



from collections import OrderedDict
from typing import Mapping

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


a_   :List[str]        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 					snake_case__						(       __UpperCamelCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""xlm-roberta-xl"""






     def __init__(       self  :  List[str], _snake_case  :  Dict=2_5_0_8_8_0, _snake_case  :  int=2_5_6_0, _snake_case  :  Tuple=3_6, _snake_case  :  Dict=3_2, _snake_case  :  Dict=1_0_2_4_0, _snake_case  :  List[Any]="gelu", _snake_case  :  Optional[Any]=0.1, _snake_case  :  Optional[int]=0.1, _snake_case  :  int=5_1_4, _snake_case  :  Any=1, _snake_case  :  Dict=0.0_2, _snake_case  :  Union[str, Any]=1e-05, _snake_case  :  List[Any]=1, _snake_case  :  Any=0, _snake_case  :  List[str]=2, _snake_case  :  List[Any]="absolute", _snake_case  :  Tuple=True, _snake_case  :  Optional[Any]=None, **_snake_case  :  Any, )   ->List[str]:
            super().__init__(pad_token_id=_snake_case, bos_token_id=_snake_case, eos_token_id=_snake_case, **_snake_case					)
            snake_case__							:	Dict   	=	vocab_size
            snake_case__							:	str   	=	hidden_size
            snake_case__							:	str   	=	num_hidden_layers
            snake_case__							:	List[Any]   	=	num_attention_heads
            snake_case__							:	List[str]   	=	hidden_act
            snake_case__							:	Dict   	=	intermediate_size
            snake_case__							:	Any   	=	hidden_dropout_prob
            snake_case__							:	List[str]   	=	attention_probs_dropout_prob
            snake_case__							:	Tuple   	=	max_position_embeddings
            snake_case__							:	Any   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	List[str]   	=	layer_norm_eps
            snake_case__							:	Any   	=	position_embedding_type
            snake_case__							:	int   	=	use_cache
            snake_case__							:	int   	=	classifier_dropout




class 					snake_case__						(       __UpperCamelCase     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	Optional[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	Dict   	=	{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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      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(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	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[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


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





