

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


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

lowerCamelCase           =					''' Hello world! cécé herlolip'''

lowerCamelCase           =					[
    ('''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      : Tuple							):





  '''simple docstring'''







  a__  =[
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(snake_case_	,      snake_case_							)




def 			UpperCAmelCase__				(			_A      : Any	,      _A      : str	,      _A      : List[Any]							):





  '''simple docstring'''







  a__  =dct.pop(snake_case_							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  a__  =torch.load(snake_case_	,      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 			UpperCAmelCase__				(			_A      : int							):





  '''simple docstring'''







  a__,      a__  =emb.weight.shape
  a__  =nn.Linear(snake_case_	,      snake_case_	,      bias=snake_case_							)
  a__  =emb.weight.data
  return lin_layer




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : List[Any]	,      _A      : Union[str, Any]=None							):





  '''simple docstring'''







  if not os.path.exists(snake_case_							):
    a__  =torch.hub.load('''pytorch/fairseq'''	,      snake_case_							).eval()
  else:
    a__  =load_xsum_checkpoint(snake_case_							)

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


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

'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	int							)  ->    Optional[Any]:
  return [
      {"col_1": 3, "col_2": "a"},
      {"col_1": 2, "col_2": "b"},
      {"col_1": 1, "col_2": "c"},
      {"col_1": 0, "col_2": "d"},
  ]
 def a					(      self   :	List[Any]							)  ->    Any:
  __lowerCAmelCase											=						{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
  return Dataset.from_dict(SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						self._create_example_records()
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  self.assertListEqual(dset.column_names      ,       ["""col_1""", """col_2"""]							)
  for i, r in enumerate(SCREAMING_SNAKE_CASE__							):
   self.assertDictEqual(SCREAMING_SNAKE_CASE__      ,       example_records[i]							)
 def a					(      self   :	Tuple							)  ->    List[str]:
  __lowerCAmelCase											=						self._create_example_records()
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}							)
  self.assertEqual(dset.info      ,       dset_from_dict.info							)
 def a					(      self   :	List[str]							)  ->    List[str]:  # checks what happens with missing columns
  __lowerCAmelCase											=						[{"""col_1""": 1}, {"""col_2""": """x"""}]
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  self.assertDictEqual(dset[0]      ,       {"""col_1""": 1}							)
  self.assertDictEqual(dset[1]      ,       {"""col_1""": None}							)  # NB: first record is used for columns
 def a					(      self   :	Dict							)  ->    Optional[int]:  # checks if the type can be inferred from the second record
  __lowerCAmelCase											=						[{"""col_1""": []}, {"""col_1""": [1, 2]}]
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(dset.info.features["""col_1"""]      ,       Sequence(Value("""int64"""							)							)							)





 def a					(      self   :	Optional[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						Dataset.from_list([]							)
  self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       0							)
  self.assertListEqual(dset.column_names      ,       []							)






