
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def 				a						(							*snake_case__:					Optional[Any]					):


				'''simple docstring'''
				if not isinstance(snake_case__						,   snake_case__					):
								lowercase_       =					list(snake_case__					)
				for i in range(len(snake_case__					)					):
								lowercase_       =					None
				gc.collect()
				if is_xpu_available():
								torch.xpu.empty_cache()
				elif is_npu_available():
								torch.npu.empty_cache()
				else:
								torch.cuda.empty_cache()
				return objects




def 				a						(							snake_case__:					Exception					):


				'''simple docstring'''
				lowercase_       =					[
				    '''CUDA out of memory.''',  # CUDA OOM
				    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
				    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
				]
				if isinstance(snake_case__						,   snake_case__					) and len(exception.args					) == 1:
								return any(err in exception.args[0] for err in _statements					)
				return False




def 				a						(							snake_case__:					callable = None						,   snake_case__:					int = 128					):


				'''simple docstring'''
				if function is None:
								return functools.partial(snake_case__						,   starting_batch_size=snake_case__					)

				lowercase_       =					starting_batch_size

				def decorator(*snake_case__:					List[str]						,   **snake_case__:					List[str]					):
								nonlocal batch_size
								gc.collect()
								if is_xpu_available():
												torch.xpu.empty_cache()
								elif is_npu_available():
												torch.npu.empty_cache()
								else:
												torch.cuda.empty_cache()
								lowercase_       =					list(inspect.signature(snake_case__					).parameters.keys()					)
								# Guard against user error
								if len(snake_case__					) < (len(snake_case__					) + 1):
												lowercase_       =					''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]						,   args[1:]					)]					)
												raise TypeError(
												    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
												    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''					)
								while True:
												if batch_size == 0:
																raise RuntimeError('''No executable batch size found, reached zero.'''					)
												try:
																return function(snake_case__						,   *snake_case__						,   **snake_case__					)
												except Exception as e:
																if should_reduce_batch_size(snake_case__					):
																				gc.collect()
																				if is_xpu_available():
																								torch.xpu.empty_cache()
																				elif is_npu_available():
																								torch.npu.empty_cache()
																				else:
																								torch.cuda.empty_cache()
																				batch_size //= 2
																else:
																				raise

				return decorator





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """encoder.version""",
     """decoder.version""",
     """model.encoder.version""",
     """model.decoder.version""",
     """decoder.output_projection.weight""",
     """_float_tensor""",
     """encoder.embed_positions._float_tensor""",
     """decoder.embed_positions._float_tensor""",
 ]
 for k in ignore_keys:
  state_dict.pop(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		max_position_embeddings=10_24						,		encoder_layers=args.encoder_layers						,		decoder_layers=args.decoder_layers						,		encoder_attention_heads=args.encoder_attention_heads						,		decoder_attention_heads=args.decoder_attention_heads						,		encoder_ffn_dim=args.encoder_ffn_embed_dim						,		decoder_ffn_dim=args.decoder_ffn_embed_dim						,		d_model=args.encoder_embed_dim						,		encoder_layerdrop=args.encoder_layerdrop						,		decoder_layerdrop=args.decoder_layerdrop						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function="""relu"""						,		)

 __lowerCAmelCase											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''fairseq_path''', type=str, help='''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.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






