




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


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


def    A						(__A   :    Path   ,				__A   :    list       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							'''\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 __snake_case			(			a		):







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]					,						)
   @slow
   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_snake_case)
        UpperCAmelCase_          =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        UpperCAmelCase_          =							max(len(tokenizer.encode(_snake_case)) for a in ARTICLES)
        UpperCAmelCase_          =							max(len(tokenizer.encode(_snake_case)) for a in SUMMARIES)
        UpperCAmelCase_          =							4
        UpperCAmelCase_          =							8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        UpperCAmelCase_     ,       UpperCAmelCase_          =							'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        UpperCAmelCase_          =							SeqaSeqDataset(
            _snake_case					,						data_dir=_snake_case					,						type_path='''train'''					,						max_source_length=_snake_case					,						max_target_length=_snake_case					,						src_lang=_snake_case					,						tgt_lang=_snake_case					,						)
        UpperCAmelCase_          =							DataLoader(_snake_case					,						batch_size=2					,						collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
             assert isinstance(_snake_case					,						_snake_case)
             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_          =							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		:						Optional[int]					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_snake_case)
        UpperCAmelCase_          =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        UpperCAmelCase_          =							max(len(tokenizer.encode(_snake_case)) for a in ARTICLES)
        UpperCAmelCase_          =							max(len(tokenizer.encode(_snake_case)) for a in SUMMARIES)
        UpperCAmelCase_          =							4
        UpperCAmelCase_          =							LegacySeqaSeqDataset(
            _snake_case					,						data_dir=_snake_case					,						type_path='''train'''					,						max_source_length=20					,						max_target_length=_snake_case					,						)
        UpperCAmelCase_          =							DataLoader(_snake_case					,						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		:						Optional[int]):

        """simple docstring"""


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

        UpperCAmelCase_          =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        UpperCAmelCase_          =							tmp_dir.joinpath('''train.source''').open().readlines()
        UpperCAmelCase_          =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(_snake_case					,						_snake_case					,						128					,						_snake_case)
        UpperCAmelCase_          =							{x.name for x in tmp_dir.iterdir()}
        UpperCAmelCase_          =							{x.name for x in save_dir.iterdir()}
        UpperCAmelCase_          =							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(_snake_case) < len(_snake_case)
        assert len(_snake_case) == 1
        assert len(packed_examples[0]) == sum(len(_snake_case) for x in orig_examples)
        assert orig_paths == new_paths







   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,						reason='''This test requires fairseq''')
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        if not FAIRSEQ_AVAILABLE:
             return
        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self._get_dataset(max_len=64)
        UpperCAmelCase_          =							64
        UpperCAmelCase_          =							ds.make_dynamic_sampler(_snake_case					,						required_batch_size_multiple=_snake_case)
        UpperCAmelCase_          =							[len(_snake_case) for x in batch_sampler]
        assert len(set(_snake_case)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(_snake_case) == len(_snake_case)  # no dropped or added examples
        UpperCAmelCase_          =							DataLoader(_snake_case					,						batch_sampler=_snake_case					,						collate_fn=ds.collate_fn					,						num_workers=2)
        UpperCAmelCase_          =							[]
        UpperCAmelCase_          =							[]
        for batch in data_loader:
             UpperCAmelCase_          =							batch['''input_ids'''].shape
             UpperCAmelCase_          =							src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             UpperCAmelCase_          =							np.product(batch['''input_ids'''].shape)
             num_src_per_batch.append(_snake_case)
             if num_src_tokens > (max_tokens * 1.1):
                  failures.append(_snake_case)
        assert num_src_per_batch[0] == max(_snake_case)
        if failures:
             raise AssertionError(F"""too many tokens in {len(_snake_case)} batches""")







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self._get_dataset(max_len=512)
        UpperCAmelCase_          =							2
        UpperCAmelCase_          =							ds.make_sortish_sampler(_snake_case					,						shuffle=_snake_case)

        UpperCAmelCase_          =							DataLoader(_snake_case					,						batch_size=_snake_case					,						collate_fn=ds.collate_fn					,						num_workers=2)
        UpperCAmelCase_          =							DataLoader(_snake_case					,						batch_size=_snake_case					,						collate_fn=ds.collate_fn					,						num_workers=2					,						sampler=_snake_case)

        UpperCAmelCase_          =							tokenizer.pad_token_id

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

        assert sum(count_pad_tokens(_snake_case					,						k='''labels''')) < sum(count_pad_tokens(_snake_case					,						k='''labels'''))
        assert sum(count_pad_tokens(_snake_case)) < sum(count_pad_tokens(_snake_case))
        assert len(_snake_case) == len(_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Dict=1000					,						_snake_case		:						str=128):

        """simple docstring"""


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

        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_snake_case)
        UpperCAmelCase_          =							SeqaSeqDataset(
            _snake_case					,						data_dir=_snake_case					,						type_path='''train'''					,						max_source_length=_snake_case					,						max_target_length=_snake_case					,						n_obs=_snake_case					,						)
        return ds, max_tokens, tokenizer







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self._get_dataset()
        UpperCAmelCase_          =							set(DistributedSortishSampler(_snake_case					,						256					,						num_replicas=2					,						rank=0					,						add_extra_examples=_snake_case))
        UpperCAmelCase_          =							set(DistributedSortishSampler(_snake_case					,						256					,						num_replicas=2					,						rank=1					,						add_extra_examples=_snake_case))
        assert idsa.intersection(_snake_case) == set()







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]					,						)
   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_snake_case					,						use_fast=_snake_case)
        if tok_name == MBART_TINY:
             UpperCAmelCase_          =							SeqaSeqDataset(
                 _snake_case					,						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_          =							train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
             UpperCAmelCase_          =							SeqaSeqDataset(
                 _snake_case					,						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_          =							train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(_snake_case) == 1 if tok_name == BART_TINY else len(_snake_case) == 0












import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
snake_case_ : List[Any]    		=       data_utils.TransfoXLTokenizer
snake_case_ : int    		=       data_utils.TransfoXLCorpus
snake_case_ : List[Any]    		=       data_utils
snake_case_ : int    		=       data_utils


def    A						(__A   :    Dict   ,				__A   :    List[Any]   ,				__A   :    Union[str, Any]   ,				__A   :    Tuple       )   ->      Union[str, Any]:







     """simple docstring"""


     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__A   ,				'''rb'''       ) as fp:
               UpperCAmelCase_          =							pickle.load(__A   ,				encoding='''latin1'''       )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""       )
          UpperCAmelCase_          =							corpus.vocab.__dict__
          torch.save(__A   ,				__A       )

          UpperCAmelCase_          =							corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''   ,				__A       )
          UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F"""Save dataset to {pytorch_dataset_dump_path}"""       )
          torch.save(__A   ,				__A       )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          UpperCAmelCase_          =							os.path.abspath(__A       )
          UpperCAmelCase_          =							os.path.abspath(__A       )

          print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""       )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               UpperCAmelCase_          =							TransfoXLConfig()
          else:
               UpperCAmelCase_          =							TransfoXLConfig.from_json_file(__A       )
          print(F"""Building PyTorch model from configuration: {config}"""       )
          UpperCAmelCase_          =							TransfoXLLMHeadModel(__A       )

          UpperCAmelCase_          =							load_tf_weights_in_transfo_xl(__A   ,				__A   ,				__A       )
          # Save pytorch-model
          UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
          UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
          print(F"""Save PyTorch model to {os.path.abspath(__A       )}"""       )
          torch.save(model.state_dict()   ,				__A       )
          print(F"""Save configuration file to {os.path.abspath(__A       )}"""       )
          with open(__A   ,				'''w'''   ,				encoding='''utf-8'''       ) as f:
               f.write(config.to_json_string()       )


if __name__ == "__main__":
    snake_case_ : List[str]    		=       argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--tf_checkpoint_path",
        default="",
        type=str,
        help="An optional path to a TensorFlow checkpoint path to be converted.",
    )
    parser.add_argument(
        "--transfo_xl_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained BERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--transfo_xl_dataset_file",
        default="",
        type=str,
        help="An optional dataset file to be converted in a vocabulary.",
    )
    snake_case_ : int    		=       parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )






