




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			:      List[Any]      			=namedtuple("""ExampleNamedTuple""", """a b c""")






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[int]	) ->				Optional[Any]:
           lowerCamelCase__					     = torch.randn(5		,     2	)
           lowerCamelCase__					     = torch.device('cuda'	) if torch.cuda.is_available() else torch.device('cpu'	)

           lowerCamelCase__					     = send_to_device(A__		,     A__	)
           self.assertTrue(torch.equal(resulta.cpu()		,     A__	)	)

           lowerCamelCase__					     = send_to_device((tensor, [tensor, tensor], 1)		,     A__	)
           self.assertIsInstance(A__		,     A__	)
           self.assertTrue(torch.equal(resulta[0].cpu()		,     A__	)	)
           self.assertIsInstance(resulta[1]		,     A__	)
           self.assertTrue(torch.equal(resulta[1][0].cpu()		,     A__	)	)
           self.assertTrue(torch.equal(resulta[1][1].cpu()		,     A__	)	)
           self.assertEqual(resulta[2]		,     1	)

           lowerCamelCase__					     = send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}		,     A__	)
           self.assertIsInstance(A__		,     A__	)
           self.assertTrue(torch.equal(resulta['a'].cpu()		,     A__	)	)
           self.assertIsInstance(resulta['b']		,     A__	)
           self.assertTrue(torch.equal(resulta['b'][0].cpu()		,     A__	)	)
           self.assertTrue(torch.equal(resulta['b'][1].cpu()		,     A__	)	)
           self.assertEqual(resulta['c']		,     1	)

           lowerCamelCase__					     = send_to_device(ExampleNamedTuple(a=A__		,     b=[tensor, tensor]		,     c=1	)		,     A__	)
           self.assertIsInstance(A__		,     A__	)
           self.assertTrue(torch.equal(resulta.a.cpu()		,     A__	)	)
           self.assertIsInstance(resulta.b		,     A__	)
           self.assertTrue(torch.equal(resulta.b[0].cpu()		,     A__	)	)
           self.assertTrue(torch.equal(resulta.b[1].cpu()		,     A__	)	)
           self.assertEqual(resulta.c		,     1	)

           lowerCamelCase__					     = send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}	)		,     A__	)
           self.assertIsInstance(A__		,     A__	)
           self.assertTrue(torch.equal(resulta['a'].cpu()		,     A__	)	)
           self.assertIsInstance(resulta['b']		,     A__	)
           self.assertTrue(torch.equal(resulta['b'][0].cpu()		,     A__	)	)
           self.assertTrue(torch.equal(resulta['b'][1].cpu()		,     A__	)	)
           self.assertEqual(resulta['c']		,     1	)




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[int]:
           with self.assertRaises(A__	) as cm:
                 lowerCamelCase__					     = recursively_apply(torch.tensor		,     (torch.tensor(1	), 1)		,     error_on_other_type=A__	)
           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     :      List[Any]	) ->				List[str]:
           lowerCamelCase__					     = torch.tensor([1, 2, 3, 4, 5]	)
           self.assertEqual(listify(A__	)		,     [1, 2, 3, 4, 5]	)

           lowerCamelCase__					     = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]	)
           self.assertEqual(listify(A__	)		,     [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]	)

           lowerCamelCase__					     = torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]	)
           self.assertEqual(
               listify(A__	)		,     [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				Optional[Any]:
           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     :      Optional[Any]	) ->				int:
           lowerCamelCase__					     = RegressionModel()
           lowerCamelCase__					     = model.forward
           lowerCamelCase__					     = convert_outputs_to_fpaa(model.forward	)
           lowerCamelCase__					     = extract_model_from_parallel(A__		,     keep_fpaa_wrapper=A__	)
           lowerCamelCase__					     = pickle.dumps(A__	)




     @require_cuda
     def        __snake_case      (			self     :      Any	) ->				int:
           lowerCamelCase__					     = RegressionModel()
           lowerCamelCase__					     = model.forward
           lowerCamelCase__					     = torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
           lowerCamelCase__					     = convert_outputs_to_fpaa(model.forward	)
           lowerCamelCase__					     = extract_model_from_parallel(A__		,     keep_fpaa_wrapper=A__	)
           lowerCamelCase__					     = pickle.dumps(A__	)




     @require_cuda
     @require_torch_min_version(version='2.0'	)
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           lowerCamelCase__					     = RegressionModel()
           lowerCamelCase__					     = model.forward
           lowerCamelCase__					     = torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
           lowerCamelCase__					     = convert_outputs_to_fpaa(model.forward	)
           lowerCamelCase__					     = torch.compile(model.forward		,     backend='inductor'	)
           lowerCamelCase__					     = torch.randn(4		,     1_0	).cuda()
           lowerCamelCase__					     = model(A__	)




     def        __snake_case      (			self     :      int	) ->				Any:
           lowerCamelCase__					     = RegressionModel()
           # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
           lowerCamelCase__					     = torch.nn.parallel.DataParallel(A__	)
           lowerCamelCase__					     = extract_model_from_parallel(A__	)

           self.assertEqual(A__		,     A__	)




     @require_torch_min_version(version='2.0'	)
     def        __snake_case      (			self     :      Tuple	) ->				Tuple:
           lowerCamelCase__					     = RegressionModel()
           lowerCamelCase__					     = torch.compile(A__	)

           # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
           lowerCamelCase__					     = torch.nn.parallel.DataParallel(A__	)
           lowerCamelCase__					     = torch.compile(A__	)
           lowerCamelCase__					     = extract_model_from_parallel(A__	)

           self.assertEqual(compiled_model._orig_mod		,     compiled_model_unwrapped._orig_mod	)




     def        __snake_case      (			self     :      List[str]	) ->				str:
           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']	)	)






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)




