
"""simple docstring"""


import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      int          =    0
				a__:      bool          =    False
				a__:      float          =    3.0




class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()							,						{}   )
							self.assertDictEqual(MockClass(a=2   ).to_kwargs()							,						{'''a''': 2}   )
							self.assertDictEqual(MockClass(a=2							,						b=UpperCAmelCase   ).to_kwargs()							,						{'''a''': 2, '''b''': True}   )
							self.assertDictEqual(MockClass(a=2							,						c=2.2_5   ).to_kwargs()							,						{'''a''': 2, '''c''': 2.2_5}   )




				@require_cuda
				def        UpperCAmelCase__						(				self   ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							lowerCamelCase_					=  GradScalerKwargs(init_scale=1024							,						growth_factor=2   )
							AcceleratorState._reset_state()
							lowerCamelCase_					=  Accelerator(mixed_precision='''fp16'''							,						kwargs_handlers=[scaler_handler]   )
							print(accelerator.use_fpaa   )
							lowerCamelCase_					=  accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale							,						1_0_2_4.0   )
							self.assertEqual(scaler._growth_factor							,						2.0   )

							# Check the other values are at the default
							self.assertEqual(scaler._backoff_factor							,						0.5   )
							self.assertEqual(scaler._growth_interval							,						2000   )
							self.assertEqual(scaler._enabled							,						UpperCAmelCase   )





				@require_multi_gpu
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
							execute_subprocess_async(UpperCAmelCase							,						env=os.environ.copy()   )


if __name__ == "__main__":
						A_    							=					DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A_    							=					Accelerator(kwargs_handlers=[ddp_scaler])
						A_    							=					torch.nn.Linear(100, 200)
						A_    							=					accelerator.prepare(model)

						# Check the values changed in kwargs
						A_    							=					""""""
						A_    							=					model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)





"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_		, lowerCamelCase_					=  emb.weight.shape
			lowerCamelCase_					=  nn.Linear(lowerCAmelCase__	,lowerCAmelCase__	,bias=lowerCAmelCase__						)
			lowerCamelCase_					=  emb.weight.data
			return lin_layer






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="facebook/mbart-large-en-ro"	,lowerCAmelCase__=False	,lowerCAmelCase__=False						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']
			remove_ignore_keys_(lowerCAmelCase__						)
			lowerCamelCase_					=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

			lowerCamelCase_					=  MBartConfig.from_pretrained(lowerCAmelCase__	,vocab_size=lowerCAmelCase__						)
			if mbart_aa and finetuned:
						lowerCamelCase_					=  '''relu'''

			lowerCamelCase_					=  state_dict['''decoder.embed_tokens.weight''']
			lowerCamelCase_					=  MBartForConditionalGeneration(lowerCAmelCase__						)
			model.model.load_state_dict(lowerCAmelCase__						)

			if finetuned:
						lowerCamelCase_					=  make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
						)
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument(
						    """--hf_config""",
						    default="""facebook/mbart-large-cc25""",
						    type=str,
						    help="""Which huggingface architecture to use: mbart-large""",
						)
						parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
						parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
						A_    							=					parser.parse_args()
						A_    							=					convert_fairseq_mbart_checkpoint_from_disk(
						    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
						)
						model.save_pretrained(args.pytorch_dump_folder_path)



