
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			lowerCAmelCase__ : Optional[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(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			lowerCAmelCase__ ,							lowerCAmelCase__ : Union[str, Any]   =						emb.weight.shape
			lowerCAmelCase__ : int   =						nn.Linear(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				bias=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Dict   =						emb.weight.data
			return lin_layer
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_="uclanlp/plbart-base"			,				SCREAMING_SNAKE_CASE_=False			,				SCREAMING_SNAKE_CASE_=False	)							->						List[str]:
			lowerCAmelCase__ : int   =						torch.load(SCREAMING_SNAKE_CASE_			,				map_location='cpu'	)['model']
			remove_ignore_keys_(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Dict   =						state_dict['encoder.embed_tokens.weight'].shape[0]

			lowerCAmelCase__ : Union[str, Any]   =						PLBartConfig.from_pretrained(SCREAMING_SNAKE_CASE_			,				vocab_size=SCREAMING_SNAKE_CASE_	)

			lowerCAmelCase__ : List[str]   =						state_dict['decoder.embed_tokens.weight']
			if not classification:
						lowerCAmelCase__ : List[Any]   =						PLBartForConditionalGeneration(SCREAMING_SNAKE_CASE_	)
						model.model.load_state_dict(SCREAMING_SNAKE_CASE_	)
						if finetuned:
									lowerCAmelCase__ : Any   =						make_linear_from_emb(model.model.shared	)

			else:
						lowerCAmelCase__ : Optional[int]   =						{}
						for key, value in state_dict.copy().items():
									if key.startswith('classification_heads.sentence_classification_head'	):
												lowerCAmelCase__ : Optional[int]   =						value
												state_dict.pop(SCREAMING_SNAKE_CASE_	)
						lowerCAmelCase__ : Any   =						PLBartForSequenceClassification(SCREAMING_SNAKE_CASE_	)
						model.model.load_state_dict(SCREAMING_SNAKE_CASE_	)
						model.classification_head.load_state_dict(SCREAMING_SNAKE_CASE_	)

			return model


if __name__ == "__main__":
					lowerCamelCase__        =						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"""
					)
					lowerCamelCase__        =						parser.parse_args()
					lowerCamelCase__        =						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 sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase__        =						load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)