




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_UpperCAmelCase		:  Dict	=   "bert-base-cased"
_UpperCAmelCase		:  List[str]	=   "google/pegasus-xsum"
_UpperCAmelCase		:  Optional[int]	=   [" Sam ate lunch today.", "Sams lunch ingredients."]
_UpperCAmelCase		:  Optional[Any]	=   ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_UpperCAmelCase		:  Optional[int]	=   "patrickvonplaten/t5-tiny-random"
_UpperCAmelCase		:  Optional[int]	=   "sshleifer/bart-tiny-random"
_UpperCAmelCase		:  List[Any]	=   "sshleifer/tiny-mbart"
_UpperCAmelCase		:  str	=   "sshleifer/tiny-marian-en-de"



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:int							=  "\n".join(__lowerCamelCase      )
  Path(__lowerCamelCase      ).open("w"      ).writelines(__lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(__lowerCamelCase,	F"{split}.source"      ),	__lowerCamelCase      )
    _dump_articles(os.path.join(__lowerCamelCase,	F"{split}.target"      ),	__lowerCamelCase      )
  return tmp_dir







class       __lowerCAmelCase				(							A_):






       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]				,    )
       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    Union[str, Any] ):
         lowercase			:List[Any]							=  AutoTokenizer.from_pretrained(_lowerCamelCase )
         lowercase			:Dict							=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
         lowercase			:List[str]							=  max(len(tokenizer.encode(_lowerCamelCase ) ) for a in ARTICLES )
         lowercase			:Optional[Any]							=  max(len(tokenizer.encode(_lowerCamelCase ) ) for a in SUMMARIES )
         lowercase			:Optional[int]							=  4
         lowercase			:List[Any]							=  8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         lowercase		,			lowercase			:Any							=  "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         lowercase			:Any							=  SeqaSeqDataset(
             _lowerCamelCase				,    data_dir=_lowerCamelCase				,    type_path="train"				,    max_source_length=_lowerCamelCase				,    max_target_length=_lowerCamelCase				,    src_lang=_lowerCamelCase				,    tgt_lang=_lowerCamelCase				,    )
         lowercase			:Optional[int]							=  DataLoader(_lowerCamelCase				,    batch_size=2				,    collate_fn=train_dataset.collate_fn )
         for batch in dataloader:
           assert isinstance(_lowerCamelCase				,    _lowerCamelCase )
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_src_len
           # show that targets are the same len
           assert batch["labels"].shape[1] == max_tgt_len
           if tok_name != MBART_TINY:
             continue
           # check language codes in correct place
           lowercase			:Union[str, Any]							=  shift_tokens_right(batch["labels"]				,    tokenizer.pad_token_id )
           assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
           assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

           break  # No need to test every batch






       @parameterized.expand([BART_TINY, BERT_BASE_CASED] )
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    Optional[Any] ):
         lowercase			:List[Any]							=  AutoTokenizer.from_pretrained(_lowerCamelCase )
         lowercase			:Any							=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
         lowercase			:int							=  max(len(tokenizer.encode(_lowerCamelCase ) ) for a in ARTICLES )
         lowercase			:Optional[int]							=  max(len(tokenizer.encode(_lowerCamelCase ) ) for a in SUMMARIES )
         lowercase			:str							=  4
         lowercase			:List[str]							=  LegacySeqaSeqDataset(
             _lowerCamelCase				,    data_dir=_lowerCamelCase				,    type_path="train"				,    max_source_length=20				,    max_target_length=_lowerCamelCase				,    )
         lowercase			:Dict							=  DataLoader(_lowerCamelCase				,    batch_size=2				,    collate_fn=train_dataset.collate_fn )
         for batch in dataloader:
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_len_source
           assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
           # show that targets were truncated
           assert batch["labels"].shape[1] == trunc_target  # Truncated
           assert max_len_target > trunc_target  # Truncated
           break  # No need to test every batch






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Tuple							=  AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )

         lowercase			:Dict							=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
         lowercase			:Optional[Any]							=  tmp_dir.joinpath("train.source" ).open().readlines()
         lowercase			:Union[str, Any]							=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
         pack_data_dir(_lowerCamelCase				,    _lowerCamelCase				,    1_28				,    _lowerCamelCase )
         lowercase			:Optional[int]							=  {x.name for x in tmp_dir.iterdir()}
         lowercase			:Dict							=  {x.name for x in save_dir.iterdir()}
         lowercase			:List[str]							=  save_dir.joinpath("train.source" ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(_lowerCamelCase ) < len(_lowerCamelCase )
         assert len(_lowerCamelCase ) == 1
         assert len(packed_examples[0] ) == sum(len(_lowerCamelCase ) for x in orig_examples )
         assert orig_paths == new_paths






       @pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,    reason="This test requires fairseq" )
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if not FAIRSEQ_AVAILABLE:
           return
         lowercase		,			lowercase		,			lowercase			:Any							=  self._get_dataset(max_len=64 )
         lowercase			:List[str]							=  64
         lowercase			:Optional[int]							=  ds.make_dynamic_sampler(_lowerCamelCase				,    required_batch_size_multiple=_lowerCamelCase )
         lowercase			:Tuple							=  [len(_lowerCamelCase ) for x in batch_sampler]
         assert len(set(_lowerCamelCase ) ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(_lowerCamelCase ) == len(_lowerCamelCase )  # no dropped or added examples
         lowercase			:Dict							=  DataLoader(_lowerCamelCase				,    batch_sampler=_lowerCamelCase				,    collate_fn=ds.collate_fn				,    num_workers=2 )
         lowercase			:str							=  []
         lowercase			:Union[str, Any]							=  []
         for batch in data_loader:
           lowercase			:Dict							=  batch["input_ids"].shape
           lowercase			:Tuple							=  src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           lowercase			:Union[str, Any]							=  np.product(batch["input_ids"].shape )
           num_src_per_batch.append(_lowerCamelCase )
           if num_src_tokens > (max_tokens * 1.1):
             failures.append(_lowerCamelCase )
         assert num_src_per_batch[0] == max(_lowerCamelCase )
         if failures:
           raise AssertionError(F"too many tokens in {len(_lowerCamelCase )} batches" )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase		,			lowercase		,			lowercase			:Tuple							=  self._get_dataset(max_len=5_12 )
         lowercase			:Union[str, Any]							=  2
         lowercase			:Any							=  ds.make_sortish_sampler(_lowerCamelCase				,    shuffle=_lowerCamelCase )

         lowercase			:str							=  DataLoader(_lowerCamelCase				,    batch_size=_lowerCamelCase				,    collate_fn=ds.collate_fn				,    num_workers=2 )
         lowercase			:Tuple							=  DataLoader(_lowerCamelCase				,    batch_size=_lowerCamelCase				,    collate_fn=ds.collate_fn				,    num_workers=2				,    sampler=_lowerCamelCase )

         lowercase			:Dict							=  tokenizer.pad_token_id

         def count_pad_tokens(_lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Optional[int]="input_ids" ):
           return [batch[k].eq(_lowerCamelCase ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(_lowerCamelCase				,    k="labels" ) ) < sum(count_pad_tokens(_lowerCamelCase				,    k="labels" ) )
         assert sum(count_pad_tokens(_lowerCamelCase ) ) < sum(count_pad_tokens(_lowerCamelCase ) )
         assert len(_lowerCamelCase ) == len(_lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Any=10_00				,    _lowerCAmelCase:    Optional[Any]=1_28 ):
         if os.getenv("USE_REAL_DATA"				,    _lowerCamelCase ):
           lowercase			:Dict							=  "examples/seq2seq/wmt_en_ro"
           lowercase			:List[Any]							=  max_len * 2 * 64
           if not Path(_lowerCamelCase ).joinpath("train.len" ).exists():
             save_len_file(_lowerCamelCase				,    _lowerCamelCase )
         else:
           lowercase			:Optional[Any]							=  "examples/seq2seq/test_data/wmt_en_ro"
           lowercase			:Optional[Any]							=  max_len * 4
           save_len_file(_lowerCamelCase				,    _lowerCamelCase )

         lowercase			:Optional[int]							=  AutoTokenizer.from_pretrained(_lowerCamelCase )
         lowercase			:int							=  SeqaSeqDataset(
             _lowerCamelCase				,    data_dir=_lowerCamelCase				,    type_path="train"				,    max_source_length=_lowerCamelCase				,    max_target_length=_lowerCamelCase				,    n_obs=_lowerCamelCase				,    )
         return ds, max_tokens, tokenizer






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase		,			lowercase		,			lowercase			:Tuple							=  self._get_dataset()
         lowercase			:List[str]							=  set(DistributedSortishSampler(_lowerCamelCase				,    2_56				,    num_replicas=2				,    rank=0				,    add_extra_examples=_lowerCamelCase ) )
         lowercase			:List[str]							=  set(DistributedSortishSampler(_lowerCamelCase				,    2_56				,    num_replicas=2				,    rank=1				,    add_extra_examples=_lowerCamelCase ) )
         assert idsa.intersection(_lowerCamelCase ) == set()






       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]				,    )
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    Any ):
         lowercase			:int							=  AutoTokenizer.from_pretrained(_lowerCamelCase				,    use_fast=_lowerCamelCase )
         if tok_name == MBART_TINY:
           lowercase			:Optional[Any]							=  SeqaSeqDataset(
               _lowerCamelCase				,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )				,    type_path="train"				,    max_source_length=4				,    max_target_length=8				,    src_lang="EN"				,    tgt_lang="FR"				,    )
           lowercase			:Union[str, Any]							=  train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
           lowercase			:Union[str, Any]							=  SeqaSeqDataset(
               _lowerCamelCase				,    data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )				,    type_path="train"				,    max_source_length=4				,    max_target_length=8				,    )
           lowercase			:int							=  train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(_lowerCamelCase ) == 1 if tok_name == BART_TINY else len(_lowerCamelCase ) == 0





"""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)



