






"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any]							)->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       [
         "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   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple							)->		int:






     '''simple docstring'''



     UpperCAmelCase__       ,							UpperCAmelCase__	:						Optional[Any]       =       emb.weight.shape
     UpperCAmelCase__	:						Optional[int]       =       nn.Linear(snake_case ,				snake_case ,				bias=snake_case							)
     UpperCAmelCase__	:						Tuple       =       emb.weight.data
     return lin_layer


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int] ,				snake_case					:   Dict="uclanlp/plbart-base" ,				snake_case					:   Any=False ,				snake_case					:   Any=False							)->		Union[str, Any]:






     '''simple docstring'''



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

     UpperCAmelCase__	:						Any       =       PLBartConfig.from_pretrained(snake_case ,				vocab_size=snake_case							)

     UpperCAmelCase__	:						int       =       state_dict["decoder.embed_tokens.weight"]
     if not classification:
          UpperCAmelCase__	:						str       =       PLBartForConditionalGeneration(snake_case							)
          model.model.load_state_dict(snake_case							)
          if finetuned:
               UpperCAmelCase__	:						str       =       make_linear_from_emb(model.model.shared							)

     else:
          UpperCAmelCase__	:						Any       =       {}
          for key, value in state_dict.copy().items():
               if key.startswith("classification_heads.sentence_classification_head"							):
                    UpperCAmelCase__	:						List[str]       =       value
                    state_dict.pop(snake_case							)
          UpperCAmelCase__	:						Dict       =       PLBartForSequenceClassification(snake_case							)
          model.model.load_state_dict(snake_case							)
          model.classification_head.load_state_dict(snake_case							)

     return model


if __name__ == "__main__":
 _lowerCAmelCase  :  Any   =			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"""
 )
 _lowerCAmelCase  :  Dict   =			parser.parse_args()
 _lowerCAmelCase  :  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)










"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase  :  Optional[Any]   =			{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			[
     """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ConvNextV2ForImageClassification""",
     """ConvNextV2Model""",
     """ConvNextV2PreTrainedModel""",
     """ConvNextV2Backbone""",
 ]


if TYPE_CHECKING:
 from .configuration_convnextva import (
     CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConvNextVaConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convnextva import (
      CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvNextVaBackbone,
      ConvNextVaForImageClassification,
      ConvNextVaModel,
      ConvNextVaPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase  :  List[str]   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


