




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase		:  List[str]	=   get_tests_dir("fixtures/test_sentencepiece.model")

_UpperCAmelCase		:  List[Any]	=   {"target_lang": "fi", "source_lang": "en"}
_UpperCAmelCase		:  Tuple	=   ">>zh<<"
_UpperCAmelCase		:  str	=   "Helsinki-NLP/"

if is_torch_available():
     _UpperCAmelCase		:  List[Any]	=   "pt"
elif is_tf_available():
     _UpperCAmelCase		:  Tuple	=   "tf"
else:
     _UpperCAmelCase		:  Dict	=   "jax"




@require_sentencepiece
class       __lowerCAmelCase				(							A_					,       unittest.TestCase):
       _a           =  MarianTokenizer
       _a           =  False
       _a           =  True






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         super().setUp()
         lowercase			:Tuple							=  ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
         lowercase			:int							=  dict(zip(_lowerCamelCase				,    range(len(_lowerCamelCase ) ) ) )
         lowercase			:Optional[Any]							=  Path(self.tmpdirname )
         save_json(_lowerCamelCase				,    save_dir / VOCAB_FILES_NAMES["vocab"] )
         save_json(_lowerCamelCase				,    save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"] )
         if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
           copyfile(_lowerCamelCase				,    save_dir / VOCAB_FILES_NAMES["source_spm"] )
           copyfile(_lowerCamelCase				,    save_dir / VOCAB_FILES_NAMES["target_spm"] )

         lowercase			:int							=  MarianTokenizer.from_pretrained(self.tmpdirname )
         tokenizer.save_pretrained(self.tmpdirname )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    **_lowerCAmelCase:    Optional[int] ):
         return MarianTokenizer.from_pretrained(self.tmpdirname				,    **_lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    int ):
         return (
             "This is a test",
             "This is a test",
         )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:List[Any]							=  "</s>"
         lowercase			:str							=  0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase )				,    _lowerCamelCase )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase )				,    _lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:int							=  list(self.get_tokenizer().get_vocab().keys() )

         self.assertEqual(vocab_keys[0]				,    "</s>" )
         self.assertEqual(vocab_keys[1]				,    "<unk>" )
         self.assertEqual(vocab_keys[-1]				,    "<pad>" )
         self.assertEqual(len(_lowerCamelCase )				,    9 )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         self.assertEqual(self.get_tokenizer().vocab_size				,    9 )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Optional[Any]							=  MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de" )
         lowercase			:int							=  en_de_tokenizer(["I am a small frog"]				,    return_tensors=_lowerCamelCase )
         self.assertIsInstance(_lowerCamelCase				,    _lowerCamelCase )
         lowercase			:str							=  [38, 1_21, 14, 6_97, 3_88_48, 0]
         self.assertListEqual(_lowerCamelCase				,    batch.input_ids[0] )

         lowercase			:Any							=  tempfile.mkdtemp()
         en_de_tokenizer.save_pretrained(_lowerCamelCase )
         lowercase			:List[Any]							=  [x.name for x in Path(_lowerCamelCase ).glob("*" )]
         self.assertIn("source.spm"				,    _lowerCamelCase )
         MarianTokenizer.from_pretrained(_lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Optional[int]							=  self.get_tokenizer()

         lowercase			:Union[str, Any]							=  tok(
             ["I am a small frog" * 10_00, "I am a small frog"]				,    padding=_lowerCamelCase				,    truncation=_lowerCamelCase				,    return_tensors=_lowerCamelCase )
         self.assertIsInstance(_lowerCamelCase				,    _lowerCamelCase )
         self.assertEqual(batch.input_ids.shape				,    (2, 5_12) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:int							=  self.get_tokenizer()
         lowercase			:Optional[int]							=  tok(["I am a tiny frog", "I am a small frog"]				,    padding=_lowerCamelCase				,    return_tensors=_lowerCamelCase )
         self.assertIsInstance(_lowerCamelCase				,    _lowerCamelCase )
         self.assertEqual(batch_smaller.input_ids.shape				,    (2, 10) )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         # fmt: off
         lowercase			:Tuple							=  {"input_ids": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCamelCase				,    model_name="Helsinki-NLP/opus-mt-en-de"				,    revision="1a8c2263da11e68e50938f97e10cd57820bd504c"				,    decode_kwargs={"use_source_tokenizer": True}				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:List[str]							=  MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs" )

         lowercase			:List[Any]							=  "Tämä on testi"
         lowercase			:Union[str, Any]							=  "This is a test"

         lowercase			:int							=  [76, 7, 20_47, 2]
         lowercase			:str							=  [69, 12, 11, 9_40, 2]

         lowercase			:Dict							=  tokenizer(_lowerCamelCase ).input_ids
         self.assertListEqual(_lowerCamelCase				,    _lowerCamelCase )

         lowercase			:Any							=  tokenizer(text_target=_lowerCamelCase ).input_ids
         self.assertListEqual(_lowerCamelCase				,    _lowerCamelCase )

         lowercase			:Tuple							=  tokenizer.decode(_lowerCamelCase				,    skip_special_tokens=_lowerCamelCase )
         self.assertEqual(_lowerCamelCase				,    _lowerCamelCase )





"""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__          = ['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 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''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   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

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



