

import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_             =     logging.get_logger(__name__)
def   a   (    A__			:     List[str]  )					->   int:





      """simple docstring"""



      _lowercase			 =torch.load(A__   ,     map_location='cpu'  )
      if "model" in sd.keys():
            _lowercase			 =torch.load(A__   ,     map_location='cpu'  )['model']

      # pop unnecessary weights
      _lowercase			 =[
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(A__  )

      _lowercase			 ={
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _lowercase			 =sd.pop(A__  )

      _lowercase			 =list(sd.keys()  )
      for key in keys:
            if ".qkv_proj." in key:
                  _lowercase			 =sd[key]
                  # We split QKV in separate Q,K,V

                  _lowercase			 =key.replace('.qkv_proj.'   ,     '.q_proj.'  )
                  _lowercase			 =key.replace('.qkv_proj.'   ,     '.k_proj.'  )
                  _lowercase			 =key.replace('.qkv_proj.'   ,     '.v_proj.'  )

                  _lowercase			 =value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _lowercase , _lowercase , _lowercase			 =torch.split(A__   ,     depth // 3   ,     dim=0  )

                  _lowercase			 =q
                  _lowercase			 =k
                  _lowercase			 =v
                  del sd[key]

      return sd
@torch.no_grad()
def   a   (    A__			:     int   ,     A__			:     Union[str, Any]   ,     A__			:     int=None  )					->   Dict:





      """simple docstring"""



      _lowercase			 =load_checkpoint(A__  )

      if config is not None:
            _lowercase			 =OPTConfig.from_pretrained(A__  )
      else:
            _lowercase			 =OPTConfig()

      _lowercase			 =OPTModel(A__  ).half().eval()
      model.load_state_dict(A__  )

      # Check results
      Path(A__  ).mkdir(exist_ok=A__  )
      model.save_pretrained(A__  )


if __name__ == "__main__":
 lowercase_             =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--fairseq_path',
     type=str,
     help=(
         'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
         ' https://huggingface.co/models?other=opt_metasq'
     ),
 )
 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='Define HF config.')
 lowercase_             =     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





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


lowercase_             =     'bert-base-cased'
lowercase_             =     'google/pegasus-xsum'
lowercase_             =     [' Sam ate lunch today.', 'Sams lunch ingredients.']
lowercase_             =     ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowercase_             =     'patrickvonplaten/t5-tiny-random'
lowercase_             =     'sshleifer/bart-tiny-random'
lowercase_             =     'sshleifer/tiny-mbart'
lowercase_             =     'sshleifer/tiny-marian-en-de'
def   a   (    A__			:     Path   ,     A__			:     list  )					->   Tuple:





      """simple docstring"""



      _lowercase			 ='\n'.join(A__  )
      Path(A__  ).open('w'  ).writelines(A__  )
def   a   (    A__			:     str  )					->   Union[str, Any]:





      """simple docstring"""



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







class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]							,     )
  @slow
  def 						A__  (       self							,     lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in ARTICLES      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in SUMMARIES      )
        _lowercase			 =4
        _lowercase			 =8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        _lowercase , _lowercase			 ='ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
        _lowercase			 =SeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=lowerCAmelCase							,     max_target_length=lowerCAmelCase							,     src_lang=lowerCAmelCase							,     tgt_lang=lowerCAmelCase							,     )
        _lowercase			 =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			 =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 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in ARTICLES      )
        _lowercase			 =max(len(tokenizer.encode(lowerCAmelCase      )      ) for a in SUMMARIES      )
        _lowercase			 =4
        _lowercase			 =LegacySeqaSeqDataset(
            lowerCAmelCase							,     data_dir=lowerCAmelCase							,     type_path='train'							,     max_source_length=20							,     max_target_length=lowerCAmelCase							,     )
        _lowercase			 =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 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'      )

        _lowercase			 =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
        _lowercase			 =tmp_dir.joinpath('train.source'      ).open().readlines()
        _lowercase			 =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
        pack_data_dir(lowerCAmelCase							,     lowerCAmelCase							,     128							,     lowerCAmelCase      )
        _lowercase			 ={x.name for x in tmp_dir.iterdir()}
        _lowercase			 ={x.name for x in save_dir.iterdir()}
        _lowercase			 =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 						A__  (       self      )						->				int:
        '''simple docstring'''






        if not FAIRSEQ_AVAILABLE:
              return
        _lowercase , _lowercase , _lowercase			 =self._get_dataset(max_len=64      )
        _lowercase			 =64
        _lowercase			 =ds.make_dynamic_sampler(lowerCAmelCase							,     required_batch_size_multiple=lowerCAmelCase      )
        _lowercase			 =[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			 =DataLoader(lowerCAmelCase							,     batch_sampler=lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2      )
        _lowercase			 =[]
        _lowercase			 =[]
        for batch in data_loader:
              _lowercase			 =batch['input_ids'].shape
              _lowercase			 =src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              _lowercase			 =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 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase , _lowercase , _lowercase			 =self._get_dataset(max_len=512      )
        _lowercase			 =2
        _lowercase			 =ds.make_sortish_sampler(lowerCAmelCase							,     shuffle=lowerCAmelCase      )

        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2      )
        _lowercase			 =DataLoader(lowerCAmelCase							,     batch_size=lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2							,     sampler=lowerCAmelCase      )

        _lowercase			 =tokenizer.pad_token_id

        def count_pad_tokens(lowerCAmelCase							,     lowerCAmelCase="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 						A__  (       self							,     lowerCAmelCase=1_000							,     lowerCAmelCase=128      )						->				Union[str, Any]:
        '''simple docstring'''






        if os.getenv('USE_REAL_DATA'							,     lowerCAmelCase      ):
              _lowercase			 ='examples/seq2seq/wmt_en_ro'
              _lowercase			 =max_len * 2 * 64
              if not Path(lowerCAmelCase      ).joinpath('train.len'      ).exists():
                    save_len_file(lowerCAmelCase							,     lowerCAmelCase      )
        else:
              _lowercase			 ='examples/seq2seq/test_data/wmt_en_ro'
              _lowercase			 =max_len * 4
              save_len_file(lowerCAmelCase							,     lowerCAmelCase      )

        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase      )
        _lowercase			 =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 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase , _lowercase , _lowercase			 =self._get_dataset()
        _lowercase			 =set(DistributedSortishSampler(lowerCAmelCase							,     256							,     num_replicas=2							,     rank=0							,     add_extra_examples=lowerCAmelCase      )      )
        _lowercase			 =set(DistributedSortishSampler(lowerCAmelCase							,     256							,     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 						A__  (       self							,     lowerCAmelCase      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =AutoTokenizer.from_pretrained(lowerCAmelCase							,     use_fast=lowerCAmelCase      )
        if tok_name == MBART_TINY:
              _lowercase			 =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			 =train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
              _lowercase			 =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			 =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


