

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_    :						Tuple    		=      {"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_    :						Optional[int]    		=      logging.get_logger(__name__)

a_    :						List[Any]    		=      " Hello world! cécé herlolip"

a_    :						List[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        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[
     'encoder.version',
     'decoder.version',
     'model.encoder.version',
     'model.decoder.version',
     '_float_tensor',
 ]
 for k in ignore_keys:
  state_dict.pop(_UpperCamelCase		,							_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Optional[int]	)		->							Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			dct.pop(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			val


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any	)		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.load(_UpperCamelCase		,							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        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			emb.weight.shape
 SCREAMING_SNAKE_CASE								=			nn.Linear(_UpperCamelCase		,							_UpperCamelCase		,							bias=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			emb.weight.data
 return lin_layer


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Tuple=None	)		->							Tuple:
 '''simple docstring'''


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

 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(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			bart.encode(_UpperCamelCase	).unsqueeze(0	)
 SCREAMING_SNAKE_CASE								=			BartTokenizer.from_pretrained(_UpperCamelCase	).encode(_UpperCamelCase		,							return_tensors='pt'	).unsqueeze(0	)
 if not torch.eq(_UpperCamelCase		,							_UpperCamelCase	).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_(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			state_dict['model.decoder.embed_tokens.weight']
  for src, dest in mnli_rename_keys:
   rename_key(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			BartForSequenceClassification(_UpperCamelCase	).eval()
  model.load_state_dict(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			bart.predict('mnli'		,							_UpperCamelCase		,							return_logits=_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			model(_UpperCamelCase	)[0]  # logits
 else:  # no classification heads to worry about
  SCREAMING_SNAKE_CASE								=			bart.model.state_dict()
  remove_ignore_keys_(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			state_dict['decoder.embed_tokens.weight']
  SCREAMING_SNAKE_CASE								=			bart.extract_features(_UpperCamelCase	)
  if hf_checkpoint_name == "facebook/bart-large":
   SCREAMING_SNAKE_CASE								=			BartModel(_UpperCamelCase	).eval()
   model.load_state_dict(_UpperCamelCase	)
   SCREAMING_SNAKE_CASE								=			model(_UpperCamelCase	).model[0]
  else:
   SCREAMING_SNAKE_CASE								=			BartForConditionalGeneration(_UpperCamelCase	).eval()  # an existing summarization ckpt
   model.model.load_state_dict(_UpperCamelCase	)
   if hasattr(_UpperCamelCase		,							'lm_head'	):
    SCREAMING_SNAKE_CASE								=			make_linear_from_emb(model.model.shared	)
   SCREAMING_SNAKE_CASE								=			model.model(_UpperCamelCase	)[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(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
 model.save_pretrained(_UpperCamelCase	)


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_    :						Optional[Any]    		=      parser.parse_args()
      convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)




import numpy as np


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return 1 / (1 + np.exp(-vector	))


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return vector * sigmoid(_UpperCamelCase	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

