
'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: List[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 		A_							(						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




      _lowerCamelCase    ,		_lowerCamelCase					: Tuple		    = emb.weight.shape
      _lowerCamelCase					: List[str]		    = nn.Linear(_lowerCAmelCase					,						_lowerCAmelCase					,						bias=_lowerCAmelCase				)
      _lowerCamelCase					: int		    = emb.weight.data
      return lin_layer







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = torch.load(_lowerCAmelCase					,						map_location="cpu"				)
      _lowerCamelCase					: List[Any]		    = mam_aaa["args"] or mam_aaa["cfg"]["model"]
      _lowerCamelCase					: Optional[Any]		    = mam_aaa["model"]
      remove_ignore_keys_(_lowerCAmelCase				)
      _lowerCamelCase					: int		    = state_dict["encoder.embed_tokens.weight"].shape[0]

      _lowerCamelCase					: str		    = 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"					,						)

      _lowerCamelCase					: Optional[int]		    = state_dict["decoder.embed_tokens.weight"]
      _lowerCamelCase					: List[Any]		    = MaMaaaForConditionalGeneration(_lowerCAmelCase				)
      model.model.load_state_dict(_lowerCAmelCase					,						strict=_lowerCAmelCase				)
      _lowerCamelCase					: Optional[int]		    = make_linear_from_emb(model.model.shared				)

      return model


if __name__ == "__main__":
 UpperCAmelCase_							: 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.')
 UpperCAmelCase_							: Optional[Any] =							parser.parse_args()
 UpperCAmelCase_							: List[str] =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__     :		List[Any]        = logging.get_logger(__name__)

lowerCamelCase__     :		Union[str, Any]        = {
    """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    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Union[str, Any]	   =      'convbert'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=3_05_22       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=30_72       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=1E-12       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=9       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




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

   lowercase__	: Dict     		=   vocab_size
   lowercase__	: List[Any]     		=   hidden_size
   lowercase__	: Optional[Any]     		=   num_hidden_layers
   lowercase__	: Union[str, Any]     		=   num_attention_heads
   lowercase__	: List[str]     		=   intermediate_size
   lowercase__	: Optional[int]     		=   hidden_act
   lowercase__	: Tuple     		=   hidden_dropout_prob
   lowercase__	: List[str]     		=   attention_probs_dropout_prob
   lowercase__	: Tuple     		=   max_position_embeddings
   lowercase__	: Dict     		=   type_vocab_size
   lowercase__	: Union[str, Any]     		=   initializer_range
   lowercase__	: Dict     		=   layer_norm_eps
   lowercase__	: Tuple     		=   embedding_size
   lowercase__	: List[str]     		=   head_ratio
   lowercase__	: Dict     		=   conv_kernel_size
   lowercase__	: Dict     		=   num_groups
   lowercase__	: int     		=   classifier_dropout



class    _snake_case (						UpperCAmelCase_				):


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




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






