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										= {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						List[str]      =				"""ibert"""







    def __init__(     self			,			__lowercase=30_522			,			__lowercase=768			,			__lowercase=12			,			__lowercase=12			,			__lowercase=3_072			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=512			,			__lowercase=2			,			__lowercase=0.02			,			__lowercase=1E-1_2			,			__lowercase=1			,			__lowercase=0			,			__lowercase=2			,			__lowercase="absolute"			,			__lowercase=False			,			__lowercase="none"			,			**__lowercase			,			)	->     int:
        super().__init__(pad_token_id=__lowercase			,			bos_token_id=__lowercase			,			eos_token_id=__lowercase			,			**__lowercase)

        __UpperCamelCase					:Optional[int]        =		vocab_size
        __UpperCamelCase					:Tuple        =		hidden_size
        __UpperCamelCase					:str        =		num_hidden_layers
        __UpperCamelCase					:List[Any]        =		num_attention_heads
        __UpperCamelCase					:Optional[int]        =		hidden_act
        __UpperCamelCase					:Optional[Any]        =		intermediate_size
        __UpperCamelCase					:List[str]        =		hidden_dropout_prob
        __UpperCamelCase					:List[Any]        =		attention_probs_dropout_prob
        __UpperCamelCase					:Any        =		max_position_embeddings
        __UpperCamelCase					:Dict        =		type_vocab_size
        __UpperCamelCase					:List[Any]        =		initializer_range
        __UpperCamelCase					:Union[str, Any]        =		layer_norm_eps
        __UpperCamelCase					:Optional[Any]        =		position_embedding_type
        __UpperCamelCase					:Optional[int]        =		quant_mode
        __UpperCamelCase					:Tuple        =		force_dequant


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






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






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      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					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		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)





