





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Optional[int]:






   """simple docstring"""







   _UpperCAmelCase           =   [
       '''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(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase       ,      _UpperCAmelCase           =   emb.weight.shape
   _UpperCAmelCase           =   nn.Linear(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	bias=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   emb.weight.data
   return lin_layer
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]						)  ->  Any:






   """simple docstring"""







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

   _UpperCAmelCase           =   MaMaaaConfig(
       vocab_size=SCREAMING_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'''				,	)

   _UpperCAmelCase           =   state_dict['''decoder.embed_tokens.weight''']
   _UpperCAmelCase           =   MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   model.model.load_state_dict(SCREAMING_SNAKE_CASE_				,	strict=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   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)
'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowercase        =    {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''albert'''



			def __init__(  self			,       __lowerCAmelCase=30000			,       __lowerCAmelCase=128			,       __lowerCAmelCase=4096			,       __lowerCAmelCase=12			,       __lowerCAmelCase=1			,       __lowerCAmelCase=64			,       __lowerCAmelCase=16384			,       __lowerCAmelCase=1			,       __lowerCAmelCase="gelu_new"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=0			,       __lowerCAmelCase=512			,       __lowerCAmelCase=2			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=1E-1_2			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase="absolute"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase			,       bos_token_id=__lowerCAmelCase			,       eos_token_id=__lowerCAmelCase			,       **__lowerCAmelCase)

					lowerCAmelCase     =    vocab_size
					lowerCAmelCase     =    embedding_size
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_hidden_groups
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    inner_group_num
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    max_position_embeddings
					lowerCAmelCase     =    type_vocab_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    position_embedding_type







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase     =    {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase     =    {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					    ])




