






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




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List[Any]:
       _UpperCAmelCase							=    """\n""".join(__snake_case )
       Path(__snake_case ).open("""w""" ).writelines(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(__snake_case   ,      f"""{split}.source""" )   ,      __snake_case )
              _dump_articles(os.path.join(__snake_case   ,      f"""{split}.target""" )   ,      __snake_case )
       return tmp_dir





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]       ,				)
  @slow
  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Dict							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    AutoTokenizer.from_pretrained(lowerCamelCase							)
         _UpperCAmelCase							=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
         _UpperCAmelCase							=    max(len(tokenizer.encode(lowerCamelCase							)							) for a in ARTICLES							)
         _UpperCAmelCase							=    max(len(tokenizer.encode(lowerCamelCase							)							) 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(
             lowerCamelCase       ,				data_dir=lowerCamelCase       ,				type_path="""train"""       ,				max_source_length=lowerCamelCase       ,				max_target_length=lowerCamelCase       ,				src_lang=lowerCamelCase       ,				tgt_lang=lowerCamelCase       ,				)
         _UpperCAmelCase							=    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
                _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[Any]       ,				lowerCamelCase		:			Dict							)						->	Union[str, Any]:


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


         """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(lowerCamelCase       ,				lowerCamelCase       ,				128       ,				lowerCamelCase							)
         _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(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  lowerCamelCase       (      self		:			List[Any]							)						->	str:


         """simple docstring"""
         if not FAIRSEQ_AVAILABLE:
                return
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    self._get_dataset(max_len=64							)
         _UpperCAmelCase							=    64
         _UpperCAmelCase							=    ds.make_dynamic_sampler(lowerCamelCase       ,				required_batch_size_multiple=lowerCamelCase							)
         _UpperCAmelCase							=    [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
         _UpperCAmelCase							=    DataLoader(lowerCamelCase       ,				batch_sampler=lowerCamelCase       ,				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(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  lowerCamelCase       (      self		:			int							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    self._get_dataset(max_len=512							)
         _UpperCAmelCase							=    2
         _UpperCAmelCase							=    ds.make_sortish_sampler(lowerCamelCase       ,				shuffle=lowerCamelCase							)

         _UpperCAmelCase							=    DataLoader(lowerCamelCase       ,				batch_size=lowerCamelCase       ,				collate_fn=ds.collate_fn       ,				num_workers=2							)
         _UpperCAmelCase							=    DataLoader(lowerCamelCase       ,				batch_size=lowerCamelCase       ,				collate_fn=ds.collate_fn       ,				num_workers=2       ,				sampler=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.pad_token_id

         def count_pad_tokens(lowerCamelCase		:			Tuple       ,				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  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Dict=1000       ,				lowerCamelCase		:			Optional[Any]=128							)						->	str:


         """simple docstring"""
         if os.getenv("""USE_REAL_DATA"""       ,				lowerCamelCase							):
                _UpperCAmelCase							=    """examples/seq2seq/wmt_en_ro"""
                _UpperCAmelCase							=    max_len * 2 * 64
                if not Path(lowerCamelCase							).joinpath("""train.len"""							).exists():
                       save_len_file(lowerCamelCase       ,				lowerCamelCase							)
         else:
                _UpperCAmelCase							=    """examples/seq2seq/test_data/wmt_en_ro"""
                _UpperCAmelCase							=    max_len * 4
                save_len_file(lowerCamelCase       ,				lowerCamelCase							)

         _UpperCAmelCase							=    AutoTokenizer.from_pretrained(lowerCamelCase							)
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    self._get_dataset()
         _UpperCAmelCase							=    set(DistributedSortishSampler(lowerCamelCase       ,				256       ,				num_replicas=2       ,				rank=0       ,				add_extra_examples=lowerCamelCase							)							)
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[Any]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    AutoTokenizer.from_pretrained(lowerCamelCase       ,				use_fast=lowerCamelCase							)
         if tok_name == MBART_TINY:
                _UpperCAmelCase							=    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"""       ,				)
                _UpperCAmelCase							=    train_dataset.dataset_kwargs
                assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
                _UpperCAmelCase							=    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       ,				)
                _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(lowerCamelCase							) == 1 if tok_name == BART_TINY else len(lowerCamelCase							) == 0







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a:     Any   					=					logging.get_logger(__name__)


__a:     Dict   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     int   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     str   					=					{'''facebook/blenderbot_small-90M''': 512}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    set()
       _UpperCAmelCase							=    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCAmelCase							=    char

       _UpperCAmelCase							=    set(__snake_case )
       return pairs





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  ['''input_ids''', '''attention_mask''']


  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[str]="__start__"       ,				lowerCamelCase		:			List[Any]="__end__"       ,				lowerCamelCase		:			Any="__unk__"       ,				lowerCamelCase		:			Optional[Any]="__null__"       ,				**lowerCamelCase		:			Optional[Any]       ,				)						->	Tuple:


         """simple docstring"""
         super().__init__(unk_token=lowerCamelCase       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				pad_token=lowerCamelCase       ,				**lowerCamelCase							)

         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as vocab_handle:
                _UpperCAmelCase							=    json.load(lowerCamelCase							)
         _UpperCAmelCase							=    {v: k for k, v in self.encoder.items()}
         with open(lowerCamelCase       ,				encoding="""utf-8"""							) as merges_handle:
                _UpperCAmelCase							=    merges_handle.read().split("""\n"""							)[1:-1]
         _UpperCAmelCase							=    [tuple(merge.split()							) for merge in merges]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    {}


  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         return len(self.encoder							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Dict:


         """simple docstring"""
         return dict(self.encoder       ,				**self.added_tokens_encoder							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""
         if token in self.cache:
                return self.cache[token]
         _UpperCAmelCase							=    re.sub("""([.,!?()])"""       ,				r""" \1"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub("""(')"""       ,				r""" \1 """       ,				lowerCamelCase							)
         _UpperCAmelCase							=    re.sub(r"""\s{2,}"""       ,				""" """       ,				lowerCamelCase							)
         if "\n" in token:
                _UpperCAmelCase							=    token.replace("""\n"""       ,				""" __newln__"""							)

         _UpperCAmelCase							=    token.split(""" """							)
         _UpperCAmelCase							=    []
         for token in tokens:
                if not len(lowerCamelCase							):
                       continue

                _UpperCAmelCase							=    token.lower()
                _UpperCAmelCase							=    tuple(lowerCamelCase							)
                _UpperCAmelCase							=    tuple(list(word[:-1]							) + [word[-1] + """</w>"""]							)
                _UpperCAmelCase							=    get_pairs(lowerCamelCase							)

                if not pairs:
                       words.append(lowerCamelCase							)
                       continue

                while True:
                       _UpperCAmelCase							=    min(lowerCamelCase       ,				key=lambda lowerCamelCase							: self.bpe_ranks.get(lowerCamelCase       ,				float("""inf"""							)							)							)
                       if bigram not in self.bpe_ranks:
                              break
                       _UpperCAmelCase	,			_UpperCAmelCase							=    bigram
                       _UpperCAmelCase							=    []
                       _UpperCAmelCase							=    0

                       while i < len(lowerCamelCase							):
                              try:
                                     _UpperCAmelCase							=    word.index(lowerCamelCase       ,				lowerCamelCase							)
                                     new_word.extend(word[i:j]							)
                                     _UpperCAmelCase							=    j
                              except ValueError:
                                     new_word.extend(word[i:]							)
                                     break

                              if word[i] == first and i < len(lowerCamelCase							) - 1 and word[i + 1] == second:
                                     new_word.append(first + second							)
                                     i += 2
                              else:
                                     new_word.append(word[i]							)
                                     i += 1
                       _UpperCAmelCase							=    tuple(lowerCamelCase							)
                       _UpperCAmelCase							=    new_word
                       if len(lowerCamelCase							) == 1:
                              break
                       else:
                              _UpperCAmelCase							=    get_pairs(lowerCamelCase							)
                _UpperCAmelCase							=    """@@ """.join(lowerCamelCase							)
                _UpperCAmelCase							=    word[:-4]

                _UpperCAmelCase							=    word
                words.append(lowerCamelCase							)
         return " ".join(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			str							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    []

         _UpperCAmelCase							=    re.findall(r"""\S+\n?"""       ,				lowerCamelCase							)

         for token in words:
                split_tokens.extend(list(self.bpe(lowerCamelCase							).split(""" """							)							)							)
         return split_tokens


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			str							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    token.lower()
         return self.encoder.get(lowerCamelCase       ,				self.encoder.get(self.unk_token							)							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int							)						->	str:


         """simple docstring"""
         return self.decoder.get(lowerCamelCase       ,				self.unk_token							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    """ """.join(lowerCamelCase							).replace("""@@ """       ,				""""""							).strip()
         return out_string





  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[str] = None							)						->	Tuple[str]:


         """simple docstring"""
         if not os.path.isdir(lowerCamelCase							):
                logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                return
         _UpperCAmelCase							=    os.path.join(
             lowerCamelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    os.path.join(
             lowerCamelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

         with open(lowerCamelCase       ,				"""w"""       ,				encoding="""utf-8"""							) as f:
                f.write(json.dumps(self.encoder       ,				indent=2       ,				sort_keys=lowerCamelCase       ,				ensure_ascii=lowerCamelCase							) + """\n"""							)

         _UpperCAmelCase							=    0
         with open(lowerCamelCase       ,				"""w"""       ,				encoding="""utf-8"""							) as writer:
                writer.write("""#version: 0.2\n"""							)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,				key=lambda lowerCamelCase							: kv[1]							):
                       if index != token_index:
                              logger.warning(
                                  f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                  """ Please check that the tokenizer is not corrupted!"""							)
                              _UpperCAmelCase							=    token_index
                       writer.write(""" """.join(lowerCamelCase							) + """\n"""							)
                       index += 1

         return vocab_file, merge_file