


'''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


__A					:  Optional[int]											=      ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
__A					:  Optional[int]											=      {"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					:  str											=      logging.get_logger(__name__)

__A					:  str											=      " Hello world! cécé herlolip"

__A					:  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 				UpperCamelCase_			(    A__					:   Optional[Any]					):
  '''simple docstring'''





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







def 				UpperCamelCase_			(    A__					:   str							,    A__					:   List[Any]							,    A__					:   Dict					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[int]			=   dct.pop(A__					)
  lowerCAmelCase_			:      Any			=   val







def 				UpperCamelCase_			(    A__					:   Union[str, Any]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Any			=   torch.load(A__							,    map_location="""cpu"""					)
  lowerCAmelCase_			:      List[Any]			=   torch.hub.load("""pytorch/fairseq"""							,    """bart.large.cnn"""					).eval()
  hub_interface.model.load_state_dict(sd["""model"""]					)
  return hub_interface







def 				UpperCamelCase_			(    A__					:   Dict					):
  '''simple docstring'''





  lowerCAmelCase_,							lowerCAmelCase_			:      str			=   emb.weight.shape
  lowerCAmelCase_			:      str			=   nn.Linear(A__							,    A__							,    bias=A__					)
  lowerCAmelCase_			:      Tuple			=   emb.weight.data
  return lin_layer







@torch.no_grad()
def 				UpperCamelCase_			(    A__					:   Union[str, Any]							,    A__					:   Dict							,    A__					:   Any=None					):
  '''simple docstring'''





  if not os.path.exists(A__					):
    lowerCAmelCase_			:      List[str]			=   torch.hub.load("""pytorch/fairseq"""							,    A__					).eval()
  else:
    lowerCAmelCase_			:      str			=   load_xsum_checkpoint(A__					)

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







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



