




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def       UpperCAmelCase       (     _snake_case							):
							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							):
							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							):
							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=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'''						, )

							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__":
		UpperCAmelCase_						=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.""")
		UpperCAmelCase_						=parser.parse_args()
		UpperCAmelCase_						=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						={
    """configuration_jukebox""": [
        """JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """JukeboxConfig""",
        """JukeboxPriorConfig""",
        """JukeboxVQVAEConfig""",
    ],
    """tokenization_jukebox""": ["""JukeboxTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """JukeboxModel""",
		    """JukeboxPreTrainedModel""",
		    """JukeboxVQVAE""",
		    """JukeboxPrior""",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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



