




"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 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(lowercase_      ,			lowercase_						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Tuple:
 A__,				A__	=    emb.weight.shape
 A__	=    nn.Linear(lowercase_      ,			lowercase_      ,			bias=lowercase_						)
 A__	=    emb.weight.data
 return lin_layer







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_="facebook/mbart-large-en-ro"      ,			lowercase_=False      ,			lowercase_=False						)						->		List[Any]:
 A__	=    torch.load(lowercase_      ,			map_location="cpu"						)["model"]
 remove_ignore_keys_(lowercase_						)
 A__	=    state_dict["encoder.embed_tokens.weight"].shape[0]

 A__	=    MBartConfig.from_pretrained(lowercase_      ,			vocab_size=lowercase_						)
 if mbart_aa and finetuned:
  A__	=    "relu"

 A__	=    state_dict["decoder.embed_tokens.weight"]
 A__	=    MBartForConditionalGeneration(lowercase_						)
 model.model.load_state_dict(lowercase_						)

 if finetuned:
  A__	=    make_linear_from_emb(model.model.shared						)

 return model


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a 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="facebook/mbart-large-cc25",
          type=str,
          help="Which huggingface architecture to use: mbart-large",
      )
      parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
      parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      SCREAMING_SNAKE_CASE    =	convert_fairseq_mbart_checkpoint_from_disk(
          args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
      )
      model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""



# 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.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def   _SCREAMING_SNAKE_CASE       (							lowercase_=None						)						->		Any:
 if subparsers is not None:
  A__	=    subparsers.add_parser("env"						)
 else:
  A__	=    argparse.ArgumentParser("Accelerate env command"						)

 parser.add_argument(
     "--config_file"      ,			default=lowercase_      ,			help="The config file to use for the default values in the launching script."						)

 if subparsers is not None:
  parser.set_defaults(func=lowercase_						)
 return parser







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    torch.__version__
 A__	=    torch.cuda.is_available()
 A__	=    is_xpu_available()
 A__	=    is_npu_available()

 A__	=    "Not found"
 # Get the default from the config file.
 if args.config_file is not None or os.path.isfile(lowercase_						):
  A__	=    load_config_from_file(args.config_file						).to_dict()

 A__	=    {
     "`Accelerate` version": version,
     "Platform": platform.platform(),
     "Python version": platform.python_version(),
     "Numpy version": np.__version__,
     "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
     "PyTorch XPU available": str(lowercase_						),
     "PyTorch NPU available": str(lowercase_						),
     "System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
 }
 if pt_cuda_available:
  A__	=    torch.cuda.get_device_name()

 print("\nCopy-and-paste the text below in your GitHub issue\n"						)
 print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()]						)						)

 print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"						)
 A__	=    (
     "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]						)
     if isinstance(lowercase_      ,			lowercase_						)
     else f"""\t{accelerate_config}"""
 )
 print(lowercase_						)

 A__	=    accelerate_config

 return info







def   _SCREAMING_SNAKE_CASE       (							)						->		int:
 A__	=    env_command_parser()
 A__	=    parser.parse_args()
 env_command(lowercase_						)
 return 0


if __name__ == "__main__":
      raise SystemExit(main())





