


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 	lowercase     (							A_			)-> int:
  '''simple docstring'''







  a		:						Optional[Any] 				=     [
      "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 	lowercase     (							A_			)-> Dict:
  '''simple docstring'''







  a				,						a		:						Any 				=     emb.weight.shape
  a		:						Tuple 				=     nn.Linear(A_    ,  A_    ,  bias=A_			)
  a		:						int 				=     emb.weight.data
  return lin_layer


def 	lowercase     (							A_    ,  A_="uclanlp/plbart-base"    ,  A_=False    ,  A_=False			)-> Dict:
  '''simple docstring'''







  a		:						Optional[int] 				=     torch.load(A_    ,  map_location="cpu"			)["model"]
  remove_ignore_keys_(A_			)
  a		:						Dict 				=     state_dict["encoder.embed_tokens.weight"].shape[0]

  a		:						List[Any] 				=     PLBartConfig.from_pretrained(A_    ,  vocab_size=A_			)

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

  else:
    a		:						List[str] 				=     {}
    for key, value in state_dict.copy().items():
      if key.startswith("classification_heads.sentence_classification_head"			):
        a		:						Optional[int] 				=     value
        state_dict.pop(A_			)
    a		:						Optional[int] 				=     PLBartForSequenceClassification(A_			)
    model.model.load_state_dict(A_			)
    model.classification_head.load_state_dict(A_			)

  return model


if __name__ == "__main__":
 __lowercase          = 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"""
 )
 __lowercase          = parser.parse_args()
 __lowercase          = 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)










"""simple docstring"""







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          = {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}






class 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

      a		:						List[Any] 				=     vocab_size
      a		:						List[Any] 				=     hidden_size
      a		:						Dict 				=     num_hidden_layers
      a		:						int 				=     num_attention_heads
      a		:						Dict 				=     hidden_act
      a		:						Tuple 				=     intermediate_size
      a		:						int 				=     hidden_dropout_prob
      a		:						List[Any] 				=     attention_probs_dropout_prob
      a		:						Union[str, Any] 				=     max_position_embeddings
      a		:						Union[str, Any] 				=     type_vocab_size
      a		:						Optional[Any] 				=     initializer_range
      a		:						Any 				=     layer_norm_eps
      a		:						Any 				=     embedding_size
      a		:						Tuple 				=     q_groups
      a		:						List[str] 				=     k_groups
      a		:						str 				=     v_groups
      a		:						Optional[Any] 				=     post_attention_groups
      a		:						List[str] 				=     intermediate_groups
      a		:						Optional[int] 				=     output_groups






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






