



"""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


UpperCAmelCase						:		int			= ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
UpperCAmelCase						:		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()
UpperCAmelCase						:		Dict			= logging.get_logger(__name__)

UpperCAmelCase						:		Dict			= " Hello world! cécé herlolip"

UpperCAmelCase						:		List[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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Optional[int]:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Union[str, Any]:







  '''simple docstring'''


  lowercase_								= dct.pop(__lowerCAmelCase							)
  lowercase_								= val


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  lowercase_								= torch.load(__lowerCAmelCase ,  map_location="""cpu"""							)
  lowercase_								= torch.hub.load("""pytorch/fairseq""" ,  """bart.large.cnn"""							).eval()
  hub_interface.model.load_state_dict(sd["""model"""]							)
  return hub_interface


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_					,    lowercase_								= emb.weight.shape
  lowercase_								= nn.Linear(__lowerCAmelCase ,  __lowerCAmelCase ,  bias=__lowerCAmelCase							)
  lowercase_								= emb.weight.data
  return lin_layer


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None							) ->						str:







  '''simple docstring'''


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

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






"""simple docstring"""
import os
from collections.abc import Iterator


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						Iterator[str]:







  '''simple docstring'''


  for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase							):
    lowercase_								= [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(__lowerCAmelCase							)[1] in (".py", ".ipynb"):
        yield os.path.join(__lowerCAmelCase ,  __lowerCAmelCase							).lstrip("""./"""							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  return F'''{i * "  "}*''' if i else "\n##"


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= old_path.split(os.sep							)
  for i, new_part in enumerate(new_path.split(os.sep							)							):
    if (i + 1 > len(__lowerCAmelCase							) or old_parts[i] != new_part) and new_part:
      print(F'''{md_prefix(__lowerCAmelCase							)} {new_part.replace("_" ,  " "							).title()}'''							)
  return new_path


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						None:







  '''simple docstring'''


  lowercase_								= """"""
  for filepath in sorted(good_file_paths(__lowerCAmelCase							)							):
    lowercase_					,    lowercase_								= os.path.split(__lowerCAmelCase							)
    if filepath != old_path:
      lowercase_								= print_path(__lowerCAmelCase ,  __lowerCAmelCase							)
    lowercase_								= (filepath.count(os.sep							) + 1) if filepath else 0
    lowercase_								= F'''{filepath}/{filename}'''.replace(""" """ ,  """%20"""							)
    lowercase_								= os.path.splitext(filename.replace("""_""" ,  """ """							).title()							)[0]
    print(F'''{md_prefix(__lowerCAmelCase							)} [{filename}]({url})'''							)


if __name__ == "__main__":
       print_directory_md(".")

