






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


__a      :       Any				  =   ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__a      :       Dict				  =   {'''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()
__a      :       Union[str, Any]				  =   logging.get_logger(__name__)

__a      :       Dict				  =   ''' Hello world! cécé herlolip'''

__a      :       Optional[Any]				  =   [
    ('''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__						( __lowercase   :			List[str]    )						->      List[Any]:





    """simple docstring"""

    __A         =	[
        """encoder.version""",
        """decoder.version""",
        """model.encoder.version""",
        """model.decoder.version""",
        """_float_tensor""",
    ]
    for k in ignore_keys:
        state_dict.pop(__lowercase			,				__lowercase    )



def 			lowerCamelCase__						( __lowercase   :			int			,				__lowercase   :			str			,				__lowercase   :			List[Any]    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	dct.pop(__lowercase    )
    __A         =	val



def 			lowerCamelCase__						( __lowercase   :			str    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	torch.load(__lowercase			,				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__						( __lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    __A    ,	__A         =	emb.weight.shape
    __A         =	nn.Linear(__lowercase			,				__lowercase			,				bias=__lowercase    )
    __A         =	emb.weight.data
    return lin_layer



@torch.no_grad()
def 			lowerCamelCase__						( __lowercase   :			Optional[Any]			,				__lowercase   :			List[str]			,				__lowercase   :			Optional[Any]=None    )						->      Optional[int]:





    """simple docstring"""

    if not os.path.exists(__lowercase    ):
        __A         =	torch.hub.load("""pytorch/fairseq"""			,				__lowercase    ).eval()
    else:
        __A         =	load_xsum_checkpoint(__lowercase    )

    bart.model.upgrade_state_dict(bart.model.state_dict()    )
    if hf_checkpoint_name is None:
        __A         =	checkpoint_path.replace("""."""			,				"""-"""    )
    __A         =	BartConfig.from_pretrained(__lowercase    )
    __A         =	bart.encode(__lowercase    ).unsqueeze(0    )
    __A         =	BartTokenizer.from_pretrained(__lowercase    ).encode(__lowercase			,				return_tensors="""pt"""    ).unsqueeze(0    )
    if not torch.eq(__lowercase			,				__lowercase    ).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_(__lowercase    )
        __A         =	state_dict["""model.decoder.embed_tokens.weight"""]
        for src, dest in mnli_rename_keys:
            rename_key(__lowercase			,				__lowercase			,				__lowercase    )
        __A         =	BartForSequenceClassification(__lowercase    ).eval()
        model.load_state_dict(__lowercase    )
        __A         =	bart.predict("""mnli"""			,				__lowercase			,				return_logits=__lowercase    )
        __A         =	model(__lowercase    )[0]  # logits
    else:  # no classification heads to worry about
        __A         =	bart.model.state_dict()
        remove_ignore_keys_(__lowercase    )
        __A         =	state_dict["""decoder.embed_tokens.weight"""]
        __A         =	bart.extract_features(__lowercase    )
        if hf_checkpoint_name == "facebook/bart-large":
            __A         =	BartModel(__lowercase    ).eval()
            model.load_state_dict(__lowercase    )
            __A         =	model(__lowercase    ).model[0]
        else:
            __A         =	BartForConditionalGeneration(__lowercase    ).eval()  # an existing summarization ckpt
            model.model.load_state_dict(__lowercase    )
            if hasattr(__lowercase			,				"""lm_head"""    ):
                __A         =	make_linear_from_emb(model.model.shared    )
            __A         =	model.model(__lowercase    )[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(__lowercase    ).mkdir(exist_ok=__lowercase    )
    model.save_pretrained(__lowercase    )


if __name__ == "__main__":
  __a      :       Optional[int]				  =   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"
  )
  __a      :       List[str]				  =   parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)












from typing import TYPE_CHECKING

from ...utils import _LazyModule


__a      :       Optional[int]				  =   {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
  from .tokenization_bertweet import BertweetTokenizer

else:
  import sys

  __a      :       int				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




