

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

__UpperCAmelCase    				=     ' Hello world! cécé herlolip'

__UpperCAmelCase    				=     [
    ('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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    dct.pop(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    val







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Any:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    emb.weight.shape
     _lowerCAmelCase   :					Any        =    nn.Linear(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							bias=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[Any]        =    emb.weight.data
     return lin_layer







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None				)       ->      List[Any]:

     """simple docstring"""







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

     bart.model.upgrade_state_dict(bart.model.state_dict()				)
     if hf_checkpoint_name is None:
          _lowerCAmelCase   :					int        =    checkpoint_path.replace("."		,							"-"				)
     _lowerCAmelCase   :					Any        =    BartConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					str        =    bart.encode(SCREAMING_SNAKE_CASE				).unsqueeze(0				)
     _lowerCAmelCase   :					Tuple        =    BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				).encode(SCREAMING_SNAKE_CASE		,							return_tensors="pt"				).unsqueeze(0				)
     if not torch.eq(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				).all():
          raise ValueError(
              f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''				)

     if checkpoint_path == "bart.large.mnli":
          _lowerCAmelCase   :					Any        =    bart.state_dict()
          remove_ignore_keys_(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Dict        =    state_dict["model.decoder.embed_tokens.weight"]
          for src, dest in mnli_rename_keys:
               rename_key(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Tuple        =    BartForSequenceClassification(SCREAMING_SNAKE_CASE				).eval()
          model.load_state_dict(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Optional[Any]        =    bart.predict("mnli"		,							SCREAMING_SNAKE_CASE		,							return_logits=SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Dict        =    model(SCREAMING_SNAKE_CASE				)[0]  # logits
     else:  # no classification heads to worry about
          _lowerCAmelCase   :					Tuple        =    bart.model.state_dict()
          remove_ignore_keys_(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Any        =    state_dict["decoder.embed_tokens.weight"]
          _lowerCAmelCase   :					Optional[int]        =    bart.extract_features(SCREAMING_SNAKE_CASE				)
          if hf_checkpoint_name == "facebook/bart-large":
               _lowerCAmelCase   :					List[str]        =    BartModel(SCREAMING_SNAKE_CASE				).eval()
               model.load_state_dict(SCREAMING_SNAKE_CASE				)
               _lowerCAmelCase   :					str        =    model(SCREAMING_SNAKE_CASE				).model[0]
          else:
               _lowerCAmelCase   :					int        =    BartForConditionalGeneration(SCREAMING_SNAKE_CASE				).eval()  # an existing summarization ckpt
               model.model.load_state_dict(SCREAMING_SNAKE_CASE				)
               if hasattr(SCREAMING_SNAKE_CASE		,							"lm_head"				):
                    _lowerCAmelCase   :					Tuple        =    make_linear_from_emb(model.model.shared				)
               _lowerCAmelCase   :					str        =    model.model(SCREAMING_SNAKE_CASE				)[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(SCREAMING_SNAKE_CASE				).mkdir(exist_ok=SCREAMING_SNAKE_CASE				)
     model.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     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    				=     parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__UpperCAmelCase    				=     {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ErnieForCausalLM',
      'ErnieForMaskedLM',
      'ErnieForMultipleChoice',
      'ErnieForNextSentencePrediction',
      'ErnieForPreTraining',
      'ErnieForQuestionAnswering',
      'ErnieForSequenceClassification',
      'ErnieForTokenClassification',
      'ErnieModel',
      'ErniePreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ernie import (
        ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ErnieForCausalLM,
        ErnieForMaskedLM,
        ErnieForMultipleChoice,
        ErnieForNextSentencePrediction,
        ErnieForPreTraining,
        ErnieForQuestionAnswering,
        ErnieForSequenceClassification,
        ErnieForTokenClassification,
        ErnieModel,
        ErniePreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






