
'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__      :		Union[str, Any]       							=  logging.get_logger(__name__)

UpperCamelCase__      :		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    _a				(a__):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =		"""xlm-roberta-xl"""






       def __init__(							self       ,							A__=25_08_80       ,							A__=25_60       ,							A__=36       ,							A__=32       ,							A__=1_02_40       ,							A__="gelu"       ,							A__=0.1       ,							A__=0.1       ,							A__=5_14       ,							A__=1       ,							A__=0.02       ,							A__=1E-05       ,							A__=1       ,							A__=0       ,							A__=2       ,							A__="absolute"       ,							A__=True       ,							A__=None       ,							**A__       ,							)    ->			List[str]:
          super().__init__(pad_token_id=lowercase__       ,							bos_token_id=lowercase__       ,							eos_token_id=lowercase__       ,							**lowercase__      )
          _SCREAMING_SNAKE_CASE      =				vocab_size
          _SCREAMING_SNAKE_CASE      =				hidden_size
          _SCREAMING_SNAKE_CASE      =				num_hidden_layers
          _SCREAMING_SNAKE_CASE      =				num_attention_heads
          _SCREAMING_SNAKE_CASE      =				hidden_act
          _SCREAMING_SNAKE_CASE      =				intermediate_size
          _SCREAMING_SNAKE_CASE      =				hidden_dropout_prob
          _SCREAMING_SNAKE_CASE      =				attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE      =				max_position_embeddings
          _SCREAMING_SNAKE_CASE      =				type_vocab_size
          _SCREAMING_SNAKE_CASE      =				initializer_range
          _SCREAMING_SNAKE_CASE      =				layer_norm_eps
          _SCREAMING_SNAKE_CASE      =				position_embedding_type
          _SCREAMING_SNAKE_CASE      =				use_cache
          _SCREAMING_SNAKE_CASE      =				classifier_dropout






class    _a				(a__):





       """simple docstring"""




       @property
       def        UpperCamelCase				(							self      )    ->			Mapping[str, Mapping[int, str]]:





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








'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




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

   _SCREAMING_SNAKE_CASE      =				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"""    ,      )

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

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  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__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






