
"""simple docstring"""




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


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



def 							a__ (				lowerCAmelCase	:       Path       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						"\n".join(lowerCAmelCase		)
     Path(lowerCAmelCase		).open("w"		).writelines(lowerCAmelCase		)



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     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   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ] ,			)
     @slow
     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: Any 							=						AutoTokenizer.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
          UpperCAmelCase__							: Optional[Any] 							=						max(len(tokenizer.encode(__UpperCamelCase  )  ) for a in ARTICLES  )
          UpperCAmelCase__							: Tuple 							=						max(len(tokenizer.encode(__UpperCamelCase  )  ) for a in SUMMARIES  )
          UpperCAmelCase__							: Dict 							=						4
          UpperCAmelCase__							: Tuple 							=						8
          assert max_len_target > max_src_len  # Will be truncated
          assert max_len_source > max_src_len  # Will be truncated
          UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
          UpperCAmelCase__							: Dict 							=						SeqaSeqDataset(
              __UpperCamelCase ,			data_dir=__UpperCamelCase ,			type_path="train" ,			max_source_length=__UpperCamelCase ,			max_target_length=__UpperCamelCase ,			src_lang=__UpperCamelCase ,			tgt_lang=__UpperCamelCase ,			)
          UpperCAmelCase__							: str 							=						DataLoader(__UpperCamelCase ,			batch_size=2 ,			collate_fn=train_dataset.collate_fn  )
          for batch in dataloader:
               assert isinstance(__UpperCamelCase ,			__UpperCamelCase  )
               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
               UpperCAmelCase__							: List[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  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Tuple 							=						AutoTokenizer.from_pretrained(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
          UpperCAmelCase__							: Union[str, Any] 							=						max(len(tokenizer.encode(__UpperCamelCase  )  ) for a in ARTICLES  )
          UpperCAmelCase__							: Any 							=						max(len(tokenizer.encode(__UpperCamelCase  )  ) for a in SUMMARIES  )
          UpperCAmelCase__							: List[Any] 							=						4
          UpperCAmelCase__							: Tuple 							=						LegacySeqaSeqDataset(
              __UpperCamelCase ,			data_dir=__UpperCamelCase ,			type_path="train" ,			max_source_length=20 ,			max_target_length=__UpperCamelCase ,			)
          UpperCAmelCase__							: str 							=						DataLoader(__UpperCamelCase ,			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  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Tuple 							=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"  )

          UpperCAmelCase__							: str 							=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
          UpperCAmelCase__							: Dict 							=						tmp_dir.joinpath("train.source"  ).open().readlines()
          UpperCAmelCase__							: Tuple 							=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
          pack_data_dir(__UpperCamelCase ,			__UpperCamelCase ,			1_28 ,			__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						{x.name for x in tmp_dir.iterdir()}
          UpperCAmelCase__							: Any 							=						{x.name for x in save_dir.iterdir()}
          UpperCAmelCase__							: Dict 							=						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(__UpperCamelCase  ) < len(__UpperCamelCase  )
          assert len(__UpperCamelCase  ) == 1
          assert len(packed_examples[0]  ) == sum(len(__UpperCamelCase  ) for x in orig_examples  )
          assert orig_paths == new_paths



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



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						self._get_dataset(max_len=5_12  )
          UpperCAmelCase__							: Tuple 							=						2
          UpperCAmelCase__							: Optional[Any] 							=						ds.make_sortish_sampler(__UpperCamelCase ,			shuffle=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						DataLoader(__UpperCamelCase ,			batch_size=__UpperCamelCase ,			collate_fn=ds.collate_fn ,			num_workers=2  )
          UpperCAmelCase__							: int 							=						DataLoader(__UpperCamelCase ,			batch_size=__UpperCamelCase ,			collate_fn=ds.collate_fn ,			num_workers=2 ,			sampler=__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						tokenizer.pad_token_id

          def count_pad_tokens(__UpperCamelCase ,			__UpperCamelCase="input_ids"  ):
               return [batch[k].eq(__UpperCamelCase  ).sum().item() for batch in data_loader]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase=10_00 ,			__UpperCamelCase=1_28  )->					Any:
          if os.getenv("USE_REAL_DATA" ,			__UpperCamelCase  ):
               UpperCAmelCase__							: Optional[int] 							=						"examples/seq2seq/wmt_en_ro"
               UpperCAmelCase__							: Optional[int] 							=						max_len * 2 * 64
               if not Path(__UpperCamelCase  ).joinpath("train.len"  ).exists():
                    save_len_file(__UpperCamelCase ,			__UpperCamelCase  )
          else:
               UpperCAmelCase__							: List[Any] 							=						"examples/seq2seq/test_data/wmt_en_ro"
               UpperCAmelCase__							: Any 							=						max_len * 4
               save_len_file(__UpperCamelCase ,			__UpperCamelCase  )

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



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self._get_dataset()
          UpperCAmelCase__							: Any 							=						set(DistributedSortishSampler(__UpperCamelCase ,			2_56 ,			num_replicas=2 ,			rank=0 ,			add_extra_examples=__UpperCamelCase  )  )
          UpperCAmelCase__							: int 							=						set(DistributedSortishSampler(__UpperCamelCase ,			2_56 ,			num_replicas=2 ,			rank=1 ,			add_extra_examples=__UpperCamelCase  )  )
          assert idsa.intersection(__UpperCamelCase  ) == set()



     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ] ,			)
     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Tuple 							=						AutoTokenizer.from_pretrained(__UpperCamelCase ,			use_fast=__UpperCamelCase  )
          if tok_name == MBART_TINY:
               UpperCAmelCase__							: str 							=						SeqaSeqDataset(
                   __UpperCamelCase ,			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" ,			)
               UpperCAmelCase__							: Dict 							=						train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
               UpperCAmelCase__							: Optional[int] 							=						SeqaSeqDataset(
                   __UpperCamelCase ,			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 ,			)
               UpperCAmelCase__							: Optional[Any] 							=						train_dataset.dataset_kwargs
               assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
               assert len(__UpperCamelCase  ) == 1 if tok_name == BART_TINY else len(__UpperCamelCase  ) == 0





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


if __name__ == "__main__":
    A__      :	List[Any]          =    argparse.ArgumentParser()
    parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
    A__      :	Tuple          =    parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        A__      :	Tuple          =    os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
        A__      :	str          =    f"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )



