



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     str      ):
				"""simple docstring"""







				__a					       =						[
				    """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    :     Union[str, Any]      ):
				"""simple docstring"""







				__a       ,   __a					       =						emb.weight.shape
				__a					       =						nn.Linear(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       bias=_SCREAMING_SNAKE_CASE      )
				__a					       =						emb.weight.data
				return lin_layer

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Union[str, Any]="uclanlp/plbart-base"			,       _SCREAMING_SNAKE_CASE    :     List[Any]=False			,       _SCREAMING_SNAKE_CASE    :     Union[str, Any]=False      ):
				"""simple docstring"""







				__a					       =						torch.load(_SCREAMING_SNAKE_CASE			,       map_location="""cpu"""      )["""model"""]
				remove_ignore_keys_(_SCREAMING_SNAKE_CASE      )
				__a					       =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

				__a					       =						PLBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,       vocab_size=_SCREAMING_SNAKE_CASE      )

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

				else:
								__a					       =						{}
								for key, value in state_dict.copy().items():
												if key.startswith("""classification_heads.sentence_classification_head"""      ):
																__a					       =						value
																state_dict.pop(_SCREAMING_SNAKE_CASE      )
								__a					       =						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)





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     =	"""Update an existing config file with the latest defaults while maintaining the old configuration."""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				__a					       =						args.config_file
				if config_file is None and Path(_SCREAMING_SNAKE_CASE      ).exists():
								__a					       =						default_config_file
				elif not Path(_SCREAMING_SNAKE_CASE      ).exists():
								raise ValueError(f"The passed config file located at {config_file} doesn't exist."      )
				__a					       =						load_config_from_file(_SCREAMING_SNAKE_CASE      )

				if config_file.endswith(""".json"""      ):
								config.to_json_file(_SCREAMING_SNAKE_CASE      )
				else:
								config.to_yaml_file(_SCREAMING_SNAKE_CASE      )
				return config_file

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     int      ):
				"""simple docstring"""







				__a					       =						parser.add_parser("""update"""			,       parents=_SCREAMING_SNAKE_CASE			,       help=_SCREAMING_SNAKE_CASE			,       formatter_class=_SCREAMING_SNAKE_CASE      )
				parser.add_argument(
				    """--config_file"""			,       default=_SCREAMING_SNAKE_CASE			,       help=(
				        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
				        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
				        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
				        """with 'huggingface'."""
				    )			,       )

				parser.set_defaults(func=_SCREAMING_SNAKE_CASE      )
				return parser

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				__a					       =						update_config(_SCREAMING_SNAKE_CASE      )
				print(f"Sucessfully updated the configuration file at {config_file}."      )
