


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







def 				UpperCamelCase_			(    A__					:   Path							,    A__					:   list					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[Any]			=   """\n""".join(A__					)
  Path(A__					).open("""w"""					).writelines(A__					)







def 				UpperCamelCase_			(    A__					:   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       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



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

      lowerCAmelCase_			:      Optional[Any]			=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
      lowerCAmelCase_			:      Union[str, Any]			=   tmp_dir.joinpath("""train.source"""		).open().readlines()
      lowerCAmelCase_			:      str			=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
      pack_data_dir(lowerCamelCase    ,						lowerCamelCase    ,						1_28    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   {x.name for x in tmp_dir.iterdir()}
      lowerCAmelCase_			:      Dict			=   {x.name for x in save_dir.iterdir()}
      lowerCAmelCase_			:      Optional[Any]			=   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 __lowercase   (   self    :  Optional[int]		)   ->  Optional[Any]:
      if not FAIRSEQ_AVAILABLE:
        return
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Any			=   self._get_dataset(max_len=64		)
      lowerCAmelCase_			:      Tuple			=   64
      lowerCAmelCase_			:      Union[str, Any]			=   ds.make_dynamic_sampler(lowerCamelCase    ,						required_batch_size_multiple=lowerCamelCase		)
      lowerCAmelCase_			:      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
      lowerCAmelCase_			:      List[Any]			=   DataLoader(lowerCamelCase    ,						batch_sampler=lowerCamelCase    ,						collate_fn=ds.collate_fn    ,						num_workers=2		)
      lowerCAmelCase_			:      str			=   []
      lowerCAmelCase_			:      List[str]			=   []
      for batch in data_loader:
        lowerCAmelCase_			:      Optional[Any]			=   batch["""input_ids"""].shape
        lowerCAmelCase_			:      str			=   src_shape[0]
        assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
        lowerCAmelCase_			:      List[str]			=   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 __lowercase   (   self    :  Union[str, Any]		)   ->  Optional[int]:
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[int]			=   self._get_dataset(max_len=5_12		)
      lowerCAmelCase_			:      Tuple			=   2
      lowerCAmelCase_			:      str			=   ds.make_sortish_sampler(lowerCamelCase    ,						shuffle=lowerCamelCase		)

      lowerCAmelCase_			:      Any			=   DataLoader(lowerCamelCase    ,						batch_size=lowerCamelCase    ,						collate_fn=ds.collate_fn    ,						num_workers=2		)
      lowerCAmelCase_			:      Optional[int]			=   DataLoader(lowerCamelCase    ,						batch_size=lowerCamelCase    ,						collate_fn=ds.collate_fn    ,						num_workers=2    ,						sampler=lowerCamelCase		)

      lowerCAmelCase_			:      Union[str, Any]			=   tokenizer.pad_token_id

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

      lowerCAmelCase_			:      Tuple			=   AutoTokenizer.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   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 __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[Any]			=   self._get_dataset()
      lowerCAmelCase_			:      Tuple			=   set(DistributedSortishSampler(lowerCamelCase    ,						2_56    ,						num_replicas=2    ,						rank=0    ,						add_extra_examples=lowerCamelCase		)		)
      lowerCAmelCase_			:      Any			=   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 __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  int		)   ->  int:
      lowerCAmelCase_			:      Dict			=   AutoTokenizer.from_pretrained(lowerCamelCase    ,						use_fast=lowerCamelCase		)
      if tok_name == MBART_TINY:
        lowerCAmelCase_			:      Dict			=   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"""    ,						)
        lowerCAmelCase_			:      Dict			=   train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
        lowerCAmelCase_			:      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    ,						)
        lowerCAmelCase_			:      Optional[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 json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					BioGptTokenizer
    lowercase    =					False

    def __lowercase   (   self    :  List[Any]		)   ->  List[str]:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCAmelCase_			:      Optional[Any]			=   [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """w</w>""",
          """r</w>""",
          """t</w>""",
          """lo""",
          """low""",
          """er</w>""",
          """low</w>""",
          """lowest</w>""",
          """newer</w>""",
          """wider</w>""",
          """<unk>""",
      ]
      lowerCAmelCase_			:      int			=   dict(zip(lowerCamelCase    ,						range(len(lowerCamelCase		)		)		)		)
      lowerCAmelCase_			:      int			=   ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

      lowerCAmelCase_			:      Optional[int]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]		)
      lowerCAmelCase_			:      Union[str, Any]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]		)
      with open(self.vocab_file    ,						"""w"""		) as fp:
        fp.write(json.dumps(lowerCamelCase		)		)
      with open(self.merges_file    ,						"""w"""		) as fp:
        fp.write("""\n""".join(lowerCamelCase		)		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  List[str]:
      lowerCAmelCase_			:      Dict			=   """lower newer"""
      lowerCAmelCase_			:      str			=   """lower newer"""
      return input_text, output_text

    def __lowercase   (   self    :  Optional[int]		)   ->  str:
      lowerCAmelCase_			:      Any			=   BioGptTokenizer(self.vocab_file    ,						self.merges_file		)

      lowerCAmelCase_			:      int			=   """lower"""
      lowerCAmelCase_			:      str			=   ["""low""", """er</w>"""]
      lowerCAmelCase_			:      Any			=   tokenizer.tokenize(lowerCamelCase		)
      self.assertListEqual(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   tokens + ["""<unk>"""]
      lowerCAmelCase_			:      Optional[Any]			=   [14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)    ,						lowerCamelCase		)





    @slow
    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Dict			=   BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)

      lowerCAmelCase_			:      int			=   tokenizer.encode("""sequence builders"""    ,						add_special_tokens=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=lowerCamelCase		)

      lowerCAmelCase_			:      str			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase    ,						lowerCamelCase		)

      self.assertTrue(encoded_sentence == [2] + text		)
      self.assertTrue(encoded_pair == [2] + text + [2] + text_a		)



