





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 		__lowercase					(       snake_case_  :  int						)       ->List[Any]:



				'''simple docstring'''







				__A   :						int									=					[
				    '''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 		__lowercase					(       snake_case_  :  List[Any]						)       ->Tuple:



				'''simple docstring'''







				__A   :						Tuple									=					emb.weight.shape
				__A   :						str									=					nn.Linear(snake_case_    ,snake_case_    ,bias=snake_case_						)
				__A   :						str									=					emb.weight.data
				return lin_layer







def 		__lowercase					(       snake_case_  :  Dict						)       ->str:



				'''simple docstring'''







				__A   :						List[str]									=					torch.load(snake_case_    ,map_location='''cpu'''						)
				__A   :						int									=					mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				__A   :						int									=					mam_aaa['''model''']
				remove_ignore_keys_(snake_case_						)
				__A   :						str									=					state_dict['''encoder.embed_tokens.weight'''].shape[0]

				__A   :						Union[str, Any]									=					MaMaaaConfig(
				    vocab_size=snake_case_    ,max_position_embeddings=1024    ,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'''    ,)

				__A   :						List[str]									=					state_dict['''decoder.embed_tokens.weight''']
				__A   :						List[str]									=					MaMaaaForConditionalGeneration(snake_case_						)
				model.model.load_state_dict(snake_case_    ,strict=snake_case_						)
				__A   :						int									=					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="""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_			=      parser.parse_args()
							a_			=      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




