





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					:				List[str]				=			["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
_A					:				Any				=			{"""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					:				Dict				=			logging.get_logger(__name__)

_A					:				str				=			""" Hello world! cécé herlolip"""

_A					:				str				=			[
    ("""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 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''_float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase_ ,							lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			dct.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location='''cpu'''	)
    SCREAMING_SNAKE_CASE__											=			torch.hub.load('''pytorch/fairseq''' ,							'''bart.large.cnn'''	).eval()
    hub_interface.model.load_state_dict(sd['''model''']	)
    return hub_interface


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			emb.weight.shape
    SCREAMING_SNAKE_CASE__											=			nn.Linear(lowerCAmelCase_ ,							lowerCAmelCase_ ,							bias=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			emb.weight.data
    return lin_layer


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None	)				->						Tuple:
    if not os.path.exists(lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			torch.hub.load('''pytorch/fairseq''' ,							lowerCAmelCase_	).eval()
    else:
        SCREAMING_SNAKE_CASE__											=			load_xsum_checkpoint(lowerCAmelCase_	)

    bart.model.upgrade_state_dict(bart.model.state_dict()	)
    if hf_checkpoint_name is None:
        SCREAMING_SNAKE_CASE__											=			checkpoint_path.replace('''.''' ,							'''-'''	)
    SCREAMING_SNAKE_CASE__											=			BartConfig.from_pretrained(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			bart.encode(lowerCAmelCase_	).unsqueeze(0	)
    SCREAMING_SNAKE_CASE__											=			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":
        SCREAMING_SNAKE_CASE__											=			bart.state_dict()
        remove_ignore_keys_(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			state_dict['''model.decoder.embed_tokens.weight''']
        for src, dest in mnli_rename_keys:
            rename_key(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			BartForSequenceClassification(lowerCAmelCase_	).eval()
        model.load_state_dict(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			bart.predict('''mnli''' ,							lowerCAmelCase_ ,							return_logits=lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			model(lowerCAmelCase_	)[0]  # logits
    else:  # no classification heads to worry about
        SCREAMING_SNAKE_CASE__											=			bart.model.state_dict()
        remove_ignore_keys_(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			state_dict['''decoder.embed_tokens.weight''']
        SCREAMING_SNAKE_CASE__											=			bart.extract_features(lowerCAmelCase_	)
        if hf_checkpoint_name == "facebook/bart-large":
            SCREAMING_SNAKE_CASE__											=			BartModel(lowerCAmelCase_	).eval()
            model.load_state_dict(lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			model(lowerCAmelCase_	).model[0]
        else:
            SCREAMING_SNAKE_CASE__											=			BartForConditionalGeneration(lowerCAmelCase_	).eval()  # an existing summarization ckpt
            model.model.load_state_dict(lowerCAmelCase_	)
            if hasattr(lowerCAmelCase_ ,							'''lm_head'''	):
                SCREAMING_SNAKE_CASE__											=			make_linear_from_emb(model.model.shared	)
            SCREAMING_SNAKE_CASE__											=			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__":
  _A					:				List[str]				=			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					:				Union[str, Any]				=			parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
