



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= [
					    "encoder.version",
					    "decoder.version",
					    "model.encoder.version",
					    "model.decoder.version",
					    "_float_tensor",
					    "decoder.output_projection.weight",
					]
					for k in ignore_keys:
										state_dict.pop(_lowerCAmelCase ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_	,     UpperCamelCase_			= emb.weight.shape
					UpperCamelCase_			= nn.Linear(_lowerCAmelCase ,						_lowerCAmelCase ,						bias=_lowerCAmelCase)
					UpperCamelCase_			= emb.weight.data
					return lin_layer



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase="uclanlp/plbart-base" ,						_lowerCAmelCase=False ,						_lowerCAmelCase=False):
					UpperCamelCase_			= torch.load(_lowerCAmelCase ,						map_location="cpu")["model"]
					remove_ignore_keys_(_lowerCAmelCase)
					UpperCamelCase_			= state_dict["encoder.embed_tokens.weight"].shape[0]

					UpperCamelCase_			= PLBartConfig.from_pretrained(_lowerCAmelCase ,						vocab_size=_lowerCAmelCase)

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

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

					return model


if __name__ == "__main__":
	UpperCAmelCase :				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"""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	UpperCAmelCase :				Optional[int]							=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)





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
