

import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowercase_             =     pytest.mark.integration


lowercase_             =     {'comet'}
lowercase_             =     importlib.util.find_spec('fairseq') is not None

lowercase_             =     {'code_eval'}
lowercase_             =     os.name == 'nt'

lowercase_             =     {'bertscore', 'frugalscore', 'perplexity'}
lowercase_             =     importlib.util.find_spec('transformers') is not None
def   a   (    A__			:     Dict  )					->   Any:





      """simple docstring"""



      @wraps(A__  )
      def wrapper(self			:     Tuple   ,     A__			:     List[Any]  ):
            if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
                  self.skipTest('"test requires Fairseq"'  )
            else:
                  test_case(self   ,     A__  )

      return wrapper
def   a   (    A__			:     List[str]  )					->   str:





      """simple docstring"""



      @wraps(A__  )
      def wrapper(self			:     str   ,     A__			:     Dict  ):
            if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                  self.skipTest('"test requires transformers"'  )
            else:
                  test_case(self   ,     A__  )

      return wrapper
def   a   (    A__			:     Any  )					->   int:





      """simple docstring"""



      @wraps(A__  )
      def wrapper(self			:     str   ,     A__			:     Optional[Any]  ):
            if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                  self.skipTest('"test not supported on Windows"'  )
            else:
                  test_case(self   ,     A__  )

      return wrapper
def   a   (    )					->   Any:





      """simple docstring"""



      _lowercase			 =[metric_dir.split(os.sep  )[-2] for metric_dir in glob.glob('./metrics/*/'  )]
      return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()							)
@for_all_test_methods(
    SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE							)
@local
class 				__lowerCAmelCase  (      parameterized.TestCase							):
  _a		    =						{}
  _a		    =						None




  @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'      )
  @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'      )
  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase			 ='[...]'
        _lowercase			 =importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('metrics'							,     lowerCAmelCase      )      ).module_path      )
        _lowercase			 =datasets.load.import_main_class(metric_module.__name__							,     dataset=lowerCAmelCase      )
        # check parameters
        _lowercase			 =inspect.signature(metric._compute      ).parameters
        self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()      )      )  # no **kwargs
        # run doctest
        with self.patch_intensive_calls(lowerCAmelCase							,     metric_module.__name__      ):
              with self.use_local_metrics():
                    try:
                          _lowercase			 =doctest.testmod(lowerCAmelCase							,     verbose=lowerCAmelCase							,     raise_on_error=lowerCAmelCase      )
                    except doctest.UnexpectedException as e:
                          raise e.exc_info[1]  # raise the exception that doctest caught
        self.assertEqual(results.failed							,     0      )
        self.assertGreater(results.attempted							,     1      )




  @slow
  def 						A__  (       self							,     lowerCAmelCase      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 ='[...]'
        _lowercase			 =importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('metrics'							,     lowerCAmelCase      )      ).module_path      )
        # run doctest
        with self.use_local_metrics():
              _lowercase			 =doctest.testmod(lowerCAmelCase							,     verbose=lowerCAmelCase							,     raise_on_error=lowerCAmelCase      )
        self.assertEqual(results.failed							,     0      )
        self.assertGreater(results.attempted							,     1      )




  @contextmanager
  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        if metric_name in self.INTENSIVE_CALLS_PATCHER:
              with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase      ):
                    yield
        else:
              yield




  @contextmanager
  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        def load_local_metric(lowerCAmelCase							,     *lowerCAmelCase							,     **lowerCAmelCase      ):
              return load_metric(os.path.join('metrics'							,     lowerCAmelCase      )							,     *lowerCAmelCase							,     **lowerCAmelCase      )

        with patch('datasets.load_metric'      ) as mock_load_metric:
              _lowercase			 =load_local_metric
              yield




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase      )						->				Any:
        '''simple docstring'''






        def wrapper(lowerCAmelCase      ):
              _lowercase			 =contextmanager(lowerCAmelCase      )
              _lowercase			 =patcher
              return patcher

        return wrapper
@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def   a   (    A__			:     Optional[Any]  )					->   Tuple:





      """simple docstring"""



      import tensorflow.compat.va as tf
      from bleurt.score import Predictor

      tf.flags.DEFINE_string('sv'   ,     ''   ,     ''  )  # handle pytest cli flags





      class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




        def 						A__  (       self							,     lowerCAmelCase      )						->				List[str]:
              '''simple docstring'''






              assert len(input_dict['input_ids']      ) == 2
              return np.array([1.03, 1.04]      )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
      with patch('bleurt.score._create_predictor'  ) as mock_create_predictor:
            _lowercase			 =MockedPredictor()
            yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def   a   (    A__			:     Optional[int]  )					->   int:





      """simple docstring"""



      import torch

      def bert_cos_score_idf(A__			:     Optional[int]   ,     A__			:     List[Any]   ,     *A__			:     List[Any]   ,     **A__			:     List[Any]  ):
            return torch.tensor([[1.0, 1.0, 1.0]] * len(A__  )  )

      # mock get_model which is supposed to do download a bert model
      # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
      with patch('bert_score.scorer.get_model'  ), patch(
          'bert_score.scorer.bert_cos_score_idf'  ) as mock_bert_cos_score_idf:
            _lowercase			 =bert_cos_score_idf
            yield
@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def   a   (    A__			:     Optional[int]  )					->   str:





      """simple docstring"""



      def load_from_checkpoint(A__			:     Optional[Any]  ):





            class 				__lowerCAmelCase  :




              def 						A__  (       self							,     lowerCAmelCase							,     *lowerCAmelCase							,     **lowerCAmelCase      )						->				Dict:
                    '''simple docstring'''






                    assert len(lowerCAmelCase      ) == 2
                    _lowercase			 =[0.19, 0.92]
                    return scores, sum(lowerCAmelCase      ) / len(lowerCAmelCase      )
            return Model()

      # mock load_from_checkpoint which is supposed to do download a bert model
      # mock load_from_checkpoint which is supposed to do download a bert model
      with patch('comet.download_model'  ) as mock_download_model:
            _lowercase			 =None
            with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
                  _lowercase			 =load_from_checkpoint
                  yield







def   a   (    )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =load_metric(os.path.join('metrics'   ,     'seqeval'  )  )
      _lowercase			 ='ERROR'
      _lowercase			 =F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
      with pytest.raises(A__   ,     match=re.escape(A__  )  ):
            metric.compute(predictions=[]   ,     references=[]   ,     scheme=A__  )





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


