





"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Tuple      =       [
						    '''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 			a_							(				_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       emb.weight.shape
						_UpperCamelCase     :   Any      =       nn.Linear(_lowerCAmelCase			,		_lowerCAmelCase			,		bias=_lowerCAmelCase  )
						_UpperCamelCase     :   str      =       emb.weight.data
						return lin_layer





def 			a_							(				_lowercase			,		_lowercase="uclanlp/plbart-base"			,		_lowercase=False			,		_lowercase=False  ):
						_UpperCamelCase     :   str      =       torch.load(_lowerCAmelCase			,		map_location='''cpu'''  )['''model''']
						remove_ignore_keys_(_lowerCAmelCase  )
						_UpperCamelCase     :   List[Any]      =       state_dict['''encoder.embed_tokens.weight'''].shape[0]

						_UpperCamelCase     :   List[Any]      =       PLBartConfig.from_pretrained(_lowerCAmelCase			,		vocab_size=_lowerCAmelCase  )

						_UpperCamelCase     :   str      =       state_dict['''decoder.embed_tokens.weight''']
						if not classification:
												_UpperCamelCase     :   int      =       PLBartForConditionalGeneration(_lowerCAmelCase  )
												model.model.load_state_dict(_lowerCAmelCase  )
												if finetuned:
																		_UpperCamelCase     :   Optional[Any]      =       make_linear_from_emb(model.model.shared  )

						else:
												_UpperCamelCase     :   Optional[Any]      =       {}
												for key, value in state_dict.copy().items():
																		if key.startswith('''classification_heads.sentence_classification_head'''  ):
																								_UpperCamelCase     :   List[str]      =       value
																								state_dict.pop(_lowerCAmelCase  )
												_UpperCamelCase     :   Tuple      =       PLBartForSequenceClassification(_lowerCAmelCase  )
												model.model.load_state_dict(_lowerCAmelCase  )
												model.classification_head.load_state_dict(_lowerCAmelCase  )

						return model


if __name__ == "__main__":
						UpperCamelCase_	 =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_	 =parser.parse_args()
						UpperCamelCase_	 =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__)
