


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Any:
    _lowercase       :							Any      		=		[
        '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 				UpperCamelCase_(      lowerCamelCase_ )    ->						str:
    _lowercase		,				_lowercase       :							List[str]      		=		emb.weight.shape
    _lowercase       :							int      		=		nn.Linear(lowerCamelCase_							, lowerCamelCase_							, bias=lowerCamelCase_ )
    _lowercase       :							List[Any]      		=		emb.weight.data
    return lin_layer



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Dict:
    _lowercase       :							Optional[Any]      		=		torch.load(lowerCamelCase_							, map_location='cpu' )
    _lowercase       :							Union[str, Any]      		=		mam_aaa['args'] or mam_aaa['cfg']['model']
    _lowercase       :							Any      		=		mam_aaa['model']
    remove_ignore_keys_(lowerCamelCase_ )
    _lowercase       :							List[Any]      		=		state_dict['encoder.embed_tokens.weight'].shape[0]

    _lowercase       :							List[Any]      		=		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       :							Optional[Any]      		=		state_dict['decoder.embed_tokens.weight']
    _lowercase       :							Optional[int]      		=		MaMaaaForConditionalGeneration(lowerCamelCase_ )
    model.model.load_state_dict(lowerCamelCase_							, strict=lowerCamelCase_ )
    _lowercase       :							List[str]      		=		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      str												= 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.")
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= parser.parse_args()
 SCREAMING_SNAKE_CASE       :      Tuple												= 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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Tuple												= ["LlamaTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= ["LlamaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


