
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def        lowerCamelCase__    (    __lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			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__    (    __lowerCAmelCase  :  str    ):




			"""simple docstring"""


			lowerCAmelCase_     ,  lowerCAmelCase_      =     emb.weight.shape
			lowerCAmelCase_      =     nn.Linear(lowercase_   ,							lowercase_   ,							bias=lowercase_    )
			lowerCAmelCase_      =     emb.weight.data
			return lin_layer






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]   ,							__lowerCAmelCase  :  List[str]="uclanlp/plbart-base"   ,							__lowerCAmelCase  :  str=False   ,							__lowerCAmelCase  :  Dict=False    ):




			"""simple docstring"""


			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       =					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       =					parser.parse_args()
			_A       =					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)








import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_A       =					10_00_00_00_00_00
_A       =					1_00_00

_A,				_A       =					os.path.split(__file__)
_A       =					os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     pa.Table.from_pydict({"col": [0] * chunk_size}    )
			lowerCAmelCase_      =     pa.concat_tables([table] * (num_examples // chunk_size)    )
			return datasets.Dataset(__lowerCAmelCase   ,							fingerprint="table_100B"    )





@dataclass
class      _lowerCAmelCase :
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     np.random.default_rng(self.seed					)
							lowerCAmelCase_      =     rng.integers(low=self.low    ,    high=self.high    ,    size=self.size					).tolist()




				def __iter__( self					)    ->				List[str]:
							return iter(self._sampled_values					)




				def __len__( self					)    ->				Optional[int]:
							return self.size






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[0]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[-1]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[range(len(__lowerCAmelCase    ) // 2   ,							len(__lowerCAmelCase    ) // 2 + 1024    )]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[RandIter(0   ,							len(__lowerCAmelCase    )   ,							1024   ,							seed=42    )]






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     {"num examples": SPEED_TEST_N_EXAMPLES}
			lowerCAmelCase_      =     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
			print("generating dataset"    )
			lowerCAmelCase_      =     generate_100B_dataset(num_examples=__lowerCAmelCase   ,							chunk_size=__lowerCAmelCase    )
			print("Functions"    )
			for func in functions:
						print(func.__name__    )
						lowerCAmelCase_      =     func(__lowerCAmelCase    )

			with open(__lowerCAmelCase   ,							"wb"    ) as f:
						f.write(json.dumps(__lowerCAmelCase    ).encode("utf-8"    )    )


if __name__ == "__main__":  # useful to run the profiler
			benchmark_table_100B()



