
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



"""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)

