






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    [
				    '''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(lowerCamelCase_	,     lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__				,		lowercase__     				=    emb.weight.shape
				lowercase__     				=    nn.Linear(lowerCamelCase_	,     lowerCamelCase_	,     bias=lowerCamelCase_       )
				lowercase__     				=    emb.weight.data
				return lin_layer






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    torch.load(lowerCamelCase_	,     map_location='''cpu'''       )
				lowercase__     				=    mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				lowercase__     				=    mam_aaa['''model''']
				remove_ignore_keys_(lowerCamelCase_       )
				lowercase__     				=    state_dict['''encoder.embed_tokens.weight'''].shape[0]

				lowercase__     				=    MaMaaaConfig(
				    vocab_size=lowerCamelCase_	,     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'''	,     )

				lowercase__     				=    state_dict['''decoder.embed_tokens.weight''']
				lowercase__     				=    MaMaaaForConditionalGeneration(lowerCamelCase_       )
				model.model.load_state_dict(lowerCamelCase_	,     strict=lowerCamelCase_       )
				lowercase__     				=    make_linear_from_emb(model.model.shared       )

				return model


if __name__ == "__main__":
							A__			:    List[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__			:    Optional[int]         =				parser.parse_args()
							A__			:    int         =				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_flax_available,
    is_tf_available,
    is_torch_available,
)


A__			:    Any         =				{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Union[str, Any]         =				[
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A__			:    List[Any]         =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




