf.distribute.TPUStrategy(lowerCamelCase_		)
  
               elif len(lowerCamelCase_		) == 0:
                  _UpperCAmelCase   	=					tf.distribute.OneDeviceStrategy(device="/cpu:0"		)
               elif len(lowerCamelCase_		) == 1:
                  _UpperCAmelCase   	=					tf.distribute.OneDeviceStrategy(device="/gpu:0"		)
               elif len(lowerCamelCase_		) > 1:
                  # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                  _UpperCAmelCase   	=					tf.distribute.MirroredStrategy()
               else:
                  raise ValueError("Cannot find the proper strategy, please check your environment properties."		)
  
            return strategy
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            return self._setup_strategy
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            if self.per_gpu_train_batch_size:
               logger.warning(
                   "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                   "version. Using `--per_device_train_batch_size` is preferred."		)
            _UpperCAmelCase   	=					self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            if self.per_gpu_eval_batch_size:
               logger.warning(
                   "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                   "version. Using `--per_device_eval_batch_size` is preferred."		)
            _UpperCAmelCase   	=					self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            warnings.warn(
                "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."							,lowerCamelCase_							,)
            return self._setup_strategy.num_replicas_in_sync
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
     if "model" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
     if "norm1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
     if "norm2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
     if "norm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
        _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
     if "mha" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
     if "W_q" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
     if "W_k" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
     if "W_v" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
     if "ff.0" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
     if "ff.2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
     if "ff" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
     if "mlm_class" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
     if "mlm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
     if "backbone.backbone.encoders" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
     if "cls" not in orig_key:
        _UpperCAmelCase   	=					"mra." + orig_key
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
     for key in orig_state_dict.copy().keys():
        _UpperCAmelCase   	=					orig_state_dict.pop(a_				)
  
        if ("pooler" in key) or ("sen_class" in key):
           continue
        else:
           _UpperCAmelCase   	=					val
  
     _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
     _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2
  
     return orig_state_dict
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
     _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
     _UpperCAmelCase   	=					MraForMaskedLM(a_				)
  
     _UpperCAmelCase   	=					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__":
    SCREAMING_SNAKE_CASE_     				=   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.'
    )
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  