



'''simple docstring'''





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_lowercase						  =		['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
_lowercase						  =		{'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
   raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		' Hello world! cécé herlolip'

_lowercase						  =		[
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Union[str, Any]	)->  Dict:
    A__		 =					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
    ]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase ,       _lowerCamelCase	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       Any ,       UpperCamelCase_				:       Union[str, Any]	)->  Dict:
    A__		 =					dct.pop(_lowerCamelCase	)
    A__		 =					val



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any]	)->  Any:
    A__		 =					torch.load(_lowerCamelCase ,       map_location='''cpu'''	)
    A__		 =					torch.hub.load('''pytorch/fairseq''' ,       '''bart.large.cnn'''	).eval()
    hub_interface.model.load_state_dict(sd['''model''']	)
    return hub_interface



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int]	)->  Optional[int]:
    A__		 =					emb.weight.shape
    A__		 =					nn.Linear(_lowerCamelCase ,       _lowerCamelCase ,       bias=_lowerCamelCase	)
    A__		 =					emb.weight.data
    return lin_layer



@torch.no_grad()
def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       Optional[int]=None	)->  Dict:





    if not os.path.exists(_lowerCamelCase	):
        A__		 =					torch.hub.load('''pytorch/fairseq''' ,       _lowerCamelCase	).eval()
    else:
        A__		 =					load_xsum_checkpoint(_lowerCamelCase	)

    bart.model.upgrade_state_dict(bart.model.state_dict()	)
    if hf_checkpoint_name is None:
        A__		 =					checkpoint_path.replace('''.''' ,       '''-'''	)
    A__		 =					BartConfig.from_pretrained(_lowerCamelCase	)
    A__		 =					bart.encode(_lowerCamelCase	).unsqueeze(0	)
    A__		 =					BartTokenizer.from_pretrained(_lowerCamelCase	).encode(_lowerCamelCase ,       return_tensors='''pt'''	).unsqueeze(0	)
    if not torch.eq(_lowerCamelCase ,       _lowerCamelCase	).all():
        raise ValueError(
            f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"	)

    if checkpoint_path == "bart.large.mnli":
        A__		 =					bart.state_dict()
        remove_ignore_keys_(_lowerCamelCase	)
        A__		 =					state_dict["model.decoder.embed_tokens.weight"]
        for src, dest in mnli_rename_keys:
            rename_key(_lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase	)
        A__		 =					BartForSequenceClassification(_lowerCamelCase	).eval()
        model.load_state_dict(_lowerCamelCase	)
        A__		 =					bart.predict('''mnli''' ,       _lowerCamelCase ,       return_logits=_lowerCamelCase	)
        A__		 =					model(_lowerCamelCase	)[0]  # logits
    else:  # no classification heads to worry about
        A__		 =					bart.model.state_dict()
        remove_ignore_keys_(_lowerCamelCase	)
        A__		 =					state_dict["decoder.embed_tokens.weight"]
        A__		 =					bart.extract_features(_lowerCamelCase	)
        if hf_checkpoint_name == "facebook/bart-large":
            A__		 =					BartModel(_lowerCamelCase	).eval()
            model.load_state_dict(_lowerCamelCase	)
            A__		 =					model(_lowerCamelCase	).model[0]
        else:
            A__		 =					BartForConditionalGeneration(_lowerCamelCase	).eval()  # an existing summarization ckpt
            model.model.load_state_dict(_lowerCamelCase	)
            if hasattr(_lowerCamelCase ,       '''lm_head'''	):
                A__		 =					make_linear_from_emb(model.model.shared	)
            A__		 =					model.model(_lowerCamelCase	)[0]

    # Check results
    if fairseq_output.shape != new_model_outputs.shape:
        raise ValueError(
            f"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"	)
    if (fairseq_output != new_model_outputs).any().item():
        raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''	)
    Path(_lowerCamelCase	).mkdir(exist_ok=_lowerCamelCase	)
    model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
   _lowercase						  =		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=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
   )
   _lowercase						  =		parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       str	)->  int:
    if height >= 1:
        move_tower(height - 1 ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)
        move_disk(UpperCamelCase_ ,       UpperCamelCase_	)
        move_tower(height - 1 ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       List[Any]	)->  str:
    print('''moving disk from''' ,       UpperCamelCase_ ,       '''to''' ,       UpperCamelCase_	)



def 		lowerCAmelCase__    (					)->  Tuple:
    A__		 =					int(input('''Height of hanoi: '''	).strip()	)
    move_tower(UpperCamelCase_ ,       '''A''' ,       '''B''' ,       '''C'''	)


if __name__ == "__main__":
   main()
