
from collections import OrderedDict
from typing import Mapping

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


__magic_name__  :  Dict     			=   logging.get_logger(__name__)

__magic_name__  :  int     			=   {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  lowerCAmelCase_								=		"""ernie"""

  def __init__(					self						,    __UpperCamelCase=30522						,    __UpperCamelCase=768						,    __UpperCamelCase=12						,    __UpperCamelCase=12						,    __UpperCamelCase=3072						,    __UpperCamelCase="gelu"						,    __UpperCamelCase=0.1						,    __UpperCamelCase=0.1						,    __UpperCamelCase=512						,    __UpperCamelCase=2						,    __UpperCamelCase=3						,    __UpperCamelCase=False						,    __UpperCamelCase=0.02						,    __UpperCamelCase=1E-12						,    __UpperCamelCase=0						,    __UpperCamelCase="absolute"						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    **__UpperCamelCase						,    ):
      super().__init__(pad_token_id=__UpperCamelCase						,    **__UpperCamelCase					)

      A_								=					vocab_size
      A_								=					hidden_size
      A_								=					num_hidden_layers
      A_								=					num_attention_heads
      A_								=					hidden_act
      A_								=					intermediate_size
      A_								=					hidden_dropout_prob
      A_								=					attention_probs_dropout_prob
      A_								=					max_position_embeddings
      A_								=					type_vocab_size
      A_								=					task_type_vocab_size
      A_								=					use_task_id
      A_								=					initializer_range
      A_								=					layer_norm_eps
      A_								=					position_embedding_type
      A_								=					use_cache
      A_								=					classifier_dropout



class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  @property
  def 					lowercase_						(					self					):
      if self.task == "multiple-choice":
          A_								=					{0: "batch", 1: "choice", 2: "sequence"}
      else:
          A_								=					{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
              ("task_type_ids", dynamic_axis),
          ]					)








import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='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='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






