
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	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(_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	int									=       emb.weight.shape
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       nn.Linear(_snake_case       ,_snake_case       ,bias=_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       emb.weight.data
							return lin_layer

def lowercase_  (       _snake_case       ,_snake_case="uclanlp/plbart-base"       ,_snake_case=False       ,_snake_case=False						):
							SCREAMING_SNAKE_CASE__						:	str									=       torch.load(_snake_case       ,map_location="""cpu"""						)["""model"""]
							remove_ignore_keys_(_snake_case						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       state_dict["""encoder.embed_tokens.weight"""].shape[0]

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       PLBartConfig.from_pretrained(_snake_case       ,vocab_size=_snake_case						)

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

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

							return model


if __name__ == "__main__":
					UpperCAmelCase__				:   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'
					)
					UpperCAmelCase__				:   int   							=			parser.parse_args()
					UpperCAmelCase__				:   Tuple   							=			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


UpperCAmelCase__				:   Optional[Any]   							=			{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Tuple   							=			[
					    'UMT5EncoderModel',
					    'UMT5ForConditionalGeneration',
					    'UMT5ForQuestionAnswering',
					    'UMT5Model',
					    'UMT5PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_umta import UMTaConfig, UMTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_umta import (
										    UMTaEncoderModel,
										    UMTaForConditionalGeneration,
										    UMTaForQuestionAnswering,
										    UMTaModel,
										    UMTaPreTrainedModel,
										)
else:
					import sys

					UpperCAmelCase__				:   Optional[Any]   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




