code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import os
  import pickle
  import unittest
  from collections import UserDict, namedtuple
  
  import torch
  
  from accelerate.test_utils.testing import require_cuda, require_torch_min_version
  from accelerate.test_utils.training import RegressionModel
  from accelerate.utils import (
      convert_outputs_to_fpaa,
      extract_model_from_parallel,
      find_device,
      listify,
      patch_environment,
      recursively_apply,
      send_to_device,
  )
  
  
  SCREAMING_SNAKE_CASE_     				=   namedtuple('ExampleNamedTuple', 'a b c')
  
  
  
  
  
  
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					torch.randn(5							,2		)
            _UpperCAmelCase   	=					torch.device("cuda"		) if torch.cuda.is_available() else torch.device("cpu"		)
  
            _UpperCAmelCase   	=					send_to_device(lowerCamelCase_							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta.cpu()							,lowerCamelCase_		)		)
  
            _UpperCAmelCase   	=					send_to_device((tensor, [tensor, tensor], 1)							,lowerCamelCase_		)
            self.assertIsInstance(lowerCamelCase_							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta[0].cpu()							,lowerCamelCase_		)		)
            self.assertIsInstance(resulta[1]							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta[1][0].cpu()							,lowerCamelCase_		)		)
            self.assertTrue(torch.equal(resulta[1][1].cpu()							,lowerCamelCase_		)		)
            self.assertEqual(resulta[2]							,1		)
  
            _UpperCAmelCase   	=					send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1}							,lowerCamelCase_		)
            self.assertIsInstance(lowerCamelCase_							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta["a"].cpu()							,lowerCamelCase_		)		)
            self.assertIsInstance(resulta["b"]							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta["b"][0].cpu()							,lowerCamelCase_		)		)
            self.assertTrue(torch.equal(resulta["b"][1].cpu()							,lowerCamelCase_		)		)
            self.assertEqual(resulta["c"]							,1		)
  
            _UpperCAmelCase   	=					send_to_device(ExampleNamedTuple(a=lowerCamelCase_							,b=[tensor, tensor]							,c=1		)							,lowerCamelCase_		)
            self.assertIsInstance(lowerCamelCase_							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta.a.cpu()							,lowerCamelCase_		)		)
            self.assertIsInstance(resulta.b							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta.b[0].cpu()							,lowerCamelCase_		)		)
            self.assertTrue(torch.equal(resulta.b[1].cpu()							,lowerCamelCase_		)		)
            self.assertEqual(resulta.c							,1		)
  
            _UpperCAmelCase   	=					send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}		)							,lowerCamelCase_		)
            self.assertIsInstance(lowerCamelCase_							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta["a"].cpu()							,lowerCamelCase_		)		)
            self.assertIsInstance(resulta["b"]							,lowerCamelCase_		)
            self.assertTrue(torch.equal(resulta["b"][0].cpu()							,lowerCamelCase_		)		)
            self.assertTrue(torch.equal(resulta["b"][1].cpu()							,lowerCamelCase_		)		)
            self.assertEqual(resulta["c"]							,1		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            with self.assertRaises(lowerCamelCase_		) as cm:
               _UpperCAmelCase   	=					recursively_apply(torch.tensor							,(torch.tensor(1		), 1)							,error_on_other_type=lowerCamelCase_		)
            self.assertEqual(
                str(cm.exception		)							,"Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."							,)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					torch.tensor([1, 2, 3, 4, 5]		)
            self.assertEqual(listify(lowerCamelCase_		)							,[1, 2, 3, 4, 5]		)
  
            _UpperCAmelCase   	=					torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]		)
            self.assertEqual(listify(lowerCamelCase_		)							,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]		)
  
            _UpperCAmelCase   	=					torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]		)
            self.assertEqual(
                listify(lowerCamelCase_		)							,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            with patch_environment(aa=1							,BB=2		):
               self.assertEqual(os.environ.get("AA"		)							,"1"		)
               self.assertEqual(os.environ.get("BB"		)							,"2"		)
  
            self.assertNotIn("AA"							,os.environ		)
            self.assertNotIn("BB"							,os.environ		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					RegressionModel()
            _UpperCAmelCase   	=					model.forward
            _UpperCAmelCase   	=					convert_outputs_to_fpaa(model.forward		)
            _UpperCAmelCase   	=					extract_model_from_parallel(lowerCamelCase_							,keep_fpaa_wrapper=lowerCamelCase_		)
            _UpperCAmelCase   	=					pickle.dumps(lowerCamelCase_		)
  
  
  
  
  
  
  
         @require_cuda
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					RegressionModel()
            _UpperCAmelCase   	=					model.forward
            _UpperCAmelCase   	=					torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
            _UpperCAmelCase   	=					convert_outputs_to_fpaa(model.forward		)
            _UpperCAmelCase   	=					extract_model_from_parallel(lowerCamelCase_							,keep_fpaa_wrapper=lowerCamelCase_		)
            _UpperCAmelCase   	=					pickle.dumps(lowerCamelCase_		)
  
  
  
  
  
  
  
         @require_cuda
         @require_torch_min_version(version="2.0"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					RegressionModel()
            _UpperCAmelCase   	=					model.forward
            _UpperCAmelCase   	=					torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
            _UpperCAmelCase   	=					convert_outputs_to_fpaa(model.forward		)
            _UpperCAmelCase   	=					torch.compile(model.forward							,backend="inductor"		)
            _UpperCAmelCase   	=					torch.randn(4							,10		).cuda()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					RegressionModel()
            # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
            _UpperCAmelCase   	=					torch.nn.parallel.DataParallel(lowerCamelCase_		)
            _UpperCAmelCase   	=					extract_model_from_parallel(lowerCamelCase_		)
  
            self.assertEqual(lowerCamelCase_							,lowerCamelCase_		)
  
  
  
  
  
  
  
         @require_torch_min_version(version="2.0"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					RegressionModel()
            _UpperCAmelCase   	=					torch.compile(lowerCamelCase_		)
  
            # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
            _UpperCAmelCase   	=					torch.nn.parallel.DataParallel(lowerCamelCase_		)
            _UpperCAmelCase   	=					torch.compile(lowerCamelCase_		)
            _UpperCAmelCase   	=					extract_model_from_parallel(lowerCamelCase_		)
  
            self.assertEqual(compiled_model._orig_mod							,compiled_model_unwrapped._orig_mod		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3]		)]		)							,torch.device("cpu"		)		)
            self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3]		)}		)							,torch.device("cpu"		)		)
            self.assertIsNone(find_device([1, "a"]		)		)
  
  
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)
  
  