



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)








'''simple docstring'''







from typing import TYPE_CHECKING

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


_A      :     Tuple       	={
    '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Dict       	=['''ImageGPTFeatureExtractor''']
			_A      :     Union[str, Any]       	=['''ImageGPTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=[
			    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ImageGPTForCausalImageModeling''',
			    '''ImageGPTForImageClassification''',
			    '''ImageGPTModel''',
			    '''ImageGPTPreTrainedModel''',
			    '''load_tf_weights_in_imagegpt''',
			]


if TYPE_CHECKING:
			from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
						from .image_processing_imagegpt import ImageGPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_imagegpt import (
						    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ImageGPTForCausalImageModeling,
						    ImageGPTForImageClassification,
						    ImageGPTModel,
						    ImageGPTPreTrainedModel,
						    load_tf_weights_in_imagegpt,
						)

else:
			import sys

			_A      :     str       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

