
'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	len(lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	[[0] * n for i in range(lowerCamelCase_			)]
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Optional[int]     		=	y_points[i]

 for i in range(2      ,			lowerCamelCase_			):
  for j in range(lowerCamelCase_      ,			lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	(
       (xa - x_points[j - i + 1]) * q[j][i - 1]
       - (xa - x_points[j]) * q[j - 1][i - 1]
   ) / (x_points[j] - x_points[j - i + 1])

 return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()







'''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


snake_case									=    ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
snake_case									=    {"""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()
snake_case									=    logging.get_logger(__name__)

snake_case									=    """ Hello world! cécé herlolip"""

snake_case									=    [
    ("""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        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   List[str]     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)
 lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			"bart.large.cnn"			).eval()
 hub_interface.model.load_state_dict(sd["model"]			)
 return hub_interface


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	emb.weight.shape
 lowerCAmelCase__     :   List[Any]     		=	nn.Linear(lowerCamelCase_      ,			lowerCamelCase_      ,			bias=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	emb.weight.data
 return lin_layer


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None			):







 """simple docstring"""


 if not os.path.exists(lowerCamelCase_			):
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			lowerCamelCase_			).eval()
 else:
  lowerCAmelCase__     :   Tuple     		=	load_xsum_checkpoint(lowerCamelCase_			)

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





