




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


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

SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      """deberta-v2"""







    def __init__(    self     ,  UpperCamelCase__=12_8100     ,  UpperCamelCase__=1536     ,  UpperCamelCase__=24     ,  UpperCamelCase__=24     ,  UpperCamelCase__=6144     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=512     ,  UpperCamelCase__=0     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-7     ,  UpperCamelCase__=False     ,  UpperCamelCase__=-1     ,  UpperCamelCase__=0     ,  UpperCamelCase__=True     ,  UpperCamelCase__=None     ,  UpperCamelCase__=0     ,  UpperCamelCase__="gelu"     ,  **UpperCamelCase__     ,  )     ->      Optional[int]:
           super().__init__(**UpperCamelCase__     )

           lowerCamelCase   :      List[str]			 =					hidden_size
           lowerCamelCase   :      Optional[int]			 =					num_hidden_layers
           lowerCamelCase   :      Optional[Any]			 =					num_attention_heads
           lowerCamelCase   :      Union[str, Any]			 =					intermediate_size
           lowerCamelCase   :      Any			 =					hidden_act
           lowerCamelCase   :      Dict			 =					hidden_dropout_prob
           lowerCamelCase   :      Dict			 =					attention_probs_dropout_prob
           lowerCamelCase   :      Tuple			 =					max_position_embeddings
           lowerCamelCase   :      Optional[Any]			 =					type_vocab_size
           lowerCamelCase   :      List[str]			 =					initializer_range
           lowerCamelCase   :      Tuple			 =					relative_attention
           lowerCamelCase   :      List[str]			 =					max_relative_positions
           lowerCamelCase   :      Optional[int]			 =					pad_token_id
           lowerCamelCase   :      int			 =					position_biased_input

           # Backwards compatibility
           if type(UpperCamelCase__     ) == str:
                  lowerCamelCase   :      Any			 =					[x.strip() for x in pos_att_type.lower().split("|"     )]

           lowerCamelCase   :      List[Any]			 =					pos_att_type
           lowerCamelCase   :      Dict			 =					vocab_size
           lowerCamelCase   :      Dict			 =					layer_norm_eps

           lowerCamelCase   :      Any			 =					kwargs.get("pooler_hidden_size"     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					pooler_dropout
           lowerCamelCase   :      int			 =					pooler_hidden_act




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



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







    @property
    def     _lowercase     (    self     )     ->      int:
           return 12







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = -1     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = 3     ,  UpperCamelCase__ = 40     ,  UpperCamelCase__ = 40     ,  UpperCamelCase__ = None     ,  )     ->      Mapping[str, Any]:
           lowerCamelCase   :      Union[str, Any]			 =					super().generate_dummy_inputs(preprocessor=UpperCamelCase__     ,  framework=UpperCamelCase__     )
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                  del dummy_inputs["token_type_ids"]
           return dummy_inputs










'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




