


'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]				)      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		[
         "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		: Optional[Any]				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		emb.weight.shape
     _a						=		nn.Linear(__lowerCamelCase      ,  __lowerCamelCase      ,  bias=__lowerCamelCase				)
     _a						=		emb.weight.data
     return lin_layer






def __UpperCamelCase   (			__lowerCamelCase		: List[str]				)      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		torch.load(__lowerCamelCase      ,  map_location="cpu"				)
     _a						=		mam_aaa["args"] or mam_aaa["cfg"]["model"]
     _a						=		mam_aaa["model"]
     remove_ignore_keys_(__lowerCamelCase				)
     _a						=		state_dict["encoder.embed_tokens.weight"].shape[0]

     _a						=		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"      ,  )

     _a						=		state_dict["decoder.embed_tokens.weight"]
     _a						=		MaMaaaForConditionalGeneration(__lowerCamelCase				)
     model.model.load_state_dict(__lowerCamelCase      ,  strict=__lowerCamelCase				)
     _a						=		make_linear_from_emb(model.model.shared				)

     return model


if __name__ == "__main__":
   lowercase__				     =		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.")
   lowercase__				     =		parser.parse_args()
   lowercase__				     =		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
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''convbert'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=2     ,							__UpperCamelCase=768     ,							__UpperCamelCase=2     ,							__UpperCamelCase=9     ,							__UpperCamelCase=1     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(
              pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		embedding_size
          _a						=		head_ratio
          _a						=		conv_kernel_size
          _a						=		num_groups
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a						=		{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _a						=		{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]		)

