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


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

SCREAMING_SNAKE_CASE :	Dict							     =		""" Hello world! cécé herlolip"""

SCREAMING_SNAKE_CASE :	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   __A		(			_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			_A					):



 """simple docstring"""


 __a       =  torch.load(_A							,				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   __A		(			_A					):



 """simple docstring"""


 __a       ,     __a       =  emb.weight.shape
 __a       =  nn.Linear(_A							,				_A							,				bias=_A					)
 __a       =  emb.weight.data
 return lin_layer



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=None					):



 """simple docstring"""


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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		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"""
  )
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


