





'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





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





def    _lowerCAmelCase						(						lowercase	)					->					int:
			__lowerCAmelCase						,       __lowerCAmelCase											=				emb.weight.shape
			__lowerCAmelCase											=				nn.Linear(lowercase					,	lowercase					,	bias=lowercase	)
			__lowerCAmelCase											=				emb.weight.data
			return lin_layer





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

			__lowerCAmelCase											=				PLBartConfig.from_pretrained(lowercase					,	vocab_size=lowercase	)

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

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

			return model


if __name__ == "__main__":
	_a					: Optional[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"""
	)
	_a					: int      =    parser.parse_args()
	_a					: 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'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class     _UpperCAmelCase							(						unittest.TestCase   ):
		@patch("""diffusers.utils.hub_utils.get_full_repo_name"""			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""full_repo_name"""
					with TemporaryDirectory() as tmpdir:
								# Dummy args values
								__lowerCAmelCase											=				Mock()
								__lowerCAmelCase											=				tmpdir
								__lowerCAmelCase											=				0
								__lowerCAmelCase											=				"""hub_token"""
								__lowerCAmelCase											=				"""dataset_name"""
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				10_00_00
								__lowerCAmelCase											=				1_00_00
								__lowerCAmelCase											=				0.01
								__lowerCAmelCase											=				0.02
								__lowerCAmelCase											=				0.03
								__lowerCAmelCase											=				0.0005
								__lowerCAmelCase											=				0.00_0001
								__lowerCAmelCase											=				1
								__lowerCAmelCase											=				10
								__lowerCAmelCase											=				0.001
								__lowerCAmelCase											=				0.1
								__lowerCAmelCase											=				0.2
								__lowerCAmelCase											=				True

								# Model card mush be rendered and saved
								diffusers.utils.hub_utils.create_model_card(__SCREAMING_SNAKE_CASE,model_name="""model_name"""			)
								self.assertTrue((Path(__SCREAMING_SNAKE_CASE			) / """README.md""").is_file()			)

